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5.2: What is evolutionary correlation? - Biology

5.2: What is evolutionary correlation? - Biology


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There is sometimes a bit of confusion among beginners as to what, exactly, we are doing when we carry out a comparative method, especially when testing for character correlations. In this section, I will use the particular example of correlated evolution to try to illustrate the power of comparative methods and how they differ from standard statistical approaches that do not use phylogenies.

In statistics, two variables can be correlated with one another. We might refer to this as a standard correlation. When two traits are correlated, it means that given the value of one trait – say, body size in mammals – one can predict the value of another – like home range area. Correlations can be positive (large values of x are associated with large values of y) or negative (large values of x are associated with small values of y). A surprisingly wide variety of hypotheses in biology can be tested by evaluating correlations between characters.

In comparative biology, we are often interested more specifically in evolutionary correlations. Evolutionary correlations occur when two traits tend to evolve together due to processes like mutation, genetic drift, or natural selection. If there is an evolutionary correlation between two characters, it means that we can predict the magnitude and direction of changes in one character given knowledge of evolutionary changes in another. Just like standard correlations, evolutionary correlations can be positive (increases in trait x are associated with increases in y) or negative (decreases in x are associated with increases in y).

We can now contrast standard correlations, testing the relationships between trait values across a set of species, with evolutionary correlations - where evolutionary changes in two traits are related to each other. This is a key distinction, because phylogenetic relatedness alone can lead to a relationship between two variables that are not, in fact, evolving together (Figure 5.1; also see Felsenstein 1985). In such cases, standard correlations will, correctly, tell us that one can predict the value of trait y by knowing the value of trait x, at least among extant species; but we would be misled if we tried to make any evolutionary causal inference from this pattern. In the example of Figure 5.1, we can only predict x from y because the value of trait x tells us which clade the species belongs to, which, in turn, allows reasonable prediction of y. In fact, this is a classical example of a case where correlation is not causation: the two variables are only correlated with one another because both are related to phylogeny.

If we want to test hypotheses about trait evolution, we should specifically test evolutionary correlations1. If we find a relationship among the independent contrasts for two characters, for example, then we can infer that changes in each character are related to changes in the other – an inference that is much closer to most biological hypotheses about why characters might be related. In this case, then, we can think of statistical comparative methods as focused on disentangling patterns due to phylogenetic relatedness from patterns due to traits evolving in a correlated manner.

Figure 5.1. Examples from simulations of pure birth trees (b = 1) with n = 100 species. Plotted points represent character values for extant species in each clade. In all three panels, σx2 = σy2 = 1. σxy2 varies with σxy2 = 0 (panel A), σxy2 = 0.8 (panel B), and σxy2 = −0.8 (panel C). Note the (apparent) negative correlation in panel A, which can be explained by phylogenetic relatedness of species within two clades. Only panels B and C show data with an evolutionary correlation. However, this would be difficult or impossible to conclude without using comparative methods. Image by the author, can be reused under a CC-BY-4.0 license.


Evolutionary robotics: what, why, and where to


  • 1 UMR 7222, ISIR, Sorbonne Universités, UPMC Univ Paris 06, Paris, France
  • 2 UMR 7222, CNRS, ISIR, Paris, France
  • 3 Department of Computer Science, VU University, Amsterdam, Netherlands

Evolutionary robotics applies the selection, variation, and heredity principles of natural evolution to the design of robots with embodied intelligence. It can be considered as a subfield of robotics that aims to create more robust and adaptive robots. A pivotal feature of the evolutionary approach is that it considers the whole robot at once, and enables the exploitation of robot features in a holistic manner. Evolutionary robotics can also be seen as an innovative approach to the study of evolution based on a new kind of experimentalism. The use of robots as a substrate can help to address questions that are difficult, if not impossible, to investigate through computer simulations or biological studies. In this paper, we consider the main achievements of evolutionary robotics, focusing particularly on its contributions to both engineering and biology. We briefly elaborate on methodological issues, review some of the most interesting findings, and discuss important open issues and promising avenues for future work.


1. Introduction: developmental and selective niche construction

Recent years have seen the emergence of a range of approaches that challenge some basic assumptions of the modern evolutionary synthesis. Several of these have argued that the integration of new causes and processes would amount to an extended evolutionary synthesis [1𠄵]. One of these approaches is niche construction theory (NCT), which has been heralded as the neglected process in evolution (the book's subtitle). NCT argues that organisms actively modify their own environment and thereby influence the selection pressure acting on them and their population. But the construction of an organism's selective niche is just one way in which the organisms' interaction with and construction of their environment can have potential evolutionary significance. There is another process of potentially substantial evolutionary influence: the (constructed) environment does not just select for new variation, it also produces it, in the form of the developmental niche. This difference has been highlighted by Piaget 4 decades ago when he theorized about the impact of the behaviour of organism on evolution:

But the central problem remains, for we still have to ascertain how behavior operates here, and whether it intervenes solely in selection and survival or is also a causal factor in the actual formation of morphological characteristics, as it is suggested notably by Paul A. Weiss’ conclusion that the living organism's organization and hierarchy of the subsystems have a retroactive effect … even upon the functioning of its genome, instead of being simply determined by its functioning. ([6], xi, italics added).

This paper argues for the significance of developmental niche construction (DNC) in evolution clarifying NCT as selective niche construction (SNC) will facilitate distinguishing the two processes from each other. DNC should neither be understood as a subset of NCT, e.g. the developmental production of a selective environment in either evolution or development [7,8], nor as just a cosmetic enhancement of the evolutionary synthesis, without affecting its structure or function. The most central of claims in this paper is that whereas NCT (SNC) explains the active role of the organism in its selective environment, DNC indicates the active role of the organism in its developmental environment. The constructed developmental niche captures the exogenetic (e.g. ecological and social) legacies an organism inherits alongside its genes that together ensure the—potentially modified—reconstruction of the life cycle of the next generation. 1

The relationship between SNC and DNC may best be compared with the relationship between the modern synthesis and evolutionary developmental biology. The latter provides the developmental mechanisms that connect the phenotype with the genotype, may account for the origin of variation, and highlight the effect of these variations on natural selection without directly affecting the construction of the selective niche. DNC spells out how developmental mechanisms, particularly the construction of a developmental niche, influence the origin of heritable variation and natural selection through the reproduction of a developmental system at the individual level. In other words, DNC is concerned with the origin of potentially adaptive, heritable, phenotypic variation. So while standard evolutionary theory assumes that all adaptations are the result of natural selection at the population level, this paper addresses the possibility that development can account for the creation of adaptations without invoking selection𠅊 point that has been dubbed the arrival or origin of the fittest, rather than its survival [9].

Proponents of an extended evolutionary synthesis are used to being rebuffed by comments of defenders of the status quo that the founders of the modern synthesis have been well aware of all of the phenomena and processes that are now being cited as support for the need of an extension. Where that was the case, however, that was often only to marginalize their importance, as was the case of Simpson's belittlement of Waddington's genetic assimilation as �ldwin Effect’, named after a disgraced psychologist from the turn of the century [10,11]. 2 Nobody can dispute, however, that development, particularly the developmental system comprising the organism within its developmental environment, was the most neglected process in evolution. This neglect was defended on the ground that development was entirely under the control of the genetic programme, an outcome of random mutations and natural selection. To the point that the phenotype was influenced by development and environment, it was deemed evolutionarily insignificant because only genes were regarded as having heritable effects on the fitness of an organism. The defenders of the modern synthesis would be correct to point out that it is not new that organisms shape their environment, or that the parent's phenotype influences the phenotype of their offspring, but this has rarely been stressed with any real urgency, nor has it led to a change of the standard way evolutionary theory is conceptualized in textbooks.

The theory of DNC integrates development as a contingent, constructive and emergent process of the interaction between developmental resources and the ecological context with the idea of inheritance as the transfer of essential developmental resources vital to the reconstruction of the next generation's life cycle. Such a theory needs to have a balanced account of the robustness of the organismal organization, the generation of novel variation, and its inheritance to the next generation. Arguably, its most critical component is the concept of extended inheritance that goes beyond the transmission of DNA sequences to accept the evolutionary significance of environmentally induced and developmentally regulated origin of novel variation.

The following section starts with a juxtaposition of two different niches, namely the selective and developmental niche, and highlights their differences (ੲ). Section 3 introduces the theory of DNC, traces its origin, points out its central idea of extended inheritance and how it extends to human niche construction. In ੴ, I will discuss the main distinguishing features between the two accounts of niche construction, including the divergence between my account of DNC and NCT's take on what they termed DNC as well. Section 5 shows DNC's evolutionary significance by situating it among a list of proximate causes in evolution and how DNC can be employed to answer some of the pressing questions that evolutionary theory attempts to answer. The paper closes with a conclusion and a future outlook.


Q: What are the key findings of cancer genomics studies as they relate to chromatin biology?(300 word l.

A: Genomics is a branch of biology, which deals with studying and analyzing the structure, evolution, f.

Q: hich process most likely takes place in the plant cells if it is submerged in salt water? A. Water m.

A: C-Water moves out of cell to equalize solution concentration.

Q: Discuss the Endosymbiotic Theory. Do you think it is a strong explanation on how the eukaryotes emer.

A: Endosymbiotic theory is also known as Symbiogenesis or Serial Endosymbiotic theory.

Q: Construct a mnemonics on the hierarchy of taxonomy. (example: Kindly Put Candy Out For Good Studen.

A: According to the question, we have to construct mnemonics on the hierarchy of taxonomy. First of all.

Q: Answer the number 12, 13, 14 and 15. Thank you

A: Taxonomy is the branch of science concerned with classifying the organisms in ranks or taxa based on.

Q: In the past, the evolutionary history of whales was represented by cladogram A, shown below. As you .

A: cladograms is a diagram that is constructed by cladistic principles and method.any branching line re.

Q: Under what conditions is the entropy H(X) equal to zero?

A: Gases expand on heating. Heat energy enhances the vibrational displacement of gaseous molecules. Ent.

Q: Define Modularity and Combinatorial control and give an example of both in developmental biology. Pl.

A: The Developmental biology is defined as a number of interconnected mechanisms that result in an orga.

Q: _____________ are sequences of identical DNA when it's read from the 5' to 3' direction on one stran.


Haeckel’s Fraudulent Embryo Drawings Are Still Present in Biology Textbooks — Here’s a List

Recently a colleague asked for a list of textbooks that use Ernst Haeckel’s fraudulent embryo drawings, which since the 19th century have been used to support the hypothesis of universal common ancestry. We’ve covered this many times over the years (see here, here, here, here, here, here, here, here, here, here, here, or here). Yet somehow we’ve never tried to gather into a single list many (or perhaps nearly all) known examples of textbooks in recent memory that use Haeckel’s drawings. Our colleague’s email provides an occasion for doing so.

Thus, what follows are examples of textbooks that

(1) Show embryo drawings that are either Haeckel’s originals or highly similar or near-identical versions of Haeckel’s illustrations — drawings that downplay and misrepresent the differences among early stages of vertebrate embryos

(2) Have used these drawings as evidence for current evolutionary theory and not simply to provide some kind of historical context for evolutionary thinking

(3) Have used their Haeckel-based drawings to overstate the actual similarities between early embryos, which is the key misrepresentation made by Haeckel, even if the textbooks do not completely endorse

Haeckel’s false “recapitulation” theory. They then cite these overstated similarities as still-valid evidence for common ancestry.

That having been said, here is the list:

  • Donald Prothero, Bringing Fossils to Life: An Introduction to Paleobiology (Columbia University Press, 2013).
  • Sylvia S. Mader, Jeffrey A. Isaacson, Kimberly G. Lyle-Ippolito, Andrew T. Storfer, Inquiry Into Life (13th ed., McGraw Hill, 2011).
  • Peter H. Raven, George B. Johnson, Kenneth A. Mason, Jonathan B. Losos, and Susan R. Singer, Biology (9th ed., McGraw Hill, 2011).
  • Adaptive Curriculum online curriculum submitted to Texas State Board of Education for adoption in 2011.
  • Rice University online curriculum submitted to Texas State Board of Education for adoption in 2011.
  • Sylvia S. Mader, Biology (McGraw Hill, 10th ed., 2010).
  • Sylvia S. Mader, Biology (McGraw Hill 2007).
  • BSCS Biology: A Human Approach (Kendall Hunt Publishing Company, 2006).
  • National Geographic, Alton Biggs, Lucy Daniel, Edward Ortleb, Peter Rillero, Dinah Zike, Life Science (McGraw Hill, Glencoe, 2005).

Here are some slightly older ones:

  • Donald Prothero, Bringing Fossils to Life: An Introduction to Paleobiology (McGraw-Hill, 2nd edition, 2003).
  • Joseph Raver, Biology: Patterns and Processes of Life (J.M. Lebel, 2004, draft version presented to the Texas State Board of Education for approval in 2003).
  • Cecie Starr and Ralph Taggart, Biology: The Unity and Diversity of Life (Wadsworth, 2004, draft version presented to the Texas State Board of Education in 2003).
  • Peter H. Raven and George B. Johnson, Biology (6th ed, McGraw Hill, 2002).
  • Michael Padilla et al., Focus on Life Science: California Edition (Prentice Hall, 2001).
  • Holt Science and Technology: Life Science (Holt, Rinehart and Winston, 2001).
  • Burton S. Guttman, Biology (McGraw Hill, 1999).
  • Peter H. Raven and George B. Johnson, Biology (5th ed, McGraw Hill, 1999).
  • Albert Towle, Modern Biology (Holt, Rinehart, and Winston, 1999).
  • William D. Schraer and Herbert J. Stoltze, Biology: The Study of Life (7th ed, Prentice Hall, 1999).
  • Cecie Starr and Ralph Taggart, Biology: The Unity and Diversity of Life (8th ed, Wadsworth, 1998).
  • Douglas J. Futuyma, Evolutionary Biology (3rd ed, Sinauer, 1998).
  • Kenneth R. Miller and Joseph Levine, Biology: The Living Science (Prentice Hall, 1998).
  • Kenneth R. Miller and Joseph Levine, Biology (4th ed., Prentice Hall, 1998).
  • Judith Goodenough, Robert A. Wallace, and Betty McGuire, Human Biology: Personal, Environmental, and Social Concerns, 582 (Harcourt College Publishers, 1998).
  • Donald Prothero, Bringing Fossils to Life: An Introduction to Paleobiology (McGraw-Hill, 1st edition, 1998).
  • Helene Curtis and N. Sue Barnes, Invitation to Biology (5th Ed., Worth Publishers, 1994).
  • Donald Voet and Judith G. Voet, Biochemistry (2nd ed, John Wiley & Sons, 1995).
  • Kenneth R. Miller and Joseph Levine, Biology (3rd ed., Prentice Hall, 1995).
  • Robert H. Dott and Donald R. Prothero, Evolution of the Earth (Mcgraw-Hill Education, Fifth Edition, 1994).
  • Bruce Alberts, et al., Molecular Biology of the Cell (3rd ed, Garland, 1994).
  • Joseph S. Levin and Kenneth R. Miller, Biology: Discovering Life (D.C. Heath, 2nd ed., 1994).
  • Joseph S. Levin and Kenneth R. Miller, Biology: Discovering Life (D.C. Heath, 1991).
  • Kenneth R. Miller and Joseph Levine, Biology (1st ed., Prentice Hall, 1991).
  • Scott F. Gilbert, Developmental Biology (3rd ed, Sinaeur, 1985).
  • Scott F. Gilbert, Developmental Biology (2nd ed, Sinaeur, 1988).
  • Scott F. Gilbert, Developmental Biology (1st ed, Sinaeur, 1985).

Honorable mention that’s not a textbook: Ernst Mayr, What Evolution Is (Basic Books, 2000).

There you have it. As you can see, these drawings are pervasive, continuing to misinform students as they’ve done for going on a century and a half.

And you might see a trend publication dates of the offending textbooks. There are still some very recent textbooks (i.e., 2005 or younger) that use Haeckel’s drawings, but most of the textbooks in our list predate the year 2000. Why is that? It’s because 2000 was the year that Jonathan Wells published his book Icons of Evolution which raised the public’s consciousness about inaccuracies in biology textbooks, especially the prevalence of Haeckel’s faked embryo drawings. While some textbooks continue to promote the inaccurate “icons,” Wells’s book has had a positive impact, reducing the number of textbooks that use the fraudulent drawings.

Unfortunately, as this textbook review published in 2011 makes clear, biology textbooks still have a long way to go when it comes to fixing the icons of evolution.

Image: Lithograph by J. G. Bach of Leipzig after drawings by Haeckel, from Anthropogenie published by Engelmann [Public domain], via Wikimedia Commons.


However, I do not understand. Biological evolution does cause the system (living organisms)'s entropy to decrease. So, by the second law of thermodynamics, the entropy of the universe (in this case Earth), must have overall increased.

The universe and the earth are not equatable. Earth is not an isolated system. Life causes entropy of the earth to decrease. This is offset by increased entropy of the sun, which is the primary source of energy for the earth. Overall, the entropy of the universe increases.

I would say A or D are acceptable, but A is probably the better answer: firstly, the entropy of the planet system alone is probably not increasing (indeed, it's probably near to constant) and as noted elsewhere, you need to consider a closed system to apply the Second Law, so you need to think about everything that comes to and leaves the Earth. This is the key to understanding why life on Earth doesn't violate the second law: Earth absorbs and re-radiates roughly the same amount of light, but that light's state is radically changed as this happens, and this state change and the huge entropy increase it brings about overwhelmingly offsets the decrease in Earth's organisms' entropy as they build themselve whilst making use of that light.

Let's look at the radiation balance in more detail.

The thermodynamic entropy of a system is basically the system's conditional information content (also known evocatively as equivocation in information theoretic circles) conditioned on the given macroscopic properties of a system. It's the logarithm of the number of ways a system's internal microstates can be arranged consistent with the observed outward properties. A simple explanation of the logarithm is that when you add two systems together, the number of ways that they can be arranged multiplies (think of two car number plate letters: one letter yields 26 different number plates, two letters $26^2$ number plates, three letters $26^3$ number plates, three letters and two digits yields $26^3 imes 10^2$ number plates and so forth). So the entropies add when the possibilities multiply.

The entropy of a system of identical particles is proportional to the number of particles in that system. Think of each particle as like a letter in an alphabet of its states and then think of long "number plates" of concatenated particle states.

So now: let's look at the input to the Earth: around about $1< m kg>$ of energy in the form of sunlight per second is available to Earth systems to do work. It comes as $frac$ photons per second, where $lambda = frac< u>$ is of the order of 500nm so $ uapprox600< m THz>$. That is, about $2 imes 10^<35>$ photons per second.

The energy output of the Earth is basically infrared heat: it's the same amount of energy as comes in, but it is now composed of many more photons, because now $ u$ in the photon number $frac$ is of the order of $30< m THz>$ (corresponding to $lambda=10< m mu,m>$). The Earth therefore radiates roughly twenty times more photons than it absorbs: about $4 imes 10^<36>$ photons per second. Each photon can encode the same amount of information, so the entropy increase is roughly twentyfold. Life systems on Earth use about one thousandth of the incident Solar energy: it has been estimated that photosynthesis fixes energy for use by life systems at the rate of about $100< m TW>=10^<14>< m J,s^<-1>>$, compared with an input of $1< m kg,s^<-1>>approx 10^<17>< m J,s^<-1>>$. So, no matter how complex life organisms get, this barely makes a dint on the massive entropy "production" of the total Earth energy cycle: the nett production of entropy by the whole Earth system is still strongly positive notwithstanding the presence and evolution of life, and therefore in keeping with the Second Law.

An interesting calculation has been done on how much Solar energy is needed to evolve life on Earth to the present day. This is presented in the paper:

Bunn calculates that less than a year of sunlight would be enough to power the evolution of all life on Earth over the last four billion years and still be in keeping with the Second Law.

I should cite where I first saw this idea for explaining the life versus second law problem: I took this line of argument from:


Two Options for Similarities

Two Options for Similarities

In general, organisms that share similar physical features and genomes tend to be more closely related than those that do not. Such features that overlap both morphologically (in form) and genetically are referred to as homologous structures they stem from developmental similarities that are based on evolution. For example, the bones in the wings of bats and birds have homologous structures (Figure 20.7).

Notice it is not simply a single bone, but rather a grouping of several bones arranged in a similar way. The more complex the feature, the more likely any kind of overlap is due to a common evolutionary past. Imagine two people from different countries both inventing a car with all the same parts and in exactly the same arrangement without any previous or shared knowledge. That outcome would be highly improbable. However, if two people both invented a hammer, it would be reasonable to conclude that both could have the original idea without the help of the other. The same relationship between complexity and shared evolutionary history is true for homologous structures in organisms.

Misleading Appearances

Some organisms may be very closely related, even though a minor genetic change caused a major morphological difference to make them look quite different. Similarly, unrelated organisms may be distantly related, but appear very much alike. This usually happens because both organisms were in common adaptations that evolved within similar environmental conditions. When similar characteristics occur because of environmental constraints and not due to a close evolutionary relationship, it is called an analogy or homoplasy. For example, insects use wings to fly like bats and birds, but the wing structure and embryonic origin is completely different. These are called analogous structures (Figure 20.8).

Similar traits can be either homologous or analogous. Homologous structures share a similar embryonic origin analogous organs have a similar function. For example, the bones in the front flipper of a whale are homologous to the bones in the human arm. These structures are not analogous. The wings of a butterfly and the wings of a bird are analogous but not homologous. Some structures are both analogous and homologous: the wings of a bird and the wings of a bat are both homologous and analogous. Scientists must determine which type of similarity a feature exhibits to decipher the phylogeny of the organisms being studied.

Link to Learning

This website has several examples to show how appearances can be misleading in understanding the phylogenetic relationships of organisms.

  1. Their mitochondrial DNA resembles that of other eukaryotes.
  2. The chloroplasts of eukaryotes contain a double cell layer.
  3. All eukaryotic genes are identical to either Archaea or Bacteria.
  4. Some eukaryotic genes resemble those of Archaea, while some resemble those of Bacteria and some are unlike the genes of either domain.

Molecular Comparisons

With the advancement of DNA technology, the area of molecular systematics, which describes the use of information on the molecular level including DNA analysis, has blossomed. New computer programs not only confirm many earlier classified organisms, but also uncover previously made errors. As with physical characteristics, even the DNA sequence can be tricky to read in some cases. For some situations, two very closely related organisms can appear unrelated if a mutation occurred that caused a shift in the genetic code. An insertion or deletion mutation would move each nucleotide base over one place, causing two similar codes to appear unrelated.

Sometimes two segments of DNA code in distantly related organisms randomly share a high percentage of bases in the same locations, causing these organisms to appear closely related when they are not. For both of these situations, computer technologies have been developed to help identify the actual relationships, and, ultimately, the coupled use of both morphologic and molecular information is more effective in determining phylogeny.

Evolution Connection

Why Does Phylogeny Matter?

Evolutionary biologists could list many reasons why understanding phylogeny is important to everyday life in human society. For botanists, phylogeny acts as a guide to discovering new plants that can be used to benefit people. Think of all the ways humans use plants—food, medicine, and clothing—are a few examples. If a plant contains a compound that is effective in treating diseases, scientists might want to examine all of the relatives of that plant for other useful drugs.

A research team in China identified a segment of DNA thought to be common to some medicinal plants in the family Fabaceae (the legume family) and worked to identify which species had this segment (Figure 20.9). After testing plant species in this family, the team found a DNA marker (a known location on a chromosome that enabled them to identify the species) present. Then, using the DNA to uncover phylogenetic relationships, the team could identify whether a newly discovered plant was in this family and assess its potential medicinal properties.

The following shows a hypothetical model of the evolution of the cell membrane of gram-negative bacteria, which has a double membrane. If this hypothesis is true, what does it suggest about the evolution of mitochondria and chloroplasts in eukaryotic cells and why?


5.2: What is evolutionary correlation? - Biology


Humans are animals. If we want to understand ourselves, we should understand them.
Active embodiment and "scenes", or object-and-action schemas are the keys to this.


Plan for this Lecture
arguments for the identity of human and animal minds
what evolutionary explanations tell about both
animal intentionality is the world of actions
active embodiment is the source of mental content
coming to a general picture of cognition


1. Arguments for the Identity of Human and Animal Mind

1.1. The Biological Function of Intelligence and its Characterization
In general:
Sensorimotor adaptation - not necessarily only in the sense of optimality.
More closely:
Successful coordination of biological actions.
"Coordination" is a tricky word here - it anticipates our later conclusion that much of high-level intelligence is based on low-level one.
For instance, having preexsiting primitive actions is important the rest is just to put them together, to "co-ordinate" them, in a minimalist sense.

In detail: 1.2. and 1.3.

1.2. Human Behavior Serves Biological Function
Explanatory illustration: an "anecdote" taken from a famous book.
Emmanuele Le Roy Ladurie: "Montaillou" (New York: Vintage, 1979)

Peasants in 13. century France spent much of their time just scratching - as baboons do ("grooming").
(in fact not anecdote a revealing story from microhistory and its coupling to a known ethological fact about primates)

About "Montaillou"
"This book tells the story of the 14th century villagers and peasants of Montaillou. It chronicles their everyday lives,
its importance is far greater than the story of the Cathar heresy, it is the story of the kind of people that are usually
forgotten to history."

"love and marriage, gestures and emotions, conversations and gossip, clans and factions, crime and violence, concepts of time
and space, attitudes to the past, animals, magic and folklore, death and beliefs about the other world."

1.3. What the Mind of an Organism is for
"Elephants Don't Play Chess" (R. Brooks). Nor did humans when they acquired minds.

Sensory input occurs in the biological context:
in meaningful situations of complex nature
not raw input but based on co-existence and mutual interaction
Remember the notion of language game - this is also a "game"
Perhaps the least unit is a meaningful complex of actions
We will resolve the holistic "situation" of this "game" into composites: origin of mechanisms, origin of logic, origin of narratives.

1.4. Same Function - Same Structure
Animal and human minds serve the same biological functions and operate among the same essential circumstances.
Is it the same mind? We have to look into evolution to see what kind of mind it is (and this will supply more details
on why to believe in the identity).


2. What Evolutionary Explanations Tell About the Mind

2.1. The Nature of Evolutionary Explanation
Search for the distal causes of behavior control
Note Mayr's distinction between proximal (= immediate, close) and distal (= past, remote) causes in evolution
Example for distal cause is adaptation.
A well-known application of this principle is sociobiology and evolutionary psychology
They have an exaggerated focus on adaptation.

(2.2. The Positive Message from all Evolutionary Explanations

Psychological properies are biological properties, which have a history.
History can explain the biological basis and function of cognition.
. This picture of the mind is usually completely ignored (not even denied!) in mainstream knowledge & mind theory.)

2.3. What is Evolution if not Adaptation

Adaptation is an extremely (!) important but NOT a universal evolutionary feature.
Three universal evolutionary features: unity, embodiment, entrenchment.
Unity, def: All animals are similar in all traits, the closer relatives the better. A consequence of evolutionary continuity.
Embodiment, def.: The input of the mind is what the body produces when interacting with the environment
Entrenchment, def.: Old systems are "buried" under new ones, and impossible to change.



2.4. Unity (works both ways)
Species exist in philogenetic continuity.
Therefore,
(1) Most human traits must exist in animals.
(2) Most animal traits must exist in man.

Both are big conceptual steps forward - (1) the lifting of animals to humans (2) the lowering of man to animals.
There is a temptation to believe that typical human cognitive abilities are very high-level and specific to us,
and to believe at the same time, that animal abilities are low level ones.
Unity shows that it cannot be that way.
This is a very simple point which is overlooked so often.
Of course this point could be elaborated in detail.

The argument from unity assumes gradualism of all traits, for instance.
In a more elaborate treatment we should consider philogenetic mechanisms etc., but that does not change much.

2.5. Entrenchment
I.e. The burying of old systems
The metaphor: layers of sand and soil and debris. as in archeology (W. Wimsatt)
Schank, J. C., and W. C. Wimsatt, 1988. Generative entrenchment and evolution.
In: A. Fine and P. K. Machamer, eds., PSA-1986, vol. 2. East Lansing: The Philosophy of Science Association, pp. 33-60.

What is buried away cannot change later, bacause it is no more accessible for change.
Less metaphorically: all mutationsare devastating because of avalanche-like cumulative effects.

If we find an old system in humans, it must be exactly (!) the same in even distant animals.
(Of course here we use the fact that historical distance implies present-day distance).

Much of the human cognitive systems is old.
Therefore, the human system must be identical with the old animal systems which we
find in distant species today.
There is an important side issue here. The concept of entrenchment is a developmental
one, and it does not directly apply to immediate gene products. So the well-known
examples for "molecular clocks in evolution" (
changes at a steady rate) do not contradict the argument.

How do we know that human cognitive systems are old?
There are various justifications of this notion. Such as:
Examples from complex physiological adaptations. E.g. sneezing.
Sneezing is an old system found in many mammals.
It always produces the same typical pattern: eyes shut, hight muscle tone, etc.
From entrenchment we understand that one feature cannot be changed later.
Therefore, speezing must also produce the same chilly feeling in dogs and cats etc.
This is not the best example for cognition (sneezing is not very smart eh?) but it is vey compellig.
A better (but less dramatic) example is social facilitation in dogs.
Dogs can spontaneously learn pointing and eye-following.
This has a twis, as dogs are human products selected for human-like features.
Why is the story relevant, then?
(1) But they remained very distant relatives to humans.
If they - with some evolutrionary modifications - can learn these, then the same basic system
of interpretation is there in the mind since the common ancestors of dog and man.
(2) Knock-down argument: but dogs are still animals, arent't they?
So probably other animals are capable of the same, with a little breeding help from man.

Another (and, again, more dramatic) example will be discussed with in relationship to "agency".


2.6. Evolutionary Embodiment

The basis:
Genotype - phenotype (replicator - interactor). Every evolutionary interaction is based on the phenotpye.
The phenotype defines boundary conditions of interaction (in the air, in the water, on the soil etc).
There is no "environment" in the abstract sense - note that historically the word "Umwelt" (von Uexkull) reflects this.
The crosspoint of environment and mind (or gene) is the actually existing body.
Mental content cannot be (is not) a "representation" of environment as something completely idependent
mental content consists of utilization recipes for the available joint features of the enviroment and the body.

It is the body where meaning resides, if seen on the evolutionary context.
Language and other concepts "plug in" into ready-made meanings available from philogenetically earlier times.
Such meaning must be non-verbal, non-conceptual, at least backwards from some point of evolutionary history
(Much research is done these days on embodiment and "non-conceptual content" etc. so part
of this is fairly standard material - up to the point where I will speak of active embodiment)

NCC:
Definitions vary, but all writers agree on this: a mental state's content is non-conceptual if an organism can be in
that state without having to possess the concepts used in canonically characterizing that state's content.


"What is Non-Conceptual Content?
" http://www.cogs.susx.ac.uk/users/ronc/whatis-ncc.html
A bibliography of non-conceptual content http://www.cogs.susx.ac.uk/users/ronc/ncc-bibliography.htm

More about embodiment comes below.


3. Inside the Animal Mind
the strongest pillars are these: inborn intentionality intentionality is coupled to agency animal agency detection

3.1. Intentionality
Intentionality, definition: beliefs, desires etc and their attribution
(one of the evergreens of cognitive science, http://plato.stanford.edu/entries/consciousness-intentionality/
http://www.u.arizona.edu/

There is a vast amount of evidence about the existence of animal intentionality, with details unclear.
consciousness tests http://plato.stanford.edu/entries/consciousness-animal/ http://www.u.arizona.edu/

chalmers/biblio/6.html#6.4c
imitation (social facilitation), viewpoint taking (e.g. triangulation), animal lies (misguiding behavior)
naming (categorization plus "rigid designator") and more.
- adaptation, teleology, D.C. Dennett: The Intentional Stance (1987), Cambridge MA, MIT Press http://cognet.mit.edu/MITECS/Entry/dennett
- a comprehesive treatment of the full range: Allan and Beckoff: Species of Mind full text

Important, however:
Intentionality's roots are inborn in humans (Meltzoff Gergely and Watson)
Meltzoff early imitation experiments showed that already newborns have "social" mental competence
description of the Meltzoff experiments.
Gergely and Watson show that 3 months old infants have firm expectations about actions
description of the Gergely and Watson experiments

The apparent basis of early intentionality of the latter type is the use of built-in contingency detectors (Gergely):
stimuli with high contingency (high reliablity) are preferred in age 0-3 month
low contingency (random) is ignored both then and later
interest switches to medium contingency at 3 months
medium contingency is typical for "agents" (actors) with voluntary actions (autonomy)

To sum up: intentionality reduces to agency and has a clear evolutionary origin ("inborn" means this).
A remark. In developmental psychology a conceptually more refined picture is used.
The relationship between (early) intentionality and agency is a matter of ongoing debate.
The typical view is that intentionality is part of the "self" concept and agency also, but the
two are not directly related (there is an indirect relation throught the self).


3.2. Agency, definition
Agents: actors, pro-active entities, have the ability to initiate action
agents are movers (cf. what moves what).


3.3. Agency detection by animals
This is a hard problem, since movers and the moved parts are highly correlated
Perhaps contingency is a cue, again.
The theoretical explanation for agency detection is not known yet.
The empirical facts, however, are well-studied - in dogs and cats, for instance.
Dogs are able to launch tennis ball by their nose upon observing humans doing it by hand (pictures pending)
Cats open doors by jumping up and pulling the door-handle, upon observing humans turning it (picture pending)


3.4. Objects and Actions in Animal Life
It appears that the basic elements of the animal mental world are objects and actions.
Is this perhaps a self-evident truth? What else could they be?
In actuality, it is far from being a safe and/or simple statement, but it can be risked.
Objects - it is generally assummed / accepted that animals (i.e. higher animals) partition their environment into objects
(for instance, every study on animal intentionality and agency uses this as an unproblematic fact).
Actions - we just discussed that actions are performed by agents, and agents are not easy to define or identify.

In fact both finding objects and findig the agents in the environment are difficult problems for robotics,
even if it is assumed
that these are important.

Objects are special integrative wholes, and actions similarly so.
Instead of dealing with their sensory and neural backgrounds, we will focus on these questions:

what properties of the environment make object - and - action thinking possible?
how does object - and - action thinking work?
what consequences are there for the structure of the animal and human mind?


3.5. The 'Scene' Theory: Dynamic Situations
In particular, we can ask about the relationship between festures of the structure of environment and features of mind.
The remarks here will be rudimentary and hypothetical.
I put forward a scene theory which combines many elements that are already at hand.

Adult animals (and humans) organize their perceptual fields into scenes (plots, scripts, situations, sessions. )

Scene = an interactive bevavior complex, which involves a goal (or a main motif), some actor(s) and actions, objects, and
spatial as well as temporal elements.

A scene is the least contiguous unit for mental activity (---- > this will be captured in the notion of mental models)

Evidence for the "scene" theory
Tolman's expectation theory (animals anticipate sensory input as part of a larger unit)
Thorndike's ecological selection theory (animals select relevant signals in the environment on an ecological basis not anything is a signal)
The "verb" theory of dog behavior (dogs - and other animals - take words not as names but as 'verbs' relative to behavior complexes)


4. Embodiment and Active Embodiment
Embodiment is one of the big stories of the nineties:
How the mind uses the body, and how the body is reflected in the mind.

Wheeler, M. (1997): Cognition's Coming Home: The Reunion of Life and Mind, in:
P. Husbands és I. Harvey szerk.: Fourth European Conference on Artificial Life,
MIT Press, Cambridge, MA.
ftp://ftp.cogs.susx.ac.uk/pub/ecal97/online/F035.ps.gz

Sometimes there is a spiritual or existentialist turn here:
http://www.bodywisdom.org/pages/embod.html


4.1. The Robotics Version
Intelligence without representation - simple agents perform directional operations using their body
No description, no "mind" (in the classical sense), only action control ("Cambrian intelligence").

http://www.ai.mit.edu/people/brooks/publications.shtml
Brooks, R. A., (1986), Achieving artificial intelligence through building robots, MIT A. I. Memo No. 899.
Brooks, R. A., (1991a), New approaches to robotics, Science, 253 , 1227�.
Brooks, R. A., (1991b), Intelligence without reason. Proceedings of IJCAI󈟇, 569�,
Brooks, R. A. és Stein, L. A. (1993), „Building brains for bodies”, MIT A. I. Memo No. 1439.
Brooks, R.A., Breazeal, C., Irie, R., Kemp, C.C., Marjanovic, M., Scassellati, B.,Williamson, M. (1998): Alternate Essences of Intelligence, Proc. AAAI-98
Brooks, R.A, Breazeal, C., Marjanovic, M., Scassellati, M., Williamson, M. (1998): The Cog Project: Building a Humanoid Robot, in: (C. Nehaniv, C. szerk.)
Computation for Metaphors, Analogy and Agents, Vol. 1562, Springer Lecture Notes in Artificial Intelligence, Springer-Verlag, New York.

4.2. Beyond Robotics: Coordination Revisited
Brooks et al: embodiment reduces to "action selection".
From the persepctive of cognitive ethology of higher animals, this is not cognition itself but the 'raw material' for that.
Cognition is coordination, but it is not jut reactive.


4.3. The Meaning of Embodiment for Cognition
A bodily basis for the mental.

Example: The Theory of Metaphor (Lakoff and Johnson)
Lakoff, G. (1987): Women, Fire, and Dangerous Things, Chicago UP, Chicago, IL.
Lakoff, G. és Johnson, M. (1980): Metaphors We Live By, Chicago UP, Chicago, IL.
Johnson, M. (1987): The Body in the Mind, Chicago UP, Chicago, IL.
Annotated Bibliography of Metaphor and Cognitive science
Center for the Cognitive Science of Metaphor, Online

The basic idea: a small number of fundamental image schemas (Gestalt) bear all meaning in language ----> all meaning in the mind

Container
Balance
Full-Empty
Iteration
Compulsion
Blockage
Counterforce
Process
Surface
Restraint Removal
Enablement
Attraction
Matching
Part-Whole
Mass-Count
Path
Link
Collection
Contact
Center-Periphery
Cycle
Splitting
Merging
Object
Scale
Table: The partial list of image schemata from Johnson (1987)

Metaphor = figurative use of basic schemas (e.g. into, in, prevent, etc etc)
George Lakoff: The Contemporary Theory of Metaphor, full text here


4.4. Active Embodiment
The situated/embodied picture is based on sensorimotor notions, yet sometimes the resulting image of the mind is passive.
For instance, a focus on experience puts the mind in the position of a static observer things happen to the owner of this mind.

Most image schemas of Johnson are just descriptive, even if (as in the case of force) this is not obvious at the first glance.
Perhaps garden-path and a few others are exceptions. But even these are more contemplative trhan not.
The fundamental schemas of action, such as an if. then schema are completely missing.

Work in cognitive developmental psychology on the origin of embodied concepts somewhat changes this situation.
Thelen et al characterize "force embodiment" as a concept resulting from cycles of repeated proactivity and related experience.
The "scene" theory extends this into a general framework of active embodiment, where perception-action cycles organized
into meaningful units play the fundamental role.


5. Coming to a General Picture

5.1. The Strategy from Here

Knowledge of Animals
Thinking without words a model of the animal mind
A pre-wired world, consisting objects and organisms, as source of animal knowledge.
Body skills and their use in cognition.

The Everyday World
No knowledge without prior knowledge
Everyday realism, folk ontology, folk psychology.
The reliable world as human and animal legacy
Mechanisms as simplified causal schemes.

Rationality in Action
Rationality's basic form: goal-consistent action plan.
Abstract knowledge as based on action: the active mind.

5.2. Relationship with Human Knowledge Backflash to Lecture Two.

Wittgenstein is so special not because no one can argue against it.
(In fact many philosophers, including Kripke, Putnam, and the new analitics do that.)
That is just part of the usual game of philosophy.

But Wittgenstein's significance is different: it's the only philosophical picture of the mind
that is compatible with philogenetic continuity: unity, embodiment and entrenchment.

Remark:
Here it will be important to ask how certain our knowledge about evolution is.
That could be the topic of another lecture.
The answer, in short, is this: very certain - in fact as certain as you can get.


5.3. Summary: the "Discovery" of Animals

Cognitive science is now in the process of (re)discovering animals.
But: Native Americans don't like to hear that they were "discovered" by Columbus. and if animals had abstract
concepts, they would probably raise the same objection.
Animals were always in front of our eyes.
More than just that, humans have always lived together with animals - the dog is about as old as man.
Breeders and keepers have treated animals in everyday interaction and accummulated evidence on animal minds.

(cf. The meaning of history: compare selected writers to what large numbers of people think/know in a period.
Microsociology/microhistory - another revolution in the making. )

Motto:

Darwin grounded evolution theory on what every breeder knew but no philosopher did.
Cognitive science can ground the theory of mind in the same way.


Abstract

Natural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored, despite its critical role. However, in recent years, stochastic computational methods have become commonplace in science. They are able to appropriately account for heterogeneity indeed, they are based around the premise that systems inherently contain at least one source of heterogeneity (namely, intrinsic heterogeneity).

In this mini-review, we give a brief introduction to theoretical modelling and simulation in systems biology and discuss the three different sources of heterogeneity in natural systems. Our main topic is an overview of stochastic simulation methods in systems biology.

There are many different types of stochastic methods. We focus on one group that has become especially popular in systems biology, biochemistry, chemistry and physics. These discrete-state stochastic methods do not follow individuals over time rather they track only total populations. They also assume that the volume of interest is spatially homogeneous. We give an overview of these methods, with a discussion of the advantages and disadvantages of each, and suggest when each is more appropriate to use. We also include references to software implementations of them, so that beginners can quickly start using stochastic methods for practical problems of interest.


14.4 Calculating the Within and Between Group Covariance Matrices

The lda() function conveniently carries out the key steps of a canonical variates analysis for you. However, what if we wanted some of the intermediate matrices relevant to the analysis such as the within- and between group covariances matrices? The code below shows you how to calculate these:

14.4.1 Recapitulating the CVA analysis of lda()

If we wanted to recapitulate the calculations that the lda() function carries out, we can do so based on the within- and between-group covariance matrices we estimated in the previous code block:

Let’s plot the set of CVA scores that we calculated “by hand” to visually confirm our analysis produced similar results to the lda() function:

Note that the CVA ordination above is “flipped” left-right relative to our earlier CVA figures. Canonical variates, like principal components, are identical with respect to reflection.


Watch the video: mitosis 3d animation Phases of mitosiscell division (October 2022).