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Constancy & plasticity in biology – the central role of hierarchical causal models

Ute Deichmann of the Jacques Loeb Centre for the History and Philosophy of the Life Sciences at Ben-Gurion University, explores the role hierarchical causal models have on constancy and plasticity in biology

In natural history, notions of plasticity and change long antedated those of constancy and robustness. With his theory of the constancy of species, Linnaeus in the 18th century put an end to widespread notions of plasticity and transformation of species and thus laid the basis for a scientific understanding of species change.

Theories of the evolution of species as well as the germ theory of disease became scientifically meaningful only after the stability of organismic species sometime over long periods of time had been generally accepted. The idea of constancy became prevalent in many fields of biology in the late 19th century, especially in genetics, development and evolution, when constancy became inseparably linked with three basic biological principles:

• The structural and organisational hierarchy in organisms,
• Genetic causality of fundamental life processes,
• Biological and genetic specificity or genetic information .

These three principles are highlighted in particular in developmental biology, where the notion of constancy is prevalent, and where, in the words of Greg Gibson, “despite the fact that it takes 20.000 genes to make a complex multicellular organism and these have to work in very diverse environments, development works and leads to a constant outcome” (Gibson 2002; see Fig. 1).

Hierarchical gene regulatory networks in development

The constancy of animal development has been explained by hierarchical gene regulatory networks (GRNs) in which specific regulatory proteins, in particular transcription factors, play a major role (e.g. Davidson 2006). GRNs consist of regulatory genes and signaling pathways that execute a cascade of molecular mechanisms to transform an egg cell into a complex organism (Fig. 2). Davidson’s model has also implications for the theory of evolution: The most central genetic circuits of a GRN controlling development are so constrained that their variations are rare. This hypothesis explains the remarkable degree of constancy in evolution, i.e. the phenotypic stability of animal body plans that in some cases has persisted since around 500 million years ago.

Plasticity and unpredictability

Development is not only characterised by constancy and predictability, but there is also plasticity and unpredictability. The chemistry of life is characterised by molecular fluctuations and stochastic events in cells that seem to contradict deterministic explanations of development. The examination of the complicated hierarchy of buffering in cells and organisms to maintain constancy, e.g. through GRN and other mechanisms, is a fascinating challenge for current and future research.

The phenomenon of phenotypic plasticity, i.e. the generation of alternative phenotypes from the same genome, shows that not every single developmental trait is fully determined by particular genes. The limited effect of the environment on phenotypes was proposed already in the early 20th century by the Danish botanist Wilhelm Johannsen who equated the genotype with the notion of reaction norm, which referred to the range of potential – reversible – phenotypic variations in different environments. An intriguing example is the transition between solitarious and gregarious locusts elicited by mechanosensory input (Fig. 3).

Throughout the history of modern biology, the ideas of genetic causality and biological specificity have been rejected or marginalised in various fields. Around 100 years ago, the movement of biocolloidy, focusing on unspecific physical mechanisms replaced the search for relations of specific structures and functions by theories related to surface actions, electric charges and adsorption. All biochemically relevant substances of the cell such as proteins, enzymes and nucleic acids were regarded as biologically active colloidal aggregates of undetermined composition. The success of macromolecular chemistry and, subsequently, molecular biology, brought forward approaches that were able to causally explain the phenomena of biological specificity, rendering biocolloidy obsolete.

Questioning genetic causality

More recently, some approaches of epigenetics try to call into question genetic causality by claiming that small, unspecific molecules such as methyl groups are able to regulate gene expression. Social scientists and some biologists believe that these epigenetic marks are environmentally caused and can be inherited over many generations, thus marginalising the causal role of the genome for development. However, these approaches ignore established scientific facts in genetics and cell biology, according to which gene regulation is brought about by specific regulatory proteins. Because the enzymes that transfer epigenetic marks to DNA or histones lack DNA-sequence specificity, they require sequence specific factors such as transcription factors to guide their activity on the genome. Thus, the factors that are involved in gene regulation are hierarchically organised.

Likewise, current attempts to explain animal development by non-hierarchical, multilevel, multifactorial mechanisms, deny the relevance or even existence of causal relationships between specific regulatory factors. They reject the explanatory power of hierarchical GRN on the grounds that transcription factors contain intrinsically disordered (ID) protein regions that render them unsuitable for regulatory purposes. However, it has been shown that these ID-regions occur predominantly in domains that are used e.g. for recruiting co-factors, and less in the DNA-binding domain. The fact that ID regions contribute to the instability of transcription factors is an important pre-requisite for their suitability for regulatory functions.

Non-hierarchical, multilevel, multifactorial network models may explain phenomena of plasticity. But they do not convincingly explain how:

• Development can result in a functioning organisation,
• The development of individuals of a species always results in the same body plan, largely independently of the environment,
• How species can remain constant in different environments over long periods of time.

Historians and philosophers of science cannot predict scientific developments, as was formulated by biochemist and Nobel Laureate Otto Meyerhof some 90 years ago: “But one will only expect from scientific philosophy the consistent order of the system of scientific theories and no prediction of their contents.” However, historians and philosophers of science not only highlight the intellectual history of currently important concepts. They can also shed light on errors of reasoning and scientific dead ends, often due to neglect of basic biological principles that have been developed and revised since the late 19th century.

References:

Davidson, E.H. (2006). The Regulatory Genome. Gene Regulatory Networks in Development and Evolution. Burlington: Academic Press.

Gibson, G. (2002). Developmental Evolution: Getting Robust over Robustness. Current Biology 12, 347-349.

Deichmann, U. (2007). “Molecular” versus “Colloidal”: Controversies in Biology and Biochemistry, 1900–1940, Bulletin for the History of Chemistry 32, 105-118.

Deichmann, U. (2017). Hierarchy, Determinism, and Specificity in Theories of Development and Evolution. History and Philosophy of the Life Sciences 39 (4), 33. doi: 10.1007/s40656-017-0160-3

Deichmann, U. (2020). The Social Construction of the Social Epigenome and the Larger Biological Context. Epigenetics & Chromatin 13, 37. https://doi.org/10.1186/s13072-020-00360-w

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• Review Article
• Published: 01 March 2003

The modern molecular clock

• Lindell Bromham 1 &
• David Penny 2  

Nature Reviews Genetics volume  4 ,  pages 216–224 ( 2003 ) Cite this article

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Rates of molecular evolution can be remarkably constant over time, producing a molecular clock.

The constancy of rates was explained by the neutral theory by assuming that most changes to DNA or protein sequences are neutral — that is, driven by drift not selection.

The neutral theory has been refined to allow for the effect of population size on the chance of mutations of small selective effect being fixed in a population (the nearly neutral theory).

The molecular clock is a 'sloppy' clock: theory predicts that the rate of molecular evolution will be influenced by mutation rate, patterns of selection and population size.

Stochastic fluctuations in substitution rate over time in lineages (residual effects) make molecular date estimates imprecise.

Variation in rate between lineages can cause substantial bias in molecular date estimates.

Attempts to use molecular clocks to date evolutionary divergences must account for these sources of imprecision and bias, and variation in rates must be expressed in confidence intervals around date estimates.

The discovery of the molecular clock — a relatively constant rate of molecular evolution — provided an insight into the mechanisms of molecular evolution, and created one of the most useful new tools in biology. The unexpected constancy of rate was explained by assuming that most changes to genes are effectively neutral. Theory predicts several sources of variation in the rate of molecular evolution. However, even an approximate clock allows time estimates of events in evolutionary history, which provides a method for testing a wide range of biological hypotheses ranging from the origins of the animal kingdom to the emergence of new viral epidemics.

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We thank A. Rambaut and A. Eyre-Walker for helpful comments.

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The maximum-likelihood method takes a model of sequence evolution (essentially a set of parameters that describe the pattern of substitutions) and searches for the combination of parameter values that gives the greatest probability of obtaining the observed sequences.

A method that selects the tree that has the greatest posterior probability (the probability that the tree is correct), under a specific model of substitution.

A discrete frequency distribution of the number of independent events per time interval, for which the mean value is equal to the variance.

Evolution at, or above, the level of species; the patterns and processes of diversification and extinction of species over evolutionary time.

The process of evolution in populations: changing allele frequencies over generations, due to selection or drift.

A group of linked regulatory genes that are involved in patterning the animal body axis during development.

The reproductive strategy of an organism.

A 'cold-blooded' organism, such as a reptile, for which body temperature is dependent on the environment.

A 'warm-blooded' organism, such as a mammal or bird, for which body temperature is maintained independently of the environment.

(N e ). The equivalent number of breeding adults in a population after adjusting for complicating factors, such as reproductive dynamics. It is usually less that the actual number of living or reproducing individuals (the census population size N).

An increase in allele frequency to the point at which all individuals in a population are homozygous.

A life-history strategy in which only a subset of members of a group produce their own offspring, and others act as non-reproductive helpers, as in honeybees or naked molerats.

The random fluctuation that occurs in allele frequencies as genes are transmitted from one generation to the next. This is because allele frequencies in any sample of gametes that perpetuate the population might not represent those of the adults in the previous generation.

A measure of the variation around the central class of a distribution (the average squared deviation of the observations from their mean value).

A test for variation in the rate of molecular evolution between lineages, which compares the distance between each of a pair of taxa and an outgroup to determine the relative amount of change in each lineage since their last common ancestor.

A test for variation in the rate of molecular evolution between lineages, based on the expectation that under a uniform rate of substitution, the number of sites at which the amino-acid or nucleotide state is shared by the outgroup and only one of the two ingroups should be equal for both ingroups.

A method for hypothesis testing. The maximum of the likelihood that the data fit a full model of the data (in this case, multiple substitution rates) is compared with the maximum of the likelihood that the data fit a restricted model (a single substitution rate) and the likelihood ratio (LR) test statistic is computed. If the LR is significant, the full model provides a better fit to the data than does the restricted model.

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The modern molecular clock

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• 1 Centre for the Study of Evolution, School of Biological Sciences, University of Sussex, Falmer, Brighton BN1 9QG, UK. [email protected]
• PMID: 12610526
• DOI: 10.1038/nrg1020

The discovery of the molecular clock--a relatively constant rate of molecular evolution--provided an insight into the mechanisms of molecular evolution, and created one of the most useful new tools in biology. The unexpected constancy of rate was explained by assuming that most changes to genes are effectively neutral. Theory predicts several sources of variation in the rate of molecular evolution. However, even an approximate clock allows time estimates of events in evolutionary history, which provides a method for testing a wide range of biological hypotheses ranging from the origins of the animal kingdom to the emergence of new viral epidemics.

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4.14: Experiments and Hypotheses

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Now we’ll focus on the methods of scientific inquiry. Science often involves making observations and developing hypotheses. Experiments and further observations are often used to test the hypotheses.

A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes the result of the change. Scientific inquiry often involves doing experiments, though not always. For example, a scientist studying the mating behaviors of ladybugs might begin with detailed observations of ladybugs mating in their natural habitats. While this research may not be experimental, it is scientific: it involves careful and verifiable observation of the natural world. The same scientist might then treat some of the ladybugs with a hormone hypothesized to trigger mating and observe whether these ladybugs mated sooner or more often than untreated ones. This would qualify as an experiment because the scientist is now making a change in the system and observing the effects.

Forming a Hypothesis

When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.

For example, Michael observes that maple trees lose their leaves in the fall. He might then propose a possible explanation for this observation: “cold weather causes maple trees to lose their leaves in the fall.” This statement is testable. He could grow maple trees in a warm enclosed environment such as a greenhouse and see if their leaves still dropped in the fall. The hypothesis is also falsifiable. If the leaves still dropped in the warm environment, then clearly temperature was not the main factor in causing maple leaves to drop in autumn.

In the Try It below, you can practice recognizing scientific hypotheses. As you consider each statement, try to think as a scientist would: can I test this hypothesis with observations or experiments? Is the statement falsifiable? If the answer to either of these questions is “no,” the statement is not a valid scientific hypothesis.

Practice Questions

Determine whether each following statement is a scientific hypothesis.

• No. This statement is not testable or falsifiable.
• No. This statement is not testable.
• No. This statement is not falsifiable.
• Yes. This statement is testable and falsifiable.

[reveal-answer q=”429550″] Show Answers [/reveal-answer] [hidden-answer a=”429550″]

• d: Yes. This statement is testable and falsifiable. This could be tested with a number of different kinds of observations and experiments, and it is possible to gather evidence that indicates that air pollution is not linked with asthma.
• a: No. This statement is not testable or falsifiable. “Bad thoughts and behaviors” are excessively vague and subjective variables that would be impossible to measure or agree upon in a reliable way. The statement might be “falsifiable” if you came up with a counterexample: a “wicked” place that was not punished by a natural disaster. But some would question whether the people in that place were really wicked, and others would continue to predict that a natural disaster was bound to strike that place at some point. There is no reason to suspect that people’s immoral behavior affects the weather unless you bring up the intervention of a supernatural being, making this idea even harder to test.

[/hidden-answer]

Testing a Vaccine

Let’s examine the scientific process by discussing an actual scientific experiment conducted by researchers at the University of Washington. These researchers investigated whether a vaccine may reduce the incidence of the human papillomavirus (HPV). The experimental process and results were published in an article titled, “ A controlled trial of a human papillomavirus type 16 vaccine .”

Preliminary observations made by the researchers who conducted the HPV experiment are listed below:

• Human papillomavirus (HPV) is the most common sexually transmitted virus in the United States.
• There are about 40 different types of HPV. A significant number of people that have HPV are unaware of it because many of these viruses cause no symptoms.
• Some types of HPV can cause cervical cancer.
• About 4,000 women a year die of cervical cancer in the United States.

Practice Question

Researchers have developed a potential vaccine against HPV and want to test it. What is the first testable hypothesis that the researchers should study?

• HPV causes cervical cancer.
• People should not have unprotected sex with many partners.
• People who get the vaccine will not get HPV.
• The HPV vaccine will protect people against cancer.

[reveal-answer q=”20917″] Show Answer [/reveal-answer] [hidden-answer a=”20917″]Hypothesis A is not the best choice because this information is already known from previous studies. Hypothesis B is not testable because scientific hypotheses are not value statements; they do not include judgments like “should,” “better than,” etc. Scientific evidence certainly might support this value judgment, but a hypothesis would take a different form: “Having unprotected sex with many partners increases a person’s risk for cervical cancer.” Before the researchers can test if the vaccine protects against cancer (hypothesis D), they want to test if it protects against the virus. This statement will make an excellent hypothesis for the next study. The researchers should first test hypothesis C—whether or not the new vaccine can prevent HPV.[/hidden-answer]

Experimental Design

You’ve successfully identified a hypothesis for the University of Washington’s study on HPV: People who get the HPV vaccine will not get HPV.

The next step is to design an experiment that will test this hypothesis. There are several important factors to consider when designing a scientific experiment. First, scientific experiments must have an experimental group. This is the group that receives the experimental treatment necessary to address the hypothesis.

The experimental group receives the vaccine, but how can we know if the vaccine made a difference? Many things may change HPV infection rates in a group of people over time. To clearly show that the vaccine was effective in helping the experimental group, we need to include in our study an otherwise similar control group that does not get the treatment. We can then compare the two groups and determine if the vaccine made a difference. The control group shows us what happens in the absence of the factor under study.

However, the control group cannot get “nothing.” Instead, the control group often receives a placebo. A placebo is a procedure that has no expected therapeutic effect—such as giving a person a sugar pill or a shot containing only plain saline solution with no drug. Scientific studies have shown that the “placebo effect” can alter experimental results because when individuals are told that they are or are not being treated, this knowledge can alter their actions or their emotions, which can then alter the results of the experiment.

Moreover, if the doctor knows which group a patient is in, this can also influence the results of the experiment. Without saying so directly, the doctor may show—through body language or other subtle cues—his or her views about whether the patient is likely to get well. These errors can then alter the patient’s experience and change the results of the experiment. Therefore, many clinical studies are “double blind.” In these studies, neither the doctor nor the patient knows which group the patient is in until all experimental results have been collected.

Both placebo treatments and double-blind procedures are designed to prevent bias. Bias is any systematic error that makes a particular experimental outcome more or less likely. Errors can happen in any experiment: people make mistakes in measurement, instruments fail, computer glitches can alter data. But most such errors are random and don’t favor one outcome over another. Patients’ belief in a treatment can make it more likely to appear to “work.” Placebos and double-blind procedures are used to level the playing field so that both groups of study subjects are treated equally and share similar beliefs about their treatment.

The scientists who are researching the effectiveness of the HPV vaccine will test their hypothesis by separating 2,392 young women into two groups: the control group and the experimental group. Answer the following questions about these two groups.

• This group is given a placebo.
• This group is deliberately infected with HPV.
• This group is given nothing.
• This group is given the HPV vaccine.

[reveal-answer q=”918962″] Show Answers [/reveal-answer] [hidden-answer a=”918962″]

• a: This group is given a placebo. A placebo will be a shot, just like the HPV vaccine, but it will have no active ingredient. It may change peoples’ thinking or behavior to have such a shot given to them, but it will not stimulate the immune systems of the subjects in the same way as predicted for the vaccine itself.
• d: This group is given the HPV vaccine. The experimental group will receive the HPV vaccine and researchers will then be able to see if it works, when compared to the control group.

Experimental Variables

A variable is a characteristic of a subject (in this case, of a person in the study) that can vary over time or among individuals. Sometimes a variable takes the form of a category, such as male or female; often a variable can be measured precisely, such as body height. Ideally, only one variable is different between the control group and the experimental group in a scientific experiment. Otherwise, the researchers will not be able to determine which variable caused any differences seen in the results. For example, imagine that the people in the control group were, on average, much more sexually active than the people in the experimental group. If, at the end of the experiment, the control group had a higher rate of HPV infection, could you confidently determine why? Maybe the experimental subjects were protected by the vaccine, but maybe they were protected by their low level of sexual contact.

To avoid this situation, experimenters make sure that their subject groups are as similar as possible in all variables except for the variable that is being tested in the experiment. This variable, or factor, will be deliberately changed in the experimental group. The one variable that is different between the two groups is called the independent variable. An independent variable is known or hypothesized to cause some outcome. Imagine an educational researcher investigating the effectiveness of a new teaching strategy in a classroom. The experimental group receives the new teaching strategy, while the control group receives the traditional strategy. It is the teaching strategy that is the independent variable in this scenario. In an experiment, the independent variable is the variable that the scientist deliberately changes or imposes on the subjects.

Dependent variables are known or hypothesized consequences; they are the effects that result from changes or differences in an independent variable. In an experiment, the dependent variables are those that the scientist measures before, during, and particularly at the end of the experiment to see if they have changed as expected. The dependent variable must be stated so that it is clear how it will be observed or measured. Rather than comparing “learning” among students (which is a vague and difficult to measure concept), an educational researcher might choose to compare test scores, which are very specific and easy to measure.

In any real-world example, many, many variables MIGHT affect the outcome of an experiment, yet only one or a few independent variables can be tested. Other variables must be kept as similar as possible between the study groups and are called control variables . For our educational research example, if the control group consisted only of people between the ages of 18 and 20 and the experimental group contained people between the ages of 30 and 35, we would not know if it was the teaching strategy or the students’ ages that played a larger role in the results. To avoid this problem, a good study will be set up so that each group contains students with a similar age profile. In a well-designed educational research study, student age will be a controlled variable, along with other possibly important factors like gender, past educational achievement, and pre-existing knowledge of the subject area.

What is the independent variable in this experiment?

• Sex (all of the subjects will be female)
• Presence or absence of the HPV vaccine
• Presence or absence of HPV (the virus)

[reveal-answer q=”68680″]Show Answer[/reveal-answer] [hidden-answer a=”68680″]Answer b. Presence or absence of the HPV vaccine. This is the variable that is different between the control and the experimental groups. All the subjects in this study are female, so this variable is the same in all groups. In a well-designed study, the two groups will be of similar age. The presence or absence of the virus is what the researchers will measure at the end of the experiment. Ideally the two groups will both be HPV-free at the start of the experiment.

List three control variables other than age.

[practice-area rows=”3″][/practice-area] [reveal-answer q=”903121″]Show Answer[/reveal-answer] [hidden-answer a=”903121″]Some possible control variables would be: general health of the women, sexual activity, lifestyle, diet, socioeconomic status, etc.

What is the dependent variable in this experiment?

• Sex (male or female)
• Rates of HPV infection
• Age (years)

[reveal-answer q=”907103″]Show Answer[/reveal-answer] [hidden-answer a=”907103″]Answer b. Rates of HPV infection. The researchers will measure how many individuals got infected with HPV after a given period of time.[/hidden-answer]

Contributors and Attributions

• Revision and adaptation. Authored by : Shelli Carter and Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Scientific Inquiry. Provided by : Open Learning Initiative. Located at : https://oli.cmu.edu/jcourse/workbook/activity/page?context=434a5c2680020ca6017c03488572e0f8 . Project : Introduction to Biology (Open + Free). License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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33 Perceptual Constancy

Jonathan Cohen, University of California, San Diego

• Published: 13 January 2014
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One of the central, fundamental, and general facts about perception—and one that crucially underpins our effective engagement with the world—is that (some aspects of) our perceptual responses remain stable even through dramatic changes in perceptual circumstances that result in dramatic changes in transduced perceptual signals. This chapter presents an overview of what is and is not known about this sort of perceptual constancy. It discusses disputes about the description of the phenomenon, the psychophysical methods for its assessment, and the relation between perceptual constancy and perceptual contrast.The chapter uses constancy in colour vision (i.e., colour constancy) as a central example, and surveys a number of proposals within the research tradition of computational colour constancy for understanding the computational strategies by which perception extracts stabilities, the mechanisms underlying their implementation, and the ways these distinct strategies and mechanisms are combined with one another in real-time perception. Finally, it considers whether perceptual constancy should be construed as perceptual or cognitive in character.

Our eyes deceive us when we look down railway tracks, but our brains do not. The rails appear to converge in the distance, but we know that the rails are parallel. We know that they are the same distance apart a mile down the track as they are where we are standing, so the brain says, ‘The tracks only appear to converge because they are distant.’ But how does the brain know that the tracks are distant? The brain answers, ‘They must be distant because they appear to converge.’ (The flow of this logic must shock computer programmers, but they are accustomed to the limitations of inferior hardware.) ( Hunter et al., 2007 : 82)

1 Introduction

Students of perception have long known that perceptual constancy is an important aspect of our perceptual interaction with the world. Here is a simple example of the phenomenon concerning colour perception: there is some ordinary sense in which an unpainted ceramic coffee cup made from a uniform material looks a uniform colour when it is viewed under uneven illumination, even though the light reflected by the shaded regions to our eyes is quite different from the light reflected by the unshaded regions to our eyes (see Figure 33.1 ). Or consider this example concerning size perception: there is some ordinary sense in which two telephone poles look the same size when the first is viewed from 100 metres and when the second is viewed from 1 metre, even though the visual angle subtended by the two poles on our retinae is very different (see Figure 33.2 ). Or consider this example concerning shape perception: there is some ordinary sense in which a penny looks round both when viewed head on and when viewed from an acute angle, even though the area projected by the penny onto our retinae under these two conditions is very different (see Figure 33.3 ). Or, finally, consider this example concerning auditory volume perception (which I cannot depict graphically): there is some ordinary sense in which a speaker’s voice sounds the same volume when heard from across the room and when heard from a distance of 1 metre, even though the energy striking our ears under these two conditions is very different.

There is some good sense in which the regions of the cup in shadow and the regions of the cup in direct sunlight look the same in colour.

The kind of perceptual constancy exemplified in these cases, and others like them, is ubiquitous, ordinary, and central to the way perception tells us about the world in which we live. Without this kind of constancy, we would experience the world as a Jamesian blooming, buzzing confusion—a constant flux of colours, shapes, and sounds with no apparent organization. For, unavoidably, the perceptual signals incident on our transducers are the results of not only the kinds of distal individuals there are and properties they exemplify, but also the constantly changing details of the circumstances under which we perceive (the angle and distance from the perceived object, the lighting conditions, the ambient noise, our own cognitive and perceptual histories and futures, our expectations, and so on). If perception were incapable of representing the world as in some ways constant despite various changes in our perceptual circumstances, it would radically misrepresent the distal world: it would fail to reveal ways in which the world is stable. And since these ways underpin our engagement with that world, this would (disastrously) undermine the possibility of effective action and empirical knowledge.

However, despite its recognized ubiquity and importance, there are several respects in which the phenomenon of perceptual constancy is poorly understood. Aside from the independent interest in getting clear on these matters, perceptual constancy has figured prominently in recent debates about the ontology of colours and other sensible qualities, knowledge, attention, mental modularity, the contents of mental representation, and the objectivity of our representations of the world. 1 Therefore, in this chapter I’ll review some of what is and is not known about perceptual constancy with an eye to drawing connections with ongoing controversies in the philosophy of perception and elsewhere. 2

There is some good sense in which the telephone poles seen from different distances look the same size.

There is some good sense in which the penny looks the same in shape when seen from two different angles.

2 Perceptual Constancy as Perceptual Stability

As both its name and the initial examples used to introduce the phenomenon above suggest, perceptual constancy is, in some sense yet to be explained, about the absence of change. Indeed, the textbook characterization has it that perceptual constancy is nothing more or less than a stability in perceptual response across a range of varying perceptual conditions. 3 Thus, in the case of the unevenly illuminated coffeecup (Figure 33.1 ), the idea is that the perceptual system represents the distinct regions of the cup as bearing the same colour even though there is variation in the illumination incident on them (and, therefore, in the total amount of light energy they reflect to our retinal transducers). Or, again, in the case of volume perception, the thought is that perception represents the speaker’s voice as having the same volume even though there is significant variation in the distance from which it is heard (and, therefore, in the total amount of auditory energy absorbed by our aural transducers).

While I will want to qualify the above characterization in what follows, one of the ways in which it is useful and interesting is that it presents perception as an active process of engagement with the world. It suggests that perception is not just a matter of passively registering the impinging energy array, but of somehow articulating or decomposing that array to arrive at a representation of a subset of the distal features that contribute to the configuration of the array.

Unfortunately, the textbook characterization of perceptual constancy just presented can’t be quite right by itself. (Or, alternatively, we can retain that characterization by itself, but only at the cost of emptying the phenomenon of all of its instances.) For it is not true that our perceptual responses are entirely constant in the kinds of cases at issue. Returning once again to the unevenly illuminated coffeecup, we know there must be a difference in a subject’s perceptual response to the shaded and unshaded regions of the cup, or else she would be unable to discriminate the luminance boundary between them. Likewise in canonical cases of size constancy (subjects’ perceptual responses can clearly distinguish in some size-related way between the perception of the telephone pole at 100m and the perception of the telephone pole at 1m), shape constancy (there is clearly a discriminable difference between the subject’s perception of the penny seen head on and her perception of the penny seen at an acute angle), auditory volume constancy (there is clearly a discriminable difference between the subject’s perception of the speaker’s voice from across the room and her perception of the speaker’s voice from a distance of 1 meter), and all of the other canonical instances of perceptual constancy.

An instance of simultaneous lightness contrast: the central patches are qualitatively identical, but perception represents them differently because of the contrast with surrounding items.

Indeed, the non-constancy of our perceptual responses across variations in the perceptual circumstances is not only immediately apparent, but underlies another much-observed and much-discussed aspect of perception—the phenomenon of perceptual contrast. 4 It is easy to find instances of perceptual contrast once one begins to look for them. For example, Figure 33.4 illustrates an instance of simultaneous lightness contrast: although the two central patches depicted here are qualitatively intrinsically identical, the perceptual system represents them as different in colour because of the different ways in which they contrast in lightness with surrounding items. Simultaneous lightness contrast plays a role in many classic visual illusions, such as the appearance of grey dots at the intersections of an achromatic grid (the Hermann grid illusion, Figure 33.5 ), the interpretation of a pair of opposed lightness gradients as two constant lightness regions separated by an edge (the Cornsweet illusion, Figure 33.6 ), and the appearance of light or dark bands next to the boundary between two different lightness gradients, even when the lightness on both sides of the boundary is the same (Mach bands, Figure 33.7 ). 5

Perceptual contrast is by no means restricted to the perception of lightness/brightness; within vision there are also simultaneous contrast effects for chromatic colour, size, spatial frequency, orientation, motion, and speed, inter alia . For example, Figure 33.8 illustrates an instance of simultaneous size contrast: although the central circles are the same geometric size, the perceptual system represents them as different in size because of the contrast with the different elements surrounding them. Moreover, in addition to simultaneous contrast—contrast between simultaneously perceived items, there are also ubiquitous instances of successive contrast—effects of contrast between successively perceived items for each of these dimensions. And, of course, contrast occurs in non-visual modalities as well (although there is much less systematic investigation of contrast outside vision). Thus, in gustation, we commonly observe that sweet wines strike us as markedly less sweet when consumed with dessert items (which contain much more sugar than the wines) than on their own. In audition, we find that it is much easier to detect variations in pitch (say, while tuning a guitar string) by contrasting the target against other (simultaneously or successively perceived) tones. Or, again, in kinaesthesia, Gibson (1933) reports that after blindfolded subjects run their fingers over a curved surface for three minutes, straight edges seem to them to be curved in the opposite direction.

The Hermann grid illusion.

The Cornsweet illusion.

Mach bands.

The Ebbinghaus illusion is an instance of perceptual simultaneous size contrast.

In each of these cases, the perceptual system reacts differently to objects depending on how they contrast with other perceived items. Perceptual contrast occurs because perceptual systems tend to be responsive to magnitude differences, as opposed to magnitudes themselves. 6 For our purposes, the phenomenon of contrast is important because it makes for a vivid demonstration of the observation made above: contrary to the textbook characterization, our perceptual responses to an object/property are not constant, but instead change in interesting and systematic ways across variations in the perceptual circumstances. 7

3 Psychophysics and Measurement

So far our discussion has been framed by questions of which qualitative discriminations are made by perceivers. However, for many purposes it is useful to have quantitative measures of similarity/dissimilarity in cases of perceptual constancy. The standard technique used for this purpose is to measure the dissimilarity between a subject’s reaction to two stimuli by measuring how much of a change she must make to one of them, holding the other fixed, before she regards the two as a perceptual match. 8

Thus, for example, the main quantitative measure by which contemporary psychophysicists assess colour constancy, known as asymmetric colour matching ( Wyszecki and Stiles, 1982 : 281–293), involves asking subjects to change the chromaticity (or lightness, in lightness constancy experiments) of a test patch under one illuminant until it perceptually matches a standard patch under a different illuminant. The size of the chromaticity (/lightness) difference between the test and the standard patches required to achieve a perceptual match, then, is a quantitative measure of the effect of the illumination difference between test and standard patches on the subject’s total perceptual response to them—it is an operational measure of the extent to which perceptual responses are unchanging across variations in perceptual conditions.

Such quantitative measures reinforce the assessment made above on the strength of qualitative reactions: in canonical instances of colour constancy, subjects’ perceptual responses are not simply unchanging—rather, they are in some respects similar or unchanging and in some other respects dissimilar or changing. Moreover, interestingly, (most) subjects can be made to switch between attending to the respects of similarity and the respects of dissimilarity in many canonical instances simply by changing the experimental instructions. For example, Arend and Reeves (1986) found that subjects in an asymmetric colour matching paradigm responded to instructions to ‘adjust the test patch to match its hue and saturation to those of the standard patch’ (1986: 1744) by making large chromaticity changes (suggesting that their perceptual systems initially represented the test and the standard patch as quite different), although the same subjects responded to instructions to ‘adjust the test patch to look as if it were “cut from the same piece of paper” as the standard, i.e. to match its surface color’ (1986: 1744) by making very small chromaticity changes (suggesting that their perceptual systems initially represented the test and the standard patch as quite similar). 9

4 Stability and Instability

It seems, then, that the right thing to say is not, or not just, that the perceptual system responds in a constant or unchanging way in the face of variations in the perceptual conditions—either as a general matter or even in the cases that have been put forward as parade instances of perceptual constancy. On the other hand, neither does it seem that the perceptual system responds by treating objects as merely approximately the same in different perceptual conditions—the similarities and dissimilarities that perception recognizes are not collapsed into a single scalar value somewhere between the extremes of perfect qualitative match and perfect qualitative mismatch. Rather, what we should say is that perception represents both some aspects of similarity and some aspects of dissimilarity in its responses to objects across changing perceptual circumstances. Moreover, we should recognize that both the respects of similarity and the respects of dissimilarity are in many cases available to the perceiving subject for the purpose of making perceptual discriminations. 10 This raises an important puzzle for the understanding of perceptual constancy. Given that there is clearly substantial variation in our perceptual responses to objects across changes in perceptual circumstances even in canonical cases of constancy (such as those used to introduce the topic in section 1 ), it won’t do to think of constancy simply in terms of stability of perceptual response. Rather, if we want to be able to say that there is perceptual constancy in such canonical cases, then we owe a characterization of just which kinds of perceptual similarity, in the context of just which kinds of variation in perceptual circumstances, are necessary for the exemplification of perceptual constancy. Moreover, we need a characterization that is applicable across the broad range of cases to which we want to apply the notion. Unfortunately, there is at present no adequate and fully general characterization of this sort, and therefore no general understanding of what perceptual constancy amounts to.

5 Computation and Constancy

While the problems just discussed should not be underplayed, neither should they make us lose sight of the initial observation that makes perceptual constancy so interesting: in canonical cases there is some interesting respect in which perception is unchanging in its treatment of an object despite differences in the conditions under which it is perceived, and despite the attendant differences in the total signals impinging on our sensory transducers, even if these must be characterized in a case by case way.

This observation naturally invites the important question about how perception pulls off the feats of constant representation in the face of inconstant perceptual circumstances that it does. That is, given the complex total signal striking the transducers—a signal that is determined jointly by the features of perceived objects and perceptual circumstances, and therefore that changes as circumstances vary—how does the perceptual system arrive at a verdict about whether the perceived objects change? How, for example, does the perceptual system start with the varying array of light intensities reflected by the cup in Figure 33.1 and end with the information that the entire cup is uniform in colour (or, more cautiously, in some colour-related respect)?

A burgeoning subfield of perceptual psychology has attempted to build empirically adequate computational models that would answer this question. Perhaps the dominant approach within this tradition is to think about perception as computing a solution to an ‘inverse problem’: the job is to find ways of factoring apart the complex resultant that is the impinging energy array to arrive at a representation of the distal features that contribute to the resultant. Thus, for example, consider colour constancy once again, since that is the area in which the most intense research on computational methods has been carried out. 11 In colour perception the perceptual system begins with an array of light intensities on the retina which is the joint product of two factors—the features of the illumination incident on surfaces and those of the surfaces that reflect light to our eyes. The leading approach to computational colour constancy has involved finding methods of estimating the properties of the illuminant so that the system can, as it were, subtract off this factor from the total signal (in Helmholtz’s phrase, ‘discounting the illuminant’), leaving an illumination-independent characterization of the reflecting surface ( Maloney and Wandell, 1986 ; Brainard et al., 1997 ; Brainard, 1998 ). Crucially, since this characterization is illumination-independent, the thought is that it will be shared by distinct regions of a uniform surface that happen to be illuminated differently (e.g. the regions of the cup in Figure 33.1 ). Therefore, a perceptual system that performed this sort of computation would be able to treat such regions as (in this one respect) perceptually similar, even though they are clearly discriminably different.

Modellers have pursued a wide variety of strategies for estimating the separate contributions to the retinal array made by illuminants and surfaces. For example, Maloney (1986) ; Maloney and Wandell (1986) show how a system with more classes of receptors than there are degrees of freedom in (the system’s linear models of) surface reflection profiles can exploit its multiple receptoral signals to recover representations of surfaces. Other approaches solve the inverse problem by adding as constraints assumptions about the kinds of scenes perceptual systems will encounter. Thus, Buchsbaum (1980) proposes a model that rests on the assumption that the median lightness value in a scene corresponds to a middle grey surface, and computes from this assumption what the incident illumination would have to be to result in the observed intensity array. A related but distinct strategy proceeds from the assumption that anchors some part of the visual image (rather than a mean) to an extremal lightness value—for example, by treating the lightest visible surface as white ( Land and McCann, 1971 ; Gilchrist et al., 1999 ). Others have proposed estimating illuminants from information about mutual reflections in the scene ( Funt et al., 1991 ), the boundaries of regions known to be specular reflections ( D’Zmura and Lennie, 1986 ; Lee, 1986 ), and shadows ( D’Zmura, 1992 ). Still others propose to solve the inverse problem by appeal to higher-order scene statistics, such as the correlation between redness and luminance within the scene ( Golz and MacLeod, 2002 ) or the statistical distribution of colours within the scene ( MacLeod, 2003 ; Brainard et al., 2006 ). In recent years, many theorists have advocated ‘Bayesian’ probabilistic models as solutions to the illuminant estimation problem. According to Bayesians, the visual system first selects as its estimate that hypothesis about the illuminant with the highest probability conditional on the data received by the transducers, constrained by the prior probability of that illuminant hypothesis; then it goes on to select as its estimate about distal surfaces that hypothesis with the highest probability conditional on the transducer data and the illuminant estimate obtained at the first step, again constrained by prior probabilities assigned to the various hypotheses about surfaces ( Brainard and Freeman, 1997 ). 12 It is possible, of course, that human colour constancy involves a combination of these methods, or others.

However, there is a different class of computational models for perceptual constancy—one that has received much less attention from philosophers—that rejects the assumption that constancy requires factoring out of the perceptual signal a representation of the distinctive contribution made by the perceived object and its features. Thus, Craven and Foster (1992) ; Foster and Nascimento (1994) ; Dannemiller (1993) ; Zaidi (1998 , 2001 ); Amano et al. (2005) suggest that perceptual systems compute colour constancy not by deriving an illumination-independent representation of object surfaces, but by comparing total perceptual signals in light of what is known about the illumination or other properties of the total scene. Crudely, the idea is that the system can ask whether the difference between the two perceptual signals it gets from two perceptual episodes (simultaneous or not) can be accounted for by the behaviour of the illumination (rather than by a difference in the surfaces perceived on the two occasions). If, say, the system represents that the illumination profile includes a shadow cast over the scene (say, by a partially occluded light source) then this would have predictable effects on the perceptual signal: there would be higher intensities in the (portion of the) signal corresponding to the directly illuminated regions and lower intensities corresponding to the (portion of the) signal corresponding to the region in shadow. Therefore, the system can treat the image regions as being relevantly alike although they cause different perceptual signals (i.e. it can display perceptual constancy) if it can conclude that the two different perceptual signals lie in the graph of a transformation consistent with illumination variations.

Here, as in more traditional computational models, the computation of colour constancy depends on deriving from the perceptual signal an estimate of the illumination. But unlike more traditional models, the suggestion is that the system can compute constancy directly from the perceptual signal and the illumination estimate, without going to the trouble of separately deriving a closed-form representation of object surfaces. Also unlike more traditional models, here there is no suggestion that the perceptual system discounts or discards the illuminant—on the contrary, the claim is that the system’s continuing to represent the illuminant is absolutely vital to the computation of constancy. 13 And, though these are proposals about colour constancy in particular, the general lessons they teach may well be applicable for other visual and non-visual instances of perceptual constancy as well.

6 Is Perceptual Constancy Perceptual?

Perceptual constancy shows that perceivers are not passive receivers of the array of energy falling on their receptors—for if they were, they could not react in similar ways (in some respects), as they sometimes do, when there are large differences in that array. Something more must be going on. But is that something more a perceptual process? Or is it a post-perceptual process that gets its start at the point where perception ends? It is clear that, for example, subjects will (under some experimental instructions) judge that the penny in Figure 33.3 is relevantly alike in shape when presented from two distinct viewpoints. But what is not clear is whether that judgement is informed by the output of perceptual systems by themselves, or by the integration of perceptual systems together with certain kinds of cognitive corrective factors (e.g. memories about the canonical colours, shapes, etc. of similar objects). 14

An early instance of a post-perceptual/cognitive view about perceptual constancy is the proposal, defended by von Helmholtz (1962) and Hering (1964) , that colour constancy is (at least in part) driven by our memory/knowledge about the colours of familiar objects. 15 This ‘memory theory’ of colour constancy faces several difficulties. First, Katz (1911) showed that there is colour constancy for random and presumably newly encountered objects (for which there could not be colour memory), and thereby demonstrated that the sort of memory/knowledge enlisted by the memory theory is not necessary for successful colour constancy. Second, it is doubtful that our memory for colour is sufficiently accurate to underwrite observed levels of constancy ( Hurvich, 1981 : 2; Halsey and Chapanis, 1951: 1058 ). A third line of concern for memory (and, more generally, cognitive) explanations of colour constancy is that one can dissociate the capacity for colour constancy from (what are generally taken to be) cognitive capacities in both directions. In one direction, there appears to be robust colour constancy in goldfish, honeybees, and several other non-human animals (see the review in Neumeyer (1998) ) and human infants somewhere between 9 and 20 weeks old ( Dannemiller and Hanko, 1987 ; Dannemiller, 1989 ), whose cognitive/conceptual resources are usually assumed to be pretty limited. In the other direction, there is (admittedly more limited) evidence from lesion studies where colour constancy is impaired but memory and other conceptual capacities are spared ( Rüttiger et al., 1999 ).

These reasons, among others, have led investigators to search for less obviously cognitive explanations of colour constancy. For example, contemporary explanations of colour constancy often cite several kinds of retinal adaptation (changes in the sensitivity of retinal receptors as a response to incident light) including adaptation over temporally and spatially local regions (so-called von Kries adaptation), adaptation to the spatial mean of the whole scene, and adaptation to the region of highest intensity in the scene ( McCann, 2004 ). However, there is evidence suggesting that these factors are not always sufficient for colour constancy by themselves ( Kraft and Brainard, 1999 ). Moreover, even if they are not by themselves sufficient for constancy, it appears that cognitive factors may make an important contribution to constancy after all: several investigators have found that familiarity for types of objects perceived (e.g. common fruits and vegetables) enhances colour constancy ( Hurlbert and Ling, 2005 ; Olkkonen et al., 2008 , 2012 ).

A similarly complicated mix of findings seems to be the pattern for shape and size constancy. On the one hand, there is evidence that the visual system can in some conditions (e.g. at short distance ranges) compute constant size and shape from relatively low-level perceptual cues such as vergence (information about the relative ocular positions of the two eyes in their sockets) and disparities in the retinal projections from the two eyes. And, once again, there is double dissociation between constancy for size/shape and cognitive sophistication. Thus, for example, there appears to be size constancy (at least at short distance ranges) in comparatively cognitively unsophisticated creatures such as newborn human beings ( Granrud, 2006 , 2012 ; Slater et al., 1990 ) non-human primates ( Fujita, 1997 ; Barbet and Fagot, 2002 ), goldfish ( Douglas et al., 1988 ), and amphibians ( Ingle, 1998 ). And, in the other direction, Cohen et al. (1994) give evidence of the selective impairment of certain kinds of size constancy that spare general cognitive abilities. All that said, it is also true that higher-level, cognitive cues—for example, memory for the canonical shape and size of recognized objects, comparison to other perceived items whose shape and form are established independently, the smoothed appearance of texture from a distance—enhance shape and size constancy substantially (for a useful overview, see Palmer, 1999 : ch. 5, ch. 7).

Cumulatively, these results strongly suggest that perceptual constancy is neither exclusively perceptual nor exclusively cognitive. Instead, it appears that ‘the’ phenomenon of perceptual constancy, even considered as constancy for a single dimension of a single quality within a single modality (e.g. just for lightness), is an interaction effect produced by several different mechanisms operating across different spatial and temporal scales—some possibly more and some possibly less cognitive than others, depending on how one chooses to mark the cognitive/non-cognitive distinction. 16 Whether any one of these mechanisms contributes to perceptual constancy on any particular occasion will depend on the details of many features of the perceptual circumstance.

7 Conclusion

While I have argued that the perceptual stabilities emphasized by traditional characterizations of perceptual constancy can only be part of the story, it remains true, indisputable, and important that some aspects of our perceptual responses are stable even through changes in perceptual circumstances that result in changes in transduced perceptual signals. It is no less indisputable that there are important lessons to be learned from the phenomenon of perceptual constancy, although many unresolved questions remain.

As we have seen, there is no completely general account of which dimensions of perceptual response must remain fixed, and which may vary, across which kinds of variation in perceptual conditions, for a perceptual episode to count as an instance of perceptual constancy. Moreover, there is no general understanding of the relation between perceptual constancy and perceptual contrast. And, partly because so much less is understood about both constancy and contrast in non-visual modalities, it is so far unclear what (if any) systematic cross-modal generalizations hold for each. Finally, the range of computational strategies that perception uses to extract stabilities, of the mechanisms underlying their implementation, and of the ways these distinct strategies and mechanisms are combined with one another in real-time perception remain incompletely understood.

Notwithstanding these substantial gaps in our knowledge, it seems clear that constancy is an absolutely fundamental aspect of perception, and therefore that it will figure centrally in our ultimate understanding of mind–world interaction. 17

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Recently a number of philosophers have returned to issues about constancy anew; for example, see Hilbert (2005) ; Thompson (2006) ; Cohen (2008) ; Bradley (2008) ; Hatfield (2009) ; Gert (2010) ; Matthen (2010) ; Wright (2013) . Also see Burge (2010) , for whom perceptual constancy is used as a touchstone for the objectivity of intentional representation quite generally.

Because there is vastly more research, by both philosophers and psychologists, on perceptual constancy in vision than in other modalities (and, even more particularly, on colour constancy), this entry is, regrettably, unavoidably visuocentric in its choice of examples and theories discussed. There remains much work to be done in this area.

See, for example, Byrne and Hilbert (1997 : 445), Zaidi (1999 : 339), Palmer (1999 : 312–314, 723), Goldstein (1999 : 567), Brainard et al. (2003 : 308–309).

Whittle (2003) provides an excellent overview of the importance of perceptual contrast for colour vision.

For a discussion of the role of contrast in many lightness illusions, see Adelson (2000) .

The standard physiological explanation of this generalization turns on lateral inhibition between neurons carrying perceptual information (e.g. retinal ganglion cells, in the case of lightness perception). Lateral inhibition results in the suppression of all but the most stimulated/least inhibited neurons; consequently, the overall firing pattern is highest in cells corresponding to parts of the stimulus where there is a steep spatial/temporal gradient—where a small population of most active cells is left relatively uninhibited by the firing of their neighbours.

Objection: The cases I have used to highlight contrast (the Hermann grid illusion, the Ebbinghaus illusion, etc.) are often put forward as textbook cases of perceptual illusion. They give no reason to suppose there is substantial non-constancy in veridical cases of perception.

Response: Contrast is pretty clearly at work in ordinary perception; I have relied on textbook cases of perceptual illusion only because they make the results of perceptual contrast so vividly apparent. However, a theory of perception that set aside cases involving the operation of perceptual constancy would have little to say about the kinds of perceptual systems we happen to enjoy.

Note that perceptual matching is a statistical notion: two stimuli count as a perceptual match for a subject if the subject is unable to discriminate one from the other over several presentations at a rate higher than that attributable to chance.

That the perceptual system displays this sort of bimodal behaviour has been understood for a long time; see Evans (1948 ; 163–164); Beck (1972 : 66–67) for an overview of some of the earlier work. For more recent work (mostly on cases of simultaneous colour constancy), see Blackwell and Buchsbaum (1988) ; Valberg and Lange-Malecki (1990) ; Arend et al. (1991) ; Troost and deWeert (1991) ; Cornelissen and Brenner (1995) ; Bäuml (1999) . While there has been far less systematic investigation of this effect with respect to cases of successive colour constancy, investigators have found the same sort of bimodal pattern of results here too ( Delahunt (2001 : 114–117); Delahunt and Brainard (2004 : 71–74)).

Many philosophers and psychologists working in this area have tended to be so impressed by the constant aspects of our perceptual responses that they have played down, dismissed, or, more frequently, just ignored the inconstant aspects of our perceptual responses to the same scenarios. Thus, one sometimes sees assertions to the effect that the inconstant aspects of perception are ‘unnatural and sophisticated … [and] difficult to attain’ ( Smith, 2002 : 182, cf. 178). Whatever else we think of such claims, I suggest that an adequate theory of perception must account for all of the ways in which perceptual systems respond to the world rather than only some of them—whether these responses are natural or unnatural, naive or sophisticated, and easily attained or not.

Emphasis on constant aspects of our perceptual responses at the expense of inconstant aspects also shows up in a prominent line of argument for the view that colours are illumination-independent features of objects (I discuss these arguments critically in Cohen, 2008 ). For example, Tye (2000 : 147–148), Hilbert (1987 : 65), and Byrne and Hilbert (2003 : 9) explicitly appeal to constancy reactions in colour perception as cases where the very same feature can be extracted despite variation in the ambient illumination, and infer from this claim that colour (which they reasonably assume is indeed represented by colour perception) is itself illumination-independent. However, if it is reasonable to take constancy reactions to show that perception represents constant features, it is no less (and no more) reasonable to take inconstancy reactions to show that perception represents inconstant features. But if colour perception represents both constant and inconstant features, there is no sound inference from the premiss that colour is represented by perception to the conclusion that colour is a constant (here, illumination-independent) feature. (Nor, for that matter, is there a sound inference from that premiss to the conclusion that colour is an inconstant/illumination-dependent feature.) Consequently, the sort of appeal to perceptual constancy made by these authors does not successfully motivate the claim that colours are illumination-independent object features.

Much of the work in this tradition is restricted to the perception of surface colours (as opposed to the colours of lights, volumes, films, and so on). Moreover, many (but not all) of the models depend on the simplifying assumptions that surfaces are illuminated by constant or smoothly varying, and exclusively diffuse, illumination.

In such models, the kinds of substantive assumptions about the distal world that ground the deterministic models described above—e.g. about the way illuminants vary smoothly in ecological settings, about where the mean lightness values can be expected, and so on—show up as well, but here in the form of the prior probabilities about both illuminant and surfaces used to constrain the assignment of posterior probabilities.

There are several further pieces of evidence that confirm the prediction of such models that perceptual systems maintain representations of the illumination rather than simply discarding them. Perhaps the most direct is just that subjects can, when asked, make matches of ambient illumination as opposed to surface lightness ( Katz, 1935 ; Gilchrist, 1988 ; Hurlbert, 1989 ; Jameson and Hurvich, 1989 ; Zaidi, 1998 ).

It is worth noting that the possibility of computing constancy without deriving specific object/object-property representations undercuts the (oft-made) claim that object tracking and reidentification depend on representing condition-independent object properties.

Obviously, one’s approach to this last question will be shaped, in part, by how one understands the cognition/perception distinction. I won’t attempt to settle this vexed issue here, but will simply take for granted that, e.g., memory for the colours/shapes/sizes/etc. of objects and other apparent instances of concept deployment fall on the cognitive side of the divide, and that, e.g. receptoral adaptation effects are perceptual. What is at stake is (of course) not the labels, but instead what kinds of causal explanatory resources are invoked to explain observed instances of perceptual constancy.

An even earlier post-perceptual view of constancy emerges from Locke’s discussion of the role of judgement in sensation:

When we set before our eyes a round globe of any uniform colour … it is certain that the Idea thereby imprinted in our Mind, is of a flat Circle variously shadow’d, with several degrees of Light and Brightness coming to our Eyes. But we having by use been accustomed to perceive, what kind of appearance convex Bodies are wont to make in us; what alterations are made in the reflections of Light, by the difference of the sensible Figures of Bodies, the Judgment presently, by an habitual custom, alters the Appearances into their Causes: So that from that, which truly is variety of shadow or colour, collecting the Figure, it makes it pass for a mark of Figure, and frames to it self the perception of a convex Figure, and an uniform Colour; when the Idea we receive from thence, is only a Plain variously colour’d, as is evident in Painting. ( Locke, 1975 : II.ix.8)

Cf. Foster (2003) , who points to the heterogeneity of the factors in operation as a reason to be sceptical about the very existence of colour constancy.

Many thanks to Damon Crockett, Joshua Gert, Gary Hatfield, Don MacLeod, Mohan Matthen, and Sam Rickless for discussion and comments on earlier drafts.

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7 Hypothesis Testing

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines?  Science  (from the Latin scientia, meaning “knowledge”) can be defined as knowledge about the natural world.

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method . The scientific process was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem solving method.

The scientific process typically starts with an observation  (often a problem to be solved) that leads to a question.  Science is very good at answering questions having to do with observations about the natural world, but is very bad at answering questions having to do with purely moral questions, aesthetic questions, personal opinions, or what can be generally categorized as spiritual questions. Science has cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. Imagine that one morning when you wake up and flip a the switch to turn on your bedside lamp, the light won’t turn on. That is an observation that also describes a problem: the lights won’t turn on. Of course, you would next ask the question: “Why won’t the light turn on?”

A hypothesis  is a suggested explanation that can be tested. A hypothesis is NOT the question you are trying to answer – it is what you think the answer to the question will be and why .  Several hypotheses may be proposed as answers to one question. For example, one hypothesis about the question “Why won’t the light turn on?” is “The light won’t turn on because the bulb is burned out.” There are also other possible answers to the question, and therefore other hypotheses may be proposed. A second hypothesis is “The light won’t turn on because the lamp is unplugged” or “The light won’t turn on because the power is out.” A hypothesis should be based on credible background information. A hypothesis is NOT just a guess (not even an educated one), although it can be based on your prior experience (such as in the example where the light won’t turn on). In general, hypotheses in biology should be based on a credible, referenced source of information.

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a dog thinks is not testable, because we can’t tell what a dog thinks. It should also be  falsifiable,  meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Red is a better color than blue.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important: a hypothesis can be disproven, or eliminated, but it can never be proven.  If an experiment fails to disprove a hypothesis, then that explanation (the hypothesis) is supported as the answer to the question. However, that doesn’t mean that later on, we won’t find a better explanation or design a better experiment that will disprove the first hypothesis and lead to a better one.

A variable is any part of the experiment that can vary or change during the experiment. Typically, an experiment only tests one variable and all the other conditions in the experiment are held constant.

• The variable that is being changed or tested is known as the  independent variable .
• The  dependent variable  is the thing (or things) that you are measuring as the outcome of your experiment.
• A  constant  is a condition that is the same between all of the tested groups.
• A confounding variable  is a condition that is not held constant that could affect the experimental results.

Let’s start with the first hypothesis given above for the light bulb experiment: the bulb is burned out. When testing this hypothesis, the independent variable (the thing that you are testing) would be changing the light bulb and the dependent variable is whether or not the light turns on.

• HINT: You should be able to put your identified independent and dependent variables into the phrase “dependent depends on independent”. If you say “whether or not the light turns on depends on changing the light bulb” this makes sense and describes this experiment. In contrast, if you say “changing the light bulb depends on whether or not the light turns on” it doesn’t make sense.

It would be important to hold all the other aspects of the environment constant, for example not messing with the lamp cord or trying to turn the lamp on using a different light switch. If the entire house had lost power during the experiment because a car hit the power pole, that would be a confounding variable.

You may have learned that a hypothesis can be phrased as an “If..then…” statement. Simple hypotheses can be phrased that way (but they must always also include a “because”), but more complicated hypotheses may require several sentences. It is also very easy to get confused by trying to put your hypothesis into this format. Don’t worry about phrasing hypotheses as “if…then” statements – that is almost never done in experiments outside a classroom.

The results  of your experiment are the data that you collect as the outcome.  In the light experiment, your results are either that the light turns on or the light doesn’t turn on. Based on your results, you can make a conclusion. Your conclusion  uses the results to answer your original question.

We can put the experiment with the light that won’t go in into the figure above:

• Observation: the light won’t turn on.
• Question: why won’t the light turn on?
• Hypothesis: the lightbulb is burned out.
• Prediction: if I change the lightbulb (independent variable), then the light will turn on (dependent variable).
• Experiment: change the lightbulb while leaving all other variables the same.
• Analyze the results: the light didn’t turn on.
• Conclusion: The lightbulb isn’t burned out. The results do not support the hypothesis, time to develop a new one!
• Hypothesis 2: the lamp is unplugged.
• Prediction 2: if I plug in the lamp, then the light will turn on.
• Experiment: plug in the lamp
• Analyze the results: the light turned on!
• Conclusion: The light wouldn’t turn on because the lamp was unplugged. The results support the hypothesis, it’s time to move on to the next experiment!

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Control Groups

Another important aspect of designing an experiment is the presence of one or more control groups. A control group  allows you to make a comparison that is important for interpreting your results. Control groups are samples that help you to determine that differences between your experimental groups are due to your treatment rather than a different variable – they eliminate alternate explanations for your results (including experimental error and experimenter bias). They increase reliability, often through the comparison of control measurements and measurements of the experimental groups. Often, the control group is a sample that is not treated with the independent variable, but is otherwise treated the same way as your experimental sample. This type of control group is treated the same way as the experimental group except it does not get treated with the independent variable. Therefore, if the results of the experimental group differ from the control group, the difference must be due to the change of the independent, rather than some outside factor. It is common in complex experiments (such as those published in scientific journals) to have more control groups than experimental groups.

Question: Which fertilizer will produce the greatest number of tomatoes when applied to the plants?

Hypothesis : If I apply different brands of fertilizer to tomato plants, the most tomatoes will be produced from plants watered with Brand A because Brand A advertises that it produces twice as many tomatoes as other leading brands.

Experiment:  Purchase 10 tomato plants of the same type from the same nursery. Pick plants that are similar in size and age. Divide the plants into two groups of 5. Apply Brand A to the first group and Brand B to the second group according to the instructions on the packages. After 10 weeks, count the number of tomatoes on each plant.

Independent Variable:  Brand of fertilizer.

Dependent Variable : Number of tomatoes.

• The number of tomatoes produced depends on the brand of fertilizer applied to the plants.

Constants:  amount of water, type of soil, size of pot, amount of light, type of tomato plant, length of time plants were grown.

Confounding variables : any of the above that are not held constant, plant health, diseases present in the soil or plant before it was purchased.

Results:  Tomatoes fertilized with Brand A  produced an average of 20 tomatoes per plant, while tomatoes fertilized with Brand B produced an average of 10 tomatoes per plant.

You’d want to use Brand A next time you grow tomatoes, right? But what if I told you that plants grown without fertilizer produced an average of 30 tomatoes per plant! Now what will you use on your tomatoes?

Results including control group : Tomatoes which received no fertilizer produced more tomatoes than either brand of fertilizer.

Conclusion:  Although Brand A fertilizer produced more tomatoes than Brand B, neither fertilizer should be used because plants grown without fertilizer produced the most tomatoes!

More examples of control groups:

• You observe growth . Does this mean that your spinach is really contaminated? Consider an alternate explanation for growth: the swab, the water, or the plate is contaminated with bacteria. You could use a control group to determine which explanation is true. If you wet one of the swabs and wiped on a nutrient plate, do bacteria grow?
• You don’t observe growth.  Does this mean that your spinach is really safe? Consider an alternate explanation for no growth: Salmonella isn’t able to grow on the type of nutrient you used in your plates. You could use a control group to determine which explanation is true. If you wipe a known sample of Salmonella bacteria on the plate, do bacteria grow?
• You see a reduction in disease symptoms: you might expect a reduction in disease symptoms purely because the person knows they are taking a drug so they believe should be getting better. If the group treated with the real drug does not show more a reduction in disease symptoms than the placebo group, the drug doesn’t really work. The placebo group sets a baseline against which the experimental group (treated with the drug) can be compared.
• You don’t see a reduction in disease symptoms: your drug doesn’t work. You don’t need an additional control group for comparison.
• You would want a “placebo feeder”. This would be the same type of feeder, but with no food in it. Birds might visit a feeder just because they are interested in it; an empty feeder would give a baseline level for bird visits.
• You would want a control group where you knew the enzyme would function. This would be a tube where you did not change the pH. You need this control group so you know your enzyme is working: if you didn’t see a reaction in any of the tubes with the pH adjusted, you wouldn’t know if it was because the enzyme wasn’t working at all or because the enzyme just didn’t work at any of your tested pH values.
• You would also want a control group where you knew the enzyme would not function (no enzyme added). You need the negative control group so you can ensure that there is no reaction taking place in the absence of enzyme: if the reaction proceeds without the enzyme, your results are meaningless.

Text adapted from: OpenStax , Biology. OpenStax CNX. May 27, 2016  http://cnx.org/contents/[email protected]:RD6ERYiU@5/The-Process-of-Science .

Mt Hood Community College Biology 102 Copyright © 2016 by Lisa Bartee and Christine Anderson is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Historical Perspective

How does homeostasis happen integrative physiological, systems biological, and evolutionary perspectives.

Homeostasis is a founding principle of integrative physiology. In current systems biology, however, homeostasis seems almost invisible. Is homeostasis a key goal driving body processes, or is it an emergent mechanistic fact? In this perspective piece, I propose that the integrative physiological and systems biological viewpoints about homeostasis reflect different epistemologies, different philosophies of knowledge. Integrative physiology is concept driven. It attempts to explain biological phenomena by continuous formation of theories that experimentation or observation can test. In integrative physiology, “function” refers to goals or purposes. Systems biology is data driven. It explains biological phenomena in terms of “omics”–i.e., genomics, gene expression, epigenomics, proteomics, and metabolomics–it depicts the data in computer models of complex cascades or networks, and it makes predictions from the models. In systems biology, “function” refers more to mechanisms than to goals. The integrative physiologist emphasizes homeostasis of internal variables such as P co 2 and blood pressure. The systems biologist views these emphases as teleological and unparsimonious in that the “regulated variable” (e.g., arterial P co 2 and blood pressure) and the “regulator” (e.g., the “carbistat” and “barostat”) are unobservable constructs. The integrative physiologist views systems biological explanations as not really explanations but descriptions that cannot account for phenomena we humans believe exist, although they cannot be observed directly, such as feelings and, ultimately, the conscious mind. This essay reviews the history of the two epistemologies, emphasizing autonomic neuroscience. I predict rapprochement of integrative physiology with systems biology. The resolution will avoid teleological purposiveness, transcend pure mechanism, and incorporate adaptiveness in evolution, i.e., “Darwinian medicine.”

INTRODUCTION

What happened at harvard.

All physiologists know well that Walter B. Cannon, the most prominent American physiologist of the early 20th century, coined the term homeostasis. He spent his entire scientific career at Harvard Medical School, where he was the chair of the Department of Physiology from 1906 to 1942.

The Harvard Medical School no longer has a Department of Physiology. It has a Department of Systems Biology. The website for the Department of Systems Biology at Harvard ( https://sysbio.med.harvard.edu ) describes the subject matter as follows.

“Systems biology is the study of systems of biological components, which may be molecules, cells, organisms or entire species. Living systems are dynamic and complex, and their behavior may be hard to predict from the properties of individual parts. To study them, we use quantitative measurements of the behavior of groups of interacting components, systematic measurement technologies such as genomics, bioinformatics and proteomics, and mathematical and computational models to describe and predict dynamical behavior. Systems problems are emerging as central to all areas of biology and medicine.”

This description does not mention homeostasis or the scientific tradition that Cannon began at the same institution. What happened?

Bernard’s Purpose of Bodily Processes and Cannon’s “Useful End”

In the late 1800s, Claude Bernard, widely regarded as the father of modern physiology ( 164 ), promulgated the founding statements of integrative physiology. He is well known for introducing the notion of an apparently constant inner world ( milieu intérieur ), the fluid environment that bathes body cells, thereby insulating them from vicissitudes of the external environment. Even more meaningful, he proposed a purpose for body processes. “The constancy of the internal environment is the condition for free and independent life…All the vital mechanisms, however varied they might be, always have one purpose, that of maintaining the integrity of the conditions of life within the internal environment ( 17 ).”

Cannon took up the same theme in his book The Way of an Investigator ( 28 ). He wrote, “My first article of belief is based on the observation, almost universally confirmed in present knowledge, that what happens in our bodies is directed toward a useful end.” Cannon devotes a chapter of his book Bodily Changes in Pain, Hunger, Fear and Rage , to the “Utility of Bodily Changes,” which includes the following statement:

“It has long been recognized that the most characteristic feature of reflexes is their “purposive” nature, or their utility either in preserving the welfare of the organism or in safeguarding it against injury ( 27 ).”

Note Cannon’s use of quotation marks here. “Purposive,” with the quotation marks, summarizes in a single word the essence of the challenge at the interface of systems biology with integrative physiology ( Fig. 1 ).

Concept diagrams illustrating systems biological ( left ) and integrative physiological views ( right ) about organismic responses to an environmental or internal perturbation. Objects in blue are measurable, and objects in green are conceptual. In the integrative physiology model, there is a negative feedback loop that keeps levels of the monitored variable within bounds. In the systems physiology model, because positive (solid lines) and negative relationships (dashed lines) are embedded in the network, homeostasis is an emergent phenomenon resulting from a pattern of activation (thick solid lines) and inhibition (thick dashed lines).

Bernard’s concept of the milieu intérieur and Cannon’s of homeostasis were revolutionary in the history of medical ideas, yet to a systems biologist Bernard’s assertion about the purpose of body processes and Cannon’s about the “useful end” might seem awkward or unnecessary. This is because these statements are teleological.

Teleology refers to a purpose or goal as the reason or explanation for something. Understanding the positions of integrative physiology and systems biology requires consideration of the history and meaning of teleology.

Aristotle’s telos referred to intrinsic purposes or ends; e.g., the telos of an acorn is to become an oak tree. Atomists such as Democritus and Titus Lucretius Carus opposed the notion of the driving “final cause” inherent in Aristotle’s telos. In his epic poem On the Nature of Things , Lucretius states that “nothing in the body is made in order that we may use it. What happens to exist is the cause of its use” (Book IV, lines 811–842).

Teleology has had a checkered past in science, and it remains an unsettled aspect of biology. Physics and chemistry discarded teleological explanations as these scientific disciplines matured; when an apple falls from a tree, there is no reason to conceptualize that the telos of the apple drives its motion. In biology, however, the debate continues.

It is a debate that has garnered substantial popular and media attention because of the conflict between creationism and Darwinian evolution. The philosopher Thomas Nagel, in his 2012 book Mind and Cosmos: Why the Materialist Neo-Darwinian Conception of Nature is Almost Certainly False ( 117 ), argued that evolutionary theory cannot account for existence as experienced by the human mind, and since the mind is a basic aspect of nature, evolutionary explanations must be inadequate.

Nagel’s book was rapidly adopted by creationists and condemned by evolutionary biologists. Daniel Dennett stated, “It’s cute and it’s clever and it’s not worth a damn ( 139 ).” Dennett has offered his own quite different conception of consciousness ( 118 ). He takes the extreme opposite position, that consciousness is essentially an illusion. According to his “multiple drafts” idea, the brain contains semi-independent “agencies,” and when “content fixation” occurs in any one of them, the effects are propagated such that parallel processing gives the appearance of a serial account, and that serial account is the “self” ( 44 ). The “computational theory of mind” asserts that consciousness is an emergent property from operations of a complex information-processing system ( 3 , 99 , 105 , 149 ).

A key argument against teleological explanations in physiology has to do with timing. In considering Nagel’s Mind and Cosmos , Michael Ruse wrote, “What does one mean by “teleology,” or more specifically, what is a “teleological explanation”? It is a form of explanation that makes reference to causes that can be understood only in terms of the future ( 139 ).”

As explained below, however, instinct, imprinting, and Pavlovian and operant conditioning can readily explain how neurobehavioral, autonomic, and physiological changes can and often do occur before an anticipated event. Indeed, “feedforward” adjustments are probably far more prevalent in human daily life and more efficient in maintaining homeostasis than are reactive internal reflexes.

Mayr’s Teleonomic Processes and Programs

The evolutionary biologist and philosopher Ernst Mayr (1904–2005) proposed “teleonomic” forms of end-directed processes. To Mayr, a teleonomic process or behavior is goal-directed, and the goal-directedness depends on the operation of a program ( 108 ), “coded or prearranged information that controls a process (or behavior) leading it toward a goal. The program contains not only the blueprint of the goal but also the instructions of how to use the information of the blueprint. A program is not a description of a given situation but a set of instructions persisting toward an end point under varying conditions, where the end state of the process is determined by its properties at the beginning.” Mayr emphasizes that “the goal of a teleonomic activity does not lie in the future, but is coded in the program” ( 108 ). Programs enabling organisms to make and respond to predicted events or situations could provide key survival advantages, although natural selection itself of course operates only in the present, without anticipation.

Mayr’s teleonomic processes address “why” questions. In Mayr’s words,

“To be sure, questions that begin with “What?” and “How?” are sufficient for explanation in the physical sciences. However, since 1859, no explanation in the biological sciences had been complete until a third kind of question was asked and answered: “Why?” It is the evolutionary causation and its explanation that is asked for in this question. Anyone who eliminates evolutionary “Why?” questions closes the door on a large area of biological research…“Why?” questions do not introduce a metaphysical element into the analysis, and…there is no conflict between causal and teleological analysis, provided it is precisely specified what is meant by “teleological.”

Systems biology avoids “why” questions. The essence of systems biology is description and prediction from complexity. “Function” in systems neurobiology refers to neuronal networks, processes, and behaviors that at least theoretically can be observed, not to purposes or goals that cannot be observed.

The Marvelous Mesh

The remarkable history of the rete mirabile , to which this essay will return repeatedly, demonstrates the risk of conceptualizing purposes. The second century Greek physician Galen taught that the arterial blood containing the vital spirit would be filtered by a network of blood vessels at the base of the brain, the rete mirabile , which would produce the animal spirit responsible for cognitive functions. The animal spirit would then be distributed by tubes to the body organs, enabling them to function in concert, or sympathy, with each other. This is the origin of the phrase “sympathetic nervous system.”

Galen’s concept was scientific in that it generated predictions that observation or experimentation could test ( 167 ), although testing it did take 13 centuries. He depended on dissections of farm animals such as oxen, since human autopsies were forbidden in his time. Although ruminants (and sea mammals, discussed below) possess retia mirabilia , humans do not. This fact of course vitiated Galen’s theory about the purpose of the rete mirabile .

Andreas Vesalius (1514–1564) was the first to recognize Galen’s mistake. Initially Vesalius supported Galen’s notions. For instance, in 1538 he drew and wrote in his book Tabulae Anatomicae Sex ( 163 ) about “a reticular plexus at the base of the brain, the rete mirabile …wherein the vital is elaborated into the animal spirit.” ( Fig. 2 ) ( 140 ). Five years later, however, in his De humani corporis fabrica libri septem , Vesalius wrote, “I cannot sufficiently marvel at my own stupidity; I who have so labored in my love for Galen that I have never demonstrated the human head without that of a lamb or ox, to show in the latter what I could not in the former….” ( 148 ).

The rete mirabile (“marvelous meshwork”). Left : Vesalius’s 1538 diagram, including the rete mirabile (yellow arrow). Right : the rete mirabile (rm) of a sheep. ba, Basilar artery; ci, internal carotid artery; cr, rostral cerebral artery; cm, middle cerebral artery; ma, maximillary artery; RZ, Retezuflüsse ( rete tributaries).

No one knows the purposes of the rete mirabile . The same anatomic substrate might have been the basis for evolution to serve different physiological functions in different ecological niches. For instance, a vascular countercurrent system may have evolved to cool the brain ( 150 ), especially during exercise ( 11 ). Heat transfer occurs when relatively cool venous blood from the nasopharyngeal mucosa flows into the pterygoid plexus surrounding the carotid rete mirabile at the base of the brain. Delivery of blood that is cooler than the core temperature could also result in decreased sweating in a hot environment and consequently provide a mechanism for water preservation ( 124 , 153 ). The rete mirabile dampens blood pressure oscillations at the level of the brain, and this could be important in the giraffe when the animal stoops to drink and the brain is below the level of the heart. In sea mammals, the large surface area of the rete mirabile and the damping of pressure oscillations may prevent dissolved nitrogen coming out of solution during rapid ascent to the sea surface.

These explanations reflect the approach of an integrative physiologist. The purposes of the rete mirabile in ruminants, giraffes, or sea mammals would not be the concern of a systems biologist.

From Teleology to Biocybernetics

In the early 1940s, the Mexican physician Arturo Rosenblueth became acquainted with Norbert Wiener and Julian Bigelow at the Massachusetts Institute of Technology during Rosenblueth’s stay in Cannon’s laboratory at Harvard. The three had the idea that control systems could enable machines to self-regulate based on feedback.

In an essay published in 1943, “Behavior, purpose and teleology,” Rosenblueth et al. ( 138 ) classified behavior in terms of 1 ) active or nonactive; if active then 2 ) purposeful or nonpurposeful; if purposeful then 3 ) feedback-regulated, i.e., “teleological,” or non-feedback regulated; and if teleological then 4 ) predictive (extrapolative) or nonpredictive. Teleology was not taken to imply “final causes” in the sense of Aristotle’s telos but was viewed mechanistically as synonymous with purpose controlled by feedback. Rosenblueth et al. ( 138 ) also argued that the “broad classes of behavior are the same in machines as in living organisms,” regardless of the complexity of the behavior.

This was several years before Wiener’s book appeared in which he first used the term, “cybernetics” ( 165 ). He and his colleagues were attempting to develop a theory of control systems that would be applicable to a wide variety of disciplines, including biology–“biocybernetics.” Wiener and Schade ( 166 ) distinguished two forms of biocybernetics, medical biocybernetics, and neurocybernetics, as follows.

“[N]eurocybernetics…is concerned with the pathways of action via sense-organs, neurons and effectors because of the fact that cybernetics is primarily concerned with the construction of theories and models. The symbols and hardware in neurocybernetics resemble more closely the elements of the nervous system and the sense-organs. Medical cybernetics is where homeostasis or the maintenance of the internal constant environment is the main consideration. There is no sharp distinction between these two fields.... ( 166 )”

It is not difficult to conceptualize correspondences of integrative physiology with medical cybernetics and of systems neurobiology with neurocybernetics.

Both integrative physiology and systems biology depend on explanatory models ( 137 ).

Ashby and Good Regulators

W. Ross Ashby was a founder of the field of cybernetics ( 6 ). Ashby’s “good regulator” theorem, which Conant and Ashby ( 35 ) proved mathematically in 1970, states that a good regulator models well the system it regulates. The verb “models” here means that each variable of the regulator corresponds to one and only one of the variables being regulated, like the teeth of a good key correspond well to the lock. “Good” means that the regulator is maximally efficient and simple. Conant and Ashby ( 35 ) proposed that the living brain learns to model its environment. Ashby ( 5 ) viewed the occurrence of good regulation as the product of eons during which natural selection has acted on requisite variety of control systems. Wiener agreed ( 165 ) when he wrote that both “phylogenetic learning” and “ontogenetic learning” are modes by which an animal can adjust itself to its environment.

Damasio’s Somatic Marker Hypothesis and Rejection of Mind-Body Duality

The philosopher Rene Descartes (1596–1650) followed a long religious tradition separating the mind from the body. The central tenet of Cartesian mind-body dualism is that humans possess a material body and an immaterial mind. Reality consists of the res extensa , physical matter, and the res cogitans , the conscious mind.

In his book, Descartes’ Error , Antonio Damasio takes the opposite position ( 38 ), according to which the brain and body interact complexly to generate the mind. Damasio conceptualizes that afferent information to the brain comes from sense organs conveying signals from outside the body and also comes from inside the body in the form of signals ascending in the central nervous system from brainstem neurotransmitter nuclei, muscles and joints, visceral organs, and the circulation (e.g., hormones, cytokines) ( Fig. 3 ). Stimuli reaching limbic/hypothalamic centers result not only in ascending signals to the outer cortex but also in descending signals to muscles in the face and limbs, the autonomic nervous system, brainstem neurotransmitter nuclei, and the endocrine and immune systems.

Central neural levels and pathways mediating responses to an external signal (stimulus) ( left ) and internal signals ( right ). Adapted with permission ( 38 ).

Damasio ( 38 ) draws a distinction between emotions and feelings, defining feelings as mental experiences of body states that arise from interpretation of emotions by the brain. Hypothesized “somatic markers” are the feelings associated with emotions. The experience of an emotion in the sense described originally by Schachter and Singer ( 144 ) depends on cognitions appropriate for such an emotion coupled with physiological arousal. The latter aspect is where the autonomic nervous system enters the picture. For instance, Hohmann ( 83 ) conducted structured interviews about experienced emotional feelings of patients with spinal cord transections. The patients reported decreased feelings of sexual urge, fear, and anger, presumably because of decreased afferent information ascending via the spinal cord to the brain and decreased sympathetic efferent outflows descending via the spinal cord from the brain.

Damasio’s model ( 38 ) resolves a long-standing debate in physiological psychology about the James-Lange versus Cannon-Bard theories of emotion ( 26 ). According to the James-Lange theory, emotion is the product of bodily sensations about physiological arousal reaching the brain, whereas according to the Cannon-Bard theory, emotions drive physiological changes. The somatic markers hypothesis incorporates both processes.

To Damasio ( 38 ), consciousness may be primitive “core consciousness,” as in subhuman animals, or “extended consciousness,” as in humans. Extended consciousness requires a concept of the self and memory of one’s own emotions and feelings. That is, as humans we include in our modeling the consequences of what we do or feel in terms of their effects on us. By Damasio’s definition, Hal, the mutinous computer in the science fiction book by Arthur C. Clarke, 2001: A Space Odyssey , had extended consciousness:

“He might have handled it—as most men handle their own neuroses—if he had not been faced with a crisis that challenged his very existence. He had been threatened with disconnection; he would be deprived of all his inputs, and thrown into an unimaginable state of unconsciousness ( 34 ).”

In Damasio’s recent book, The Strange Order of Things ( 40 ), he presents the view that homeostasis is a kind of force ensuring that “life is regulated within a range that is not just compatible with survival but also conducive to flourishing, to a projection of life into the future of an organism or a species.” Damasio ( 40 ) seems to be returning here to Aristotle’s telos , with homeostasis a driving factor and not merely an emergent outcome. About feelings and homeostasis he writes, “…feelings…[are] the subjective experiences of the momentary state of homeostasis within a living body…” Systems biology may be insufficient to explain the feeling of what happens, as Damasio entitled another one of his books ( 39 ).

What is Missing is Ourselves

The evolutionary biologist Richard Dawkins states near the beginning of The Blind Watchmaker that biology is “the study of complicated things that give the appearance of having been designed for a purpose” ( 42 ). As humans, we seem to be continually seeking out purposes for what we experience.

Daniel Kahneman (Nobel Prize, 2002) has described two types of thinking ( 89 ), System 1 and System 2 . System 1 thinking is fast and intuitive, uses little energy, and enables multitasking but incorporates preconceptions and biases, and therefore, it is prone to erroneous decision making. System 2 thinking is slow, analytical, unbiased, and more likely to lead to correct decision making but is slower, uses more energy, and obviates multitasking.

An example of System 1 teleological thinking is that the body contains a thermostat that has the “purpose” of regulating core temperature. System 2 thinking leads one to the conclusion that not only does the body’s thermostat not have a purpose, there is no thermostat at all. The thermostat is unnecessary for describing the complex and dynamic mechanisms determining core temperature.

An article published in Science in September 2018 reveals both teleological thinking and the human urge to see purposes in phenomena ( 158 ). A caterpillar chews on a leafy plant. Rapidly within the plant there is long-distance transmission of ionized intracellular calcium mediated by the amino acid glutamate (a prominent neurotransmitter in animals). The first sentence of the report reads, “Plants respond within minutes to stresses such as wounding with both local and system-wide reactions that prime nondamaged regions to mount defenses .” (italics added). The statement is teleological and reveals the investigators’ purpose-seeking mindset. Stressors and stress responses are externally observable, but stress is an unobserved condition or state aroused by a stressor that “drives” a stress response. Stresses as states or forces are not observable, but we believe they exist anyway. Similarly, another article published in Science in January 2019 about mechanisms of baroreceptor mechanotransduction begins, “Blood pressure (BP) is tightly regulated to ensure that the body is prepared to meet varied daily activity demands.” (italics added)

HIERARCHIES

Rather than conceptualize the presence of homeostats located in specific brain areas, it seems more appropriate to theorize that there are hierarchies of input-output relationships at several levels in the brain, with modulation of those relationships by ascending and descending signals ( Fig. 4 ). Homeostasis would then be the product of activities in these hierarchies, which are neuroanatomic and neurochemical at the same time.

The central autonomic network (CAN). Red arrow indicates neuronal input from a variety of sensors to the nucleus of the solitary tract (NTS). Magenta arrow indicates humoral input via circumventricular organs such as the area postrema (AP). A5, A5 area; AMY, amygdala; ANS, autonomic nervous system ; CING, cingulate cortex; CVLM, caudal ventrolateral medulla; DMNX, dorsal motor nucleus of the vagus nerve; HACER, hypothalamic area mediating conditioned emotional responses; Hippo, hippocampus; insula, insular cortex; LC, locus ceruleus; PAG, periaquaductal gray region; PBN, parabrachial nucleus; Pre-Bötz, pre-Bötzinger complex; PVN, paraventricular nucleus; raphe, raphe nuclei; RPG, respiratory pattern generator; RTN, retrotrapezoid nucleus; RVLM, rostral ventrolateral medulla; VTA, ventral tegmental area. The anatomic connections in the CAN are complex, but this underestimates the actual complexity because of subnuclei, chemical pathways with different neurotransmitters and cotransmitters, and receptor subtypes and locations.

Cannon recognized the key roles the brain plays both in elaborating “emergency” responses and in coordinating body systems to keep values for internal variables within physiological bounds ( 29 ). Cannon and Britton ( 30 ) reported that cerebral decortication evokes rage behavior accompanied by hyperglycemia; decorticated adrenalectomized animals exhibit the same behavior but without hyperglycemia. These findings were consistent with cortical restraint of primitive emotional behaviors and of emotion-associated adrenomedullary secretion. Cannon’s student Philip Bard used serial brain sectioning in decorticate animals to obtain evidence that physiological concomitants of primitive emotions originate in the hypothalamus ( 12 ). The phenomenon of “sham rage” in decorticate animals fits with the view that overall the cerebral cortex exerts inhibitory restraint on the hypothalamic expression of primitive emotional behaviors. On the other hand, stimulation of baroreceptor afferents inhibits sham rage ( 13 ).

In the 1920s to 1930s, W. R. Hess focused on hypothalamic regulation of parasympathetic and sympathetic outflows and their behavioral concomitants. He showed that stimulation of posterior hypothalamic sites that altered functions of internal organs via sympathetic outflows also evoked appropriate behaviors directed toward the environment (“ergotropic” effects). Stimulation of anterior sites evoked signs consistent with generalized parasympathetic activation that were also associated with characteristic behaviors (e.g., postural change associated with defecation), which Hess viewed as protective against overloading (“trophotropic”). The sympathetic-ergotropic and parasympathetic-trophotropic areas seemed to operate in a state of dynamic equilibrium ( 82 ). These findings, for which Hess received a Nobel Prize in 1949, reinforced the view that autonomic functions depend crucially on the central nervous system and that higher centers modulate autonomic outflows in coordinated neuroendocrine and behavioral patterns.

The “central autonomic network” ( Fig. 4 ) conceptualized by the neurologist Eduardo Benarroch ( 16 ) includes (in ascending order in the neuraxis) the caudal ventrolateral medulla (CVLM), nucleus of the solitary tract (NTS), dorsal motor nucleus of the vagus, the nucleus ambiguus (NA), rostral ventrolateral medulla (RVLM), midline raphe nuclei, locus ceruleus (LC), periaqueductal gray matter (PAG), parabrachial nuclear complex (PBN), paraventricular nucleus of the hypothalamus (PVN), hypothalamic area mediating conditioned emotional responses (HACER), amygdala (AMY), hippocampus (Hippo.), and insular, anterior cingulate (CING), and retro-orbital or prefrontal cortex. Tract tracing with pseudorabies virus has confirmed that five cell groups in the brain determine sympathetic outflows ( 152 ): the PVN, A5 noradrenergic cell group, raphe nuclei, ventromedial medulla, and RVLM. Other brain areas transneuronally labeled after infection of the superior cervical and stellate ganglia are the PAG and lateral hypothalamus. The retrotrapezoid nucleus (RTN), pre-Bötzinger nucleus, and respiratory pattern generator (RPG) have been added to the network, as respiration may be considered to be an autonomic function. The ventral tegmental area (VTA) has also been added because of its role in neurobehavioral phenomena such as mood and reinforcement.

One can theorize that hierarchical networks involving input-output relationships continuously orchestrate and learn adaptive patterns of observable behaviors, cognition, memory, mood, and autonomic systems. Taken together, these networks function as “good regulators” determining levels of internal variables and act as if there were homeostatic comparators–i.e., “homeostats” ( 1 , 70 , 91 ).

Regulation of different variables involve shared brainstem centers ( 90 ). For instance, a key common site of initial synapse formation for internal reflexes is the NTS, and a key common site of output to the sympathetic preganglionic neurons is the RVLM. Neurons in the RVLM mediate sympathoneural and adrenomedullary responses to a variety of stressors, such as glucoprivation and systemic hypotension. Whether RVLM neurons constitute part of a final common pathway regardless of the stressor [in line with Selye’s doctrine of nonspecificity ( 66 , 146 )] or have a degree of stressor specificity ( 129 ) remains unsettled.

Combinations of functional magnetic resonance imaging with laboratory psychological challenges in healthy volunteers and in patients with particular brain or autonomic lesions have indicated specific roles of nodes in this hierarchy in maintaining homeostasis. For instance, Craig ( 35a ) has argued that the parabrachial nucleus (PBN) is the main integration site for all homeostatic afferent activity and that the anterior insular cortex is the site for the subjective image of oneself as a sentient entity, i.e., emotional awareness. The latter seems to fit with Damasio’s somatic marker hypothesis ( 33 ).

Classically conditioned behaviors involve the limbic system and medial prefrontal cortex. Consistent with Hess’s view ( 82 ), hypothalamic activity patterns are modulated by the limbic centers, which reflect emotions, habituation, sensitization, imprinting, and classical conditioning. Expression of conditioning-related neural activity in amygdala and insula depends on both cognitions and representations of bodily states of autonomic arousal ( 36 ), such as via input from cardiac baroreceptors ( 73 ). This interpretation provides a neuroanatomic substrate for the proposal by Schachter and Singer ( 144 ) about a half century ago of cognitive and physiological determinants of emotional experience.

Executive functions and simulations of future events are mediated at the level of the outer frontal cortex. Higher cortical centers are responsible for cognition, instrumentally conditioned learned behaviors, predictions of future events, and interpretations of environmental and social stimuli.

Benarroch’s ( 16 ) central autonomic network does not incorporate coordination with movement, but both the sympathetic noradrenergic neuronal system and the sympathetic adrenergic hormonal system are hardwired to portions of the cerebral cortex responsible for motor outflows (recall the etymology of emotion, from the Latin ēmoveō , “to move”). Dum et al. ( 47 ) used transneuronal retrograde transport of a nonpathogenic form of the rabies virus to identify cortical areas that communicate through multisynaptic connections with the adrenal medulla. They identified two general cortical sources of output: a broad network of lateral cortical motor areas that are involved with movement selection, preparation, and execution and a smaller medial network in multiple cingulate cortical areas that are involved with cognition and emotion. The association between movement and autonomic outflows fits well with Cannon’s concept of “fight or flight,” according to which sympathoadrenal stimulation prepares the organism for potentially life-saving extreme physical exertion ( 29 ).

HOW HOMEOSTASIS HAPPENS

Irwin J. (“Irv”) Kopin near the end of his life proposed with me that three types of processes maintain homeostasis ( Fig. 5 ) ( 67 ). The first and most well-known is error control by negative feedback regulation, which applies to the internal reflexes described below. The second is anticipatory (“feedforward”) regulation. The third is buffering. Figure 5 shows the relationships of reflexive error control via negative feedback (red), buffering (tan), and anticipatory regulation (blue). The anticipatory control mechanisms can be instinctive (solid lines) or conditioned (dashed lines). A disturbance can arouse anticipatory instinctive responses by pathways involving awareness (conscious or unconscious), and an associated conditioned stimulus can arouse anticipatory responses by pathways involving awareness and conditioned learning. A disturbance is sensed by interoceptors (e.g., gastrointestinal hemorrhage) or exteroceptors (e.g., touching a hot iron).

Concept diagram illustrating 3 ways homeostasis happens: negative feedback regulation via internal reflexes, anticipatory regulation via conditioning or instinct, and buffering. In the model, the effectors are both autonomic and nonautonomic. Effector responses to a disturbance are determined by 3 forms of regulation. First, effector activities are determined from input-output (afferent-efferent) curves relating sensory input to effector output (error control by negative feedback). Second, via exteroceptive or interoceptive input, effector activities are altered by instinct or imprinting in advance of a change in the level of the regulated variable. Third, via exteroceptive input, effector activities are altered by associative learning (classical or instrumental conditioning), also in advance of a change in the level of the regulated variable. The extent of sensor activation in response to a disturbance is modulated by buffering. Buffering is a means of diminishing the intensity of an external disturbance, thereby reducing the required use of reflexive homeostatic mechanisms. Effector responses can also modify buffering in advance of a disturbance, via instinct or learned behaviors (e.g., piloerection during cold exposure, donning a jacket before entering a cold environment).

Internal Reflexes

Baroreflexes, chemoreflexes, glucose regulation, and temperature regulation have received extensive research attention in integrative physiology for many years. By the names of these reflexes, one can recognize their presumed purposes. The purpose of baroreflexes is homeostasis of blood pressure ( 173 ); the purpose of chemoreflexes is homeostasis of the P o 2 , P co 2 , and pH of the arterial blood; the purpose of glucose regulation is homeostasis of this vital fuel, and the purpose of temperature regulation is homeostasis of core temperature.

Barostasis.

In the early 1920s, the German physiologist Heinrich Hering demonstrated in dogs that electrical stimulation of the carotid sinus nerve (later termed Heing’s nerve) decreased heart rate and blood pressure and that cutting this nerve produced opposite cardiovascular changes. Hering ( 80 ) was the first to show that bilateral transection of the carotid sinus nerve produces hypertension.

The carotid sinus baroreceptors are distortion receptors, not pressure receptors. In fact, no biological system has been discovered in which blood pressure is sensed directly. Stimulation of the carotid sinus baroreceptors by local stretch evokes the reflexive changes in blood pressure and heart rate. All the baroreceptor afferent nerve traffic enters the central nervous system at the nucleus of the NTS in the medulla. Destruction of the NTS also produces labile hypertension ( 119 ) and excessive responses to conditioned stimuli ( 120 ).

An integrative physiologist could claim that the NTS is the site of the “barostat” determining the autonomic responses, depending on the sensed discrepancy between afferent information and a set point for responding ( 52 , 53 ). Interestingly, the current use of the term barostat refers to a device for regulating gastrointestinal tone by monitoring gut volume changes and delivering controlled distensions to maintain a set pressure ( 160 ), but the idea is essentially the same.

At hypothalamic and limbic system levels there are yet other input-output relationships for emotional, behavioral, or arousal states that impact barostatic function. Thus, stimulation of the PVN attenuates responses of neurons of the medullary NTS that are activated when phenylephrine is injected to increase blood pressure ( 46 ). Increased activity in limbic system centers such as the amygdala and hippocampus as part of classically conditioned fear alters the relationship between PVN activity and blood pressure. Both elicitation of the “defense” reaction and sciatic nerve electrical stimulation modulate the carotid sinus baroreflex ( 93 , 94 ).

Finally, at higher cortical levels there are yet other input-output relationships for executive functions, psychosocial restraint, operantly conditioned behaviors, and simulations, all of which modulate the relationship between baroreceptor afferent and autonomic efferent traffic. Exercise is well known to alter baroreflex-cardiovagal functions, although researchers have disagreed about the exact changes in baroreflex function curves ( 18 , 21 , 88 ). Carotid sinus baroreceptors continue to regulate arterial pressure and heart rate during exercise, but due to “central command” they are reset to regulate blood pressure around an exercise-induced increased setpoint ( 88 ). Baroreflex function can also be classically conditioned ( 48 ). Consistent with classical conditioning of baroreflex-cardiovagal gain, in baboons trained by operant conditioning to increase diastolic blood pressure in 12-h sessions beginning at 12 noon, baroreflex-cardiovagal gain was found to decrease in advance of the sessions and to increase before the sessions ended, without concurrent alterations in blood pressure itself ( 65 ).

The pathways and interactions are highly complex and have not yet been defined specifically in humans; overall the system operates as if there were feedforward regulation of the “barostat.” That the baroreflex has the “purpose” of regulating blood pressure and that the NTS is the barostat are teleological concepts. The barostat is a kind of metaphor for a complex neuronal network involving multiple centers at multiple levels in the neuraxis.

One can even argue that the purpose of baroreceptors is not to regulate blood pressure at all but to regulate delivery of oxygenated blood to the brain. Thus, in aquatic animals the body is surrounded by about the same pressure much of the time, and whales, dolphins, and porpoises do not have carotid sinus baroreceptors, because they do not have carotid sinuses. They have retia mirabilia .

Chemoreflexes.

“Chemoreflexes” involve sensory information about pO 2 , pCO 2 , and pH at carotid body chemoreceptors and at sensors in the brainstem ( 76 ). Afferents from the carotid bodies synapse in the NTS, which relay to the “respiratory pattern generator” (RPG, Fig. 4 ) ( 77 ).

During exercise, central command and metaboreflexes from exercising muscles stimulate ventilation to maintain arterial pCO 2 and pH; however, the neural circuits underlying central command and metaboreflexes affecting breathing remain incompletely understood ( 76 ). Consistent with the view that breathing is in part controlled by feed-forward Pavlovian conditioning, adding resistive loads to consecutive inspirations results in anticipatory increases in inspiratory motor drive ( 171 ).

No amount of description of chemoreflex pathways maintaining blood oxygen and carbon dioxide levels and pH can convey the sense of breathlessness, air hunger, or distress experienced during asphyxiation or the daytime fatigue and drowsiness attending sleep apnea.

Glucostasis.

A variety of neuroendocrine factors determine glucose levels, including glucose-regulatory hormones, insulin, glucagon, adrenaline, cortisol, and growth hormone. The fact that blood glucose concentrations are unchanged by exercise despite very high rates of glucose flux suggests a form of “integral control” ( 91 ).

If glucose levels are regulated by so many factors, why is there type 2 diabetes mellitus or “metabolic syndrome?” One concept proposes a “flip-flop” insulin/glucagon controller. In metabolic syndrome, disruption of the flip-flop mechanism by amyloid accumulation in the pancreatic islets in type 2 diabetes mellitus could lead to hyperglucagonemia, hyperinsulinemia, insulin resistance, glucose intolerance, and impaired insulin responsiveness to hyperglycemia ( 91 ). Another potential explanation is altered negative feedback inhibition exerted by pancreatic islet δ-cells on nearby insulin-secreting β-cells ( 84 ).

These explanations are far removed from the feelings of hunger or satiety or the “sweet tooth” that so importantly influence glucose ingestion by humans. Glucose is sensed in the brain ( 131 ), and glucose-sensing cells determine eating responses to glucoprivation ( 136 ). Hyperglycemia can also be classically conditioned ( 170 ) and is a well-known correlate of experienced distress ( 79 ).

An experiment done by Walter B. Cannon more than a century ago illustrates the role of gastric contractions in the feeling of hunger ( 27 , 31 ). A colleague accustomed himself to having a tube ending in a rubber balloon in his stomach so that intragastric pressure could be monitored over time by inflating the balloon and recording its pressure using a water manometer and float recorder. The subject fasted and reported when he felt hunger pangs, which were noted on the pressure recording. The report of feeling pangs consistently followed the onset of the phasic increases in pressure, in accordance with Damasio’s somatic marker hypothesis (and ironically, in conflict with the Cannon-Bard theory). Cannon and Washburn ( 31 ) concluded the following:

“Hunger…is normally the signal that the stomach is contracted for action; the unpleasantness of hunger leads to eating; eating starts gastric secretion, distends the contracted organ, initiates the movements of gastric digestion, and abolishes the sensation…The periodic activity of the alimentary canal in fasting…is…an exhibition in the digestive organs of readiness for prompt attack on the food swallowed by the hungry animal.”

Systems biology has not yet incorporated the feeling of hunger, much less the role of stomach contractions in the feeling of being hungry, in models of glucose regulation.

Thermostasis.

Even in the primitive brain of the fruit fly, there is a form of coding of temperature ( 54 , 57 ). The antennae contain hot-sensing and cold-sensing neurons that project to distinct but adjacent central glomeruli, and second-order neurons can respond to both heat and cold ( 54 ). Because the flies are not poikilothermic, they must sense and react rapidly to altered external temperature to survive. Silencing the hot- or cold-sensing neurons results in distinct deficits in avoidance locomotor responses to reach places with appropriate temperature. In the words of the first author, “The role of our senses is to create an internal representation of the physical and chemical features of the external world,” and by implication the region of these glomeruli is the site of the modeling.

Mammals have a brainstem network of neural pathways for thermoregulation ( 116 ). These pathways are associated with behaviors such as sweating, panting, saliva spreading, avoiding heat, and seeking shade in response to heat and sun exposure, shivering, piloerection, stomping, and blowing on the fists in response to cold. Some of these adjustments are instinctive and some conditioned ( 37 ).

It has been proposed that four types of effectors mediate thermoregulatory responses to cold: cutaneous vasoconstriction limiting heat loss, shivering, sympathetic noradrenergic outflow to brown adipose tissue (BAT) for thermogenesis ( 115 ), and, at least when core temperature actually declines, adrenaline ( 55 , 56 ). Cooling of the preoptic area increases sympathetic outflow to BAT and augments BAT thermogenesis ( 114 ), whereas activation of vagal afferent traffic decreases sympathetic outflow to BAT and inhibits BAT thermogenesis ( 104 ). Tupone et al. ( 159 ) described “thermoregulatory inversion,” in which BAT thermogenesis decreases in response to skin cooling. This phenomenon occurs after transection of the neuraxis rostral to the dorsomedial hypothalamus in anesthetized rats.

These concepts about temperature regulation do not consider that in humans anticipatory regulation and buffering largely determine how temperature is actually controlled; if you are planning to venture out into the cold, you put on a jacket. Even looking out the window at the frozen outdoors could arouse a variety of conditioned responses before you go outside ( 51 , 98 , 145 ). Descriptions of thermoregulatory pathways and mechanisms do not explain the feeling of being hot or cold.

Anticipatory Regulation

Patterned stress responses may be instinctive, imprinted, or learned. Instinct is a genetically determined response that is independent of adaptability. An autonomically mediated reflex at the level of the lower brainstem can be considered instinctive. Imprinting refers to an environmentally elicited, largely but not exclusively genetically determined behavior (e.g., newly hatched ducklings follow the first moving object they see; see Ref. 101 ). Pavlovian (classical) conditioning is a form of associative learning. Instrumental (operant) conditioning, which affords the greatest amount of adaptability, involves acquisition or extinction of behaviors based on their consequences (reward or positive reinforcement and punishment or negative reinforcement).

Modeling is the basis for anticipatory regulation, which is far more efficient and less prone to system failure than negative feedback regulation. Feedforward regulation occurs by instinct, imprinting, Pavlovian conditioning, instrumental conditioning, and (especially in humans) cognitive simulations. This counters one of the classic arguments against teleological explanations, that responses cannot precede the stimuli that elicit those responses.

Buffering is a means of diminishing the intensity of an external disturbance. Many mammals have fur, which creates a layer of motionless air as an insulator above the skin, whales have blubber, and birds have closely packed feathers. The barrier to heat loss can be enhanced by reflexive bristling of the hair, which is mediated by arrector pili muscle supplied by sympathetic noradrenergic nerves ( 32 ); this increases the depth of the layer of motionless air. Behavioral responses to buffer the cold include huddling, seeking shelter, hibernation, and migration. Humans can also don appropriate clothing, which is inserting a buffer. Thus, buffering can be energy dependent or energy independent. In Fig. 5 , the + or 0 refers to buffering that is dependent on or independent of effector activation.

DARWINIAN MEDICINE: HOMEOSTASIS IN EVOLUTIONARY PERSPECTIVE

Suppose you are eating when something “goes down the wrong pipe.” Immediately, you reflexively cough. The mechanisms of sensation, the spinal cord and brainstem networks and neurotransmitters, and the efferent outflows to skeletal, visceral, and cardiac muscle have been described ( 22 ). The idea of a cough reflex, however, is a teleological concept, and the designated name for the reflex indicates the physiologist’s urge to seek out purposes. In this case, the “purpose” of the behavior seems straightforward, because choking is an immediate life threat, and an open airway is mandatory for homeostasis. Thus, lack of airway protection in multiple system atrophy, a rare form of chronic autonomic failure, increases the risk of death from aspiration pneumonia or from asphyxia after vomiting ( 147 ). No amount of analysis of the purpose (a province of integrative physiology) or mechanism (a province of systems biology) of the cough reflex explains why we humans have a risk of aspirating when we eat. This requires understanding of the phylogenetic steps that led to the confluence of the respiratory and gastrointestinal tracts at the back of the human pharynx ( 22 ), the province of “Darwinian medicine” (also called evolutionary medicine).

In their book, Why We Get Sick , Randolph M. Nesse and George C. Williams ( 123 ) considered the problem of aspiration from an evolutionary perspective. In the primordial fish that were our ancestors, lungs arose as an extension of gastrointestinal tract tissue ( 23 ). Dry land offered ecological niches that favored survival of animals that could take in oxygen by gulping air. With the evolution of land creatures, lungs replaced gills as the source of oxygen. The intersection between the feeding and breathing systems that causes our tendency to aspirate and the strong survival advantage of clearing the airway by coughing are part of our evolutionary heritage. Neither integrative physiology nor systems biology takes into account the natural selective pressures that were operational at the time of the ecological opportunities and genetic variations.

A recent Delphi study enumerated several core concepts of evolutionary medicine ( 75 ). Among these, the following seem especially relevant to homeostasis.

Evolutionary Tradeoffs

Evolutionary changes in one trait that improve fitness can be linked to changes in other traits that decrease fitness. Life history traits, such as age at first reproduction, reproductive lifespan, and rate of senescence, are shaped by evolution and have implications for health and disease.

Environmental factors can shift developmental trajectories in ways that influence health, and the plasticity of these trajectories can be the product of evolved adaptive mechanisms.

Many signs and symptoms of disease (e.g., fever) are useful defenses, which can be pathological if dysregulated.

Disease risks can be altered for organisms living in environments that differ from those in which their ancestors evolved.

Internal Reflexes in Evolutionary Perspective

Let us now reconsider baroreflexes, chemoreflexes, glucostasis, and temperature regulation with a Darwinian medical perspective.

The integrative physiologist would explain sympathetically mediated hypertension ( 69 , 102 ) in terms of barostatic resetting, perhaps related to stress ( 50 , 155 , 156 , 169 ); the systems biologist would explain this form of hypertension in terms of multiple alterations of functional relationships within the central autonomic network ( 41 , 125 ). Neither approach accounts satisfactorily for why, given the multiplicity and redundancy of blood pressure regulatory mechanisms, hypertension exists at all. The Darwinian medical approach would consider survival advantages of maintaining blood flow to vital organs in response to hemorrhage, preserving cerebral blood flow during orthostasis, ingesting salt and calories whenever they become available, and even behavioral effects of high baroreceptor afferent traffic ( 49 ). All of these could have afforded survival advantages at the time new ecological niches opened up adaptive opportunities.

Integrative physiological and systems biological explanations also do not take into account the natural selective factors that were operative during the evolution of chemoreflexes. For instance, migrants to high-altitude regions in the western and eastern hemispheres encountered new ecological niches where genetic changes enhancing adaptation to hypoxic environments offered clear selective advantages. Andean high-altitude dwellers have high hemoglobin levels, which increases the delivery of oxygenated blood to body organs; however, this comes at the risk of chronic mountain sickness ( 4 ). Tibetans living at even higher altitudes do not have elevated hemoglobin levels. Numerous single-nucleotide polymorphisms have been located near the gene encoding hemoglobin production that correlates with relatively low hemoglobin levels ( 15 ). Instead, high-altitude-dwelling Tibetans have accelerated ventilatory responses to additional hypoxia, higher levels of nitric oxide (possibly increasing oxygen diffusion capacity), and more rapid blood flow to larger capillary beds in skeletal muscle ( 14 ).

Type 2 diabetes, obesity, and metabolic syndrome are well-known health burdens of modern humanity. Evolutionary pressures in operation over millions of years have resulted in humans ingesting too much of all foodstuffs. As Randolph Nesse has written ( 122 ),

“…one would think we would be designed to eat what is good for us. The system would work fine if we lived on the African savanna. In the natural environment, fat, salt, and sugar are in such short supply that when they are encountered, the useful response is to consume them. Fat provides twice as many calories per gram as carbohydrates. Sugar is often associated with ripe fruits, and seeking it out was usually beneficial. Now that we can choose our foods, we prefer what was in short supply on the African savanna.”

Bariatric surgery is the most successful known treatment for morbid obesity, and there are ancillary benefits in terms of amelioration of insulin resistance and hypertension ( 72 , 112 , 141 ). Nevertheless, even this type of treatment involves a disappointingly high recidivism rate ( 157 , 161 ). One factor influencing the likelihood of weight regain after bariatric surgery may be geographic origin; there is more weight regain in African-Americans than Caucasian-Americans ( 157 ). Differential survival advantages of adaptation to different ecological niches during human evolution might explain this phenomenon.

Regarding thermostasis, neither systems biological nor integrative physiological explanations incorporate the natural selective factors that were at play when humans migrated to regions with different temperatures, humidity, barometric pressures, and seasonal light/dark cycles. The marked expansion of the brain during human evolution posed a challenge in terms of thermoregulation within cerebral tissue ( 24 ). Although humans rarely if ever have a carotid rete mirabile , it has been hypothesized that the diversity in human craniofacial features (e.g., nostril width) in different geographical regions reflects survival advantages of mechanisms for selective brain cooling in hot environments ( 85 ). Regulation of sympathetically mediated vasoconstrictor tone in the nasopharynx could also contribute to selective brain cooling ( 8 ). In humans, the segment of internal carotid artery passing through the cavernous sinus may be too short for countercurrent heat exchange. It is much more likely that in humans brain and core temperatures are regulated together via evaporative heat loss from the naked skin ( 20 ).

Pleiotropy, Senescence, and Homeostatic Capacity

Both Bernard ( 17 ) and Cannon ( 29 ) emphasized that the capacity to maintain the constancy of the inner world of the body decreases with aging. They paid little attention to diseases and none to chronic, multisystem disorders of senescence; however, modern medicine is increasingly concerned with the management of aging-related disorders of regulation. By allowing larger fluctuations of key internal variables, there is more allostatic load and a greater likelihood of induction of positive feedback loops.

In Why We Get Sick , Nesse and Williams ( 123 ) ask, “If senescence so devastates our fitness, why hasn’t natural selection eliminated it?...Our bodies do have some capacity to repair damage and replace worn-out parts; it is just that this capacity is limited. The body can’t maintain itself indefinitely. Why not?” They answer with an application of Williams’s pleiotropic theory. Pleiotropy is a situation where one gene produces more than one phenotypic trait. In the context of senescence, the pleiotropic theory states that genes that give a benefit in youth impose a cost with age. In Williams’s words, “…senescence results from genes that increase youthful vigor at the price of vigor later on…” ( 168 ).

Williams’s pleiotropy concept may apply to disorders associated with aging. Briefly, at least some of these conditions may exist because of an evolutionary tradeoff, i.e., enhanced survival in the young reproducers at the expense of degeneration of homeostatic systems in the elderly.

Allostasis, Allostatic Load, and Catecholaminergic Neurodegeneration

The concept of allostasis incorporates alterations in the tolerated steady-state level, i.e., “stability through change” ( 151 ). For example, a respiratory viral infection can be associated with a new apparent steady state involving low-grade fever, elevated heart rate, and malaise. These allostatic adjustments keep the regulated variables at altered levels.

Although allostatic adjustments are both compensatory and adaptive, with repetition and aging they might come at a cost over time, i.e., allostatic load, corresponding to long-term “wear and tear” ( 1 ). Many examples of allostatic load in experimental animals as well as in humans involve the effects of repeated episodes or chronic stress in which brain activation of neuroendocrine functions plays a role ( 110 , 111 ). In systems biological terms, allostasis is manifested by shifts in input-output relationships. In integrative physiological terms, allostasis is manifested by adjustment of set points in reaction to and anticipation of changes in internal and external demands ( 1 ).

Multisystem diseases and disorders might reflect disturbances of regulation that lead to adverse effects of compensatory activation or of declining efficiency of homeostatic negative feedback loops. Factors such as stress, maladaptation, allostatic load, and diminished resilience may not only alter the manifestations but also determine the outcomes of acute pathophysiological phenomena such as tilt-induced neurally mediated hypotension and senescence-related, neurodegenerative disorders of catecholaminergic systems.

The autonomic nervous system operates exactly at the interface of the body and mind ( 38 ) and is a major determinant of the neurobiology of homeostasis ( 86 ). In the nascent clinical discipline of autonomic medicine ( 63 , 103 , 109 , 142 ), dysregulation of homeostatic systems figures prominently in degenerative diseases in the elderly.

A group of senescence-related disorders involve degeneration of catecholaminergic neurons that use norepinephrine or dopamine as their chemical messengers. Individuals with relatively efficient central and autonomic catecholamine systems might have had survival advantages during their reproductive years in terms of being able to increase rapidly and massively the delivery of catecholamines to their receptors as part of vigilance ( 7 ), remembering distressing events ( 19 , 43 , 58 , 132 ), initiating behaviors ( 2 , 92 ), experiencing emotions and mood states ( 59 , 144 ), learning classically conditioned responses ( 113 , 143 ), acquiring and retaining appetitive or avoidance behaviors ( 25 , 74 , 130 ), feeling pain ( 9 , 45 , 71 , 95 , 134 , 135 ), and temporarily reversing muscle fatigue ( 27 ).

The advantages associated with these adjustments, however, could have come at the cost of accelerated senescence of catecholaminergic neurons, a form of pleiotropy. The catecholamine “autotoxicity” theory ( 68 ) is based on inherent cytotoxicity of products of enzymatic or spontaneous oxidation of catecholamines in the cytoplasm of cells in which the catecholamines are produced. Most of the released catecholamine is recycled by neuronal reuptake. Stressors that evoke release of catecholamines in effect shift intraneuronal catecholamines from vesicular to cytoplasmic pools ( 64 ). Accordingly, repeated episodes of stress could cause neuronal injury via autotoxicity, and the more substantial the catecholamine release, the greater the amount of autotoxicity and the more likely the manifestations of aging-related catecholaminergic neurodegeneration. Thus, in rats, chronic restraint, which evokes activation of catecholaminergic neurons inside and outside the brain ( 96 , 97 , 126 , 127 ), reduces the numbers of substantia nigra dopaminergic and locus ceruleus noradrenergic neurons ( 154 ), as is found in Parkinson’s disease (PD) ( 172 ).

Specifically, the catecholaldehyde hypothesis states that accumulation of the toxic catecholaldehyde 3,4-dihydroxyphenylacetaldehyde (DOPAL) is central to the degenerative process in PD. When sequestered in storage vesicles catecholamines are harmless, but in the cytoplasm they can undergo spontaneous or enzymatic oxidation to form toxic reaction products ( 81 ). Most of cytoplasmic dopamine undergoes enzymatic oxidation catalyzed by monoamine oxidase (MAO) to form hydrogen peroxide and DOPAL. DOPAL is highly toxic ( 107 , 128 ) via both increased oxidative stress ( 100 ) and modifications of numerous intracellular proteins that are important for catecholamine neuronal homeostasis ( 87 , 162 ) such as α-synuclein (AS). DOPAL oligomerizes, aggregates, and forms quinone protein adducts with AS ( 87 ), and because DOPAL-induced AS oligomers impede vesicular functions ( 133 ), DOPAL-AS interactions could challenge neuronal homeostasis and eventually kill off catecholaminergic neurons, all because of pleiotropy.

What Good are Homeostats?

If homeostats are unnecessary and simplistic, what good are they? First, lumping the complex networks that make up the metaphorical regulators and conceptualizing purposes in controlling the regulated variables enable definitions of otherwise difficult ideas, such as stress. In stress, a homeostat senses a discrepancy between afferent information about the regulated variable and the set point for arousing a response. Stress is then the condition, and the error signal is the measure of the extent of stress. The integrated error signal could correspond to a kind of “memory” that would more be more efficient than the instantaneous error signal in returning the regulated variable to the set point value ( 61 ). The homeostat theory lends itself straightforwardly to computer models that define homeostatic resetting (allostasis), compensatory activation of alternative effectors, effector sharing, allostatic load, and induction of pathophysiological positive feedback loops ( 60 – 62 ).

A second reason for thinking in terms of homeostats is that it seems fruitful for bringing worthwhile conceptual questions to mind. To illustrate this, we return a last time to the rete mirabile . If the rete mirabile enabled selective brain cooling, then during exercise the temperature of the blood bathing the thermosensitive brainstem neurons should be less than the temperature of the blood in the carotid artery ( 10 , 11 ). Doubt has been raised about whether the rete mirabile actually does enable selective brain cooling ( 106 ), but suppose it does. How would the brain’s thermostat then be able to sense and correctly regulate the core temperature? And why do humans lack a rete mirabile ? In human evolution, were genetic changes fostering evaporative heat loss via the naked skin more adaptive than those fostering countercurrent temperature regulation via a rete mirabile ( 20 )? These are questions an integrative or evolutionary physiologist might ask but a systems biologist would not.

Third, the homeostat idea helps explain clinical phenomena in autonomic medicine. Consider the following case of a patient evaluated at the National Institutes of Health (NIH) Clinical Center for autonomically mediated hypertension ( 69 , 102 ). He had “complex” sleep apnea, meaning that his condition worsened rather than improved with continuous positive airway pressure (CPAP). The complex sleep apnea included Cheyne-Stokes respiration (rhythmic cycles of hyperventilation and apnea during sleep), and he had learned that in patients with heart failure CO 2 inhalation abolishes Cheyne-Stokes respiration ( 121 ). He brought with him and used during his hospitalization a modified CPAP device that administered CO 2 via the mask. He had also been diagnosed with a form of renal tubular acidosis and panic/anxiety, and for these he took large doses of sodium bicarbonate and was on a clonidine patch ( 33 ). For autonomic function testing, the clonidine was tapered and eventually stopped after ∼2 wk. The directly recorded brachial systolic blood pressure was dangerously high at ∼250 mmHg. There were very high arterial plasma levels of norepinephrine and adrenaline, and baroreflex-cardiovagal gain was near zero ( Fig. 6 ).

Baroreflex-chemoreflex interactions in a patient with self-induced hypercarbia. The patient slept each night with a positive airway pressure device into which carbon dioxide was pumped to treat Cheyne-Stokes respiration. Chemoreflex activation released the sympathetic noradrenergic system (SNS) and sympathetic adrenergic system (SAS) from barostatic restraint, blocked baroreflex-cardiovagal outflow (X), and evoked panic/anxiety, resulting in extreme hypertension and tachycardia and high arterial plasma levels of norepinephrine (NEa) and epinephrine (EPIa). BL, baseline; BP, blood pressure; HR, heart rate.

Analysis of the case was as follows. Inhaling CO 2 produces metabolic acidosis by reacting with body water to generate carbonic acid. The patient’s chemostat sensed the hypercarbia and metabolic acidosis, and this released the sympathetic noradrenergic and adrenergic systems from barostatic restraint, blocked the cardiac baroreflex, and evoked a panicky feeling. High sodium bicarbonate intake exacerbated the hypertension.

The most effective therapy for this patient’s profound dysautonomia was education ( 63 ). The patient was counseled not to inhale CO 2 via his CPAP device and to stop taking sodium bicarbonate.

Conclusions

In conclusion, just as internal models of reality in organisms have evolved, reaching the level of sophistication and complexity of our conscious minds, theories about such models seem to have evolved, from Aristotle’s telos to Galen’s rete mirabile ; from Bernard’s milieu intérieur ( 17 ) and Cannon’s “useful end” ( 28 ) to Conent and Ashby’s good regulators ( 35 ), Wiener’s neurocybernetics ( 165 ), and Mayr’s teleonomic programming ( 108 ); from Descartes’s mind-body dualism to Damasio’s ( 39 ) somatic markers hypothesis; and from concept-driven integrative physiology and data-driven systems biology to Nesse and Williams’s evolutionary medicine ( 123 ). Rapprochement of integrative physiology with systems biology will require recognition that each of us has a System 1 -thinking, intuitive, feeling mind that seeks out purposes and asks “why” questions and a System 2 -thinking, rational, unfeeling mind that does not.

Perspectives and Significance

Homeostasis is a founding principle of integrative physiology but seems almost invisible in current systems biology. Is homeostasis a key goal driving body processes, or is it an emergent mechanistic fact? I propose that the integrative physiological and systems biological viewpoints about homeostasis reflect different epistemologies, i.e., different philosophies of knowledge. Integrative physiology explains biological phenomena by theories that experimentation or observation can test. In integrative physiology, “function” refers to goals or purposes. Systems biology explains biological phenomena in terms of “omics” data and depicts those data in complex computer models. In systems biology, “function” refers to mechanisms rather than goals. This essay reviews the history of the two epistemologies especially as they pertain to autonomic neuroscience. Physiology in the future will avoid teleological purposiveness, transcend pure mechanism, and incorporate adaptiveness in evolution, i.e.,“Darwinian medicine.”

This research was supported by the Intramural Research Program of the National Institute of Neurological Disorders and Stroke.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

AUTHOR CONTRIBUTIONS

D.S.G. interpreted results of experiments; D.S.G. prepared figures; D.S.G. drafted manuscript; D.S.G. edited and revised manuscript; D.S.G. approved final version of manuscript.

ACKNOWLEDGMENTS

I acknowledge with fondness and appreciation the mentorship I received from the late Dr. Irwin J. (“Irv”) Kopin.

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Sensations and the Constancy Hypothesis

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