## 1. Evolution, Development, and Culture



Research in comparative cognition takes place against a backdrop of
evolutionary, developmental, and socioecological considerations.
Natural selection leads to the convergence and divergence of cognitive
traits over evolutionary time. Cognitive traits might converge because
two species face similar socioecological problems, such as navigating
a similar foraging environment. Alternatively, two species might share
cognitive abilities because they have inherited them from a shared
ancestor or have developmental modules in common. Finally, cognitive
abilities interact with evolution in ways that not only alter a
species trajectory through phenotypic space, but their evolvability
(Brown 2013). Considerations such as these help researchers predict
and explain cognitive and behavioral abilities across taxa:



> 
> 
> Only by investigating proximate (mechanism and ontogeny) and ultimate
> (phylogeny and function) causes can we fully understand the nature and
> origin of behavior. (Krupenye & Call 2019: 16)
> 
> 
> 



This section presents several key ways in which evolutionary,
developmental, and socioecological considerations shape methods in
comparative cognition.


### 1.1 Convergent Evolution



The evolution of wings is a paradigm case of convergent evolution.
Wings have the function of sustaining flight and have evolved in many
different organisms, including birds, bats, and insects. The
structures comprising wings differ across these groups--bat wings
consist of extended digits, bird wings evolved from an extended
forelimb, and insect wings are outgrowths of exoskeleton. Although
wings in these groups differ in their underlying structures, they
share features like rigidity, which allow them to perform the same
function--producing lift and enabling flight. Similarities in
traits such as these can be explained in part by appealing to shared
selection pressures.



Adaptive considerations such at these guide theorizing in comparative
cognition. In this case, a "psychological trait is treated as a
design problem" (Ereshefsky 2007: 670). One determines the
function that a psychological trait serves and asks what selection
pressures might have led to that evolutionary outcome. For example,
research on western scrub jays suggests that they have episodic-like
memory or the ability to remember the "what, where, and
when" features of specific events (Clayton et al. 2001). Scrub
jays are "scatter-hoarders", hiding food in multiple
places for later recovery. Caching behavior such as this enables
individuals to survive in environments with fluctuating food supplies.
When recovering their caches, scrub jays remember where and when they
have cached a specific food item. For example, they avoid searching
for perishable food (like a worm) if its window of freshness has
elapsed (Clayton & Dickinson 1998). Recent work on cuttlefish
suggests that they face similar foraging challenges to
scatter-hoarding birds. They live in environments with variable food
supplies and appear to keep track of what they have eaten, as well as
where and when they ate (Jozet-Alves, Bertin, & Clayton 2013;
Schnell, Clayton, et al. 2021a). Cuttlefish are cephalopod mollusks
and diverged from vertebrates like scrub jays over 550 million years
ago
 (Figure 1).
 Given this phylogenetic distance, if cuttlefish and western scrub
jays share the capacity for episodic-like memory, this cognitive trait
may have evolved independently in these two taxa as a common solution
to similar ecological problems.



![a phylogenetic tree: link to extended description below](fig-01.jpg)

Figure 1. Distantly related organisms
may share similar cognitive abilities because they face similar
socioecological selection pressures over evolutionary time. Image
adapted from Schnell, Amodio, Boeckle, and Clayton (2021b: 4). [An
 extended description of figure 1
 is in the supplement.]




Alexandra Schnell and colleagues argue that studying phylogenetically
distant species like chimpanzees, corvids, and cephalopods, allows
researchers to better understand the selection pressures that give
rise to "complex cognition" like episodic-like memory,
future planning, causal reasoning, and imagination (Schnell et al.
2021b; see also van Horik et al. 2012, Powell et al. 2017, Amodio et
al. 2019). For example, the Social Intelligence Hypothesis holds that
complex cognition is a product of selection pressures that arise from
living in complex social groups (see Byrne & Whiten 1988).
However, many cephalopods live short and solitary lives. If these
cephalopods do have cognitive capacities such as causal reasoning,
these may have evolved in response to selective pressures emerging
from something other than complex social environments. Focusing on
socially complex vertebrates like chimpanzees and corvids alone makes
it "difficult to uncouple the effects of ecological and social
pressures on cognitive evolution" (Schnell et al. 2021a: 172).
If it can be shown that organisms such as corvids and cephalopods
(e.g., the octopus) have the same cognitive capacities (e.g., causal
reasoning), then researchers might be better placed to infer the
environmental demands that led to the emergence of adaptations in
these distantly related taxa.


### 1.2 Homology and Development



There are several approaches to determining what counts as a
homologous trait. The "phylogenetic approach" holds that
two traits are homologous when they are derived from a common ancestor
(Brigandt 2007). Humans and chimpanzees have similar
physical-cognitive abilities like tool use (Herrmann et al. 2007).
Under the phylogenetic approach, these abilities are homologous
insofar as they were inherited from the evolutionary ancestor that
humans and chimpanzees shared 6-8 million years ago. The
"developmental approach" to homology holds that two traits
are homologous when they're produced by the same developmental
module (Ereshefsky 2012). Mammalian vertebrae, arthropod limbs, and
bird feathers are often viewed as homologues in this sense. Some hold
that homology is best understood as having both phylogenetic and
developmental components (Ereshefsky 2007, 2012). Under this view, a
developmental module imposes proximate constraints on how a homologous
trait is constructed, while evolution explains why that developmental
module is found across taxa (namely, through common descent).



Some philosophers argue that psychologists should focus on identifying
homologous rather than convergent traits, given their interest in the
mechanisms responsible for behavior (see Matthen 2007; Clark 2010).
For example, Paul Griffiths (1997, 2007a, 2007b) argues that
identifying cognitive traits as convergent is epistemically more
demanding than identifying cognitive traits as homologous.
Explanations that appeal to convergent evolution often adopt a
lock-and-key model of adaptation where a trait is viewed as a solution
to an existing socioecological problem (e.g., episodic-like memory as
a solution to the foraging problem that western scrub jays face
today). However, Griffiths argues that it is unlikely that minds
evolved in response to contemporary socioecological problems. Instead,
the structure and organization of an organism's phenotype and
niche coevolve. Moreover, what counts as a "problem"
depends on many factors, such as resource constraints and the
developmental plasticity of an organism. Griffiths writes:



> 
> 
> Problems whose solutions cannot be developmentally dissociated must be
> solved as a single problem and so are not separate problems from the
> standpoint of adaptive evolution. (2007b: 203)
> 
> 
> 



To provide a compelling case of convergent evolution, one must take
such constraints under account. In the case of complex cognitive
traits, researchers often know little about the resource and
developmental constraints at play in an organism's evolutionary
history. In this way, identifying cognitive traits as convergent may
be epistemically demanding.


### 1.3 Culture



In addition to evolutionary and developmental factors, comparative
cognition researchers aim to understand how cultural factors affect
cognitive capacities. Philosophers and scientists have also argued
that understanding animal cultures has important implications for
conservation efforts (Brakes et al. 2019) and animal welfare
(Fitzpatrick & Andrews 2022). Broadly, culture can be defined
as



> 
> 
> information transmitted between individuals or groups, where this
> information flows through and brings about the reproduction of, and a
> lasting change in, the behavioral trait. (Ramsey 2017: 348; see also
> Ramsey 2013; Andrews 2015 [2020]: chapter 8; and entry on
>  cultural evolution)
> 
> 
> 



For example, the skills required to use a tool effectively might be
transmitted through social learning (such as copying), creating a
lasting change in a recipient's subsequent behavioral abilities
and reproductive fitness. Cultural transmission has been described as
a form of "soft inheritance" or the idea that organisms
can inherit phenotypic variation that is the result of non-genetic
effects (Mayr 1982). Eva Jablonka and colleagues argue that many
animal traditions are transmitted in this way (Avital & Jablonka
2000; Jablonka & Lamb 2008). For example, the behavioral
innovation of opening milk bottles spread quickly through Eurasian
blue tit populations in the early twentieth century. Studies suggest
that this innovation spread through a mixture of local enhancement
(naive birds learning that milk bottles are a source of food by
spending time near birds capable of exploiting this food source) and
observational learning (naive birds copying the behavior of
experienced innovators) (Aplin, Sheldon, & Morand-Ferron
2013).



One advantage of understanding culture in terms of soft inheritance is
that some of the methods and models employed in the study of
biological evolution can be applied to the study of culture (Mesoudi,
Whiten, & Laland 2006). Rachael Brown (2017a) also argues that
animal traditions are important for understanding genetic evolution.
For example, recent studies suggest that the diversification of beak
morphologies in Galapagos finches is the result of behavioral
foraging innovations spread through cultural transmission. The
presence and spread of these behavioral innovations (e.g., using
one's beak to puncture the skin and drink the blood of sea
birds, as in the case of "vampire finches") may have been
required to allow sufficient selective pressure for morphological
adaptation to occur. Despite the links between culture and genetic
evolution, there remains disagreement regarding whether the modern
synthesis in evolutionary biology should be extended to include
processes such as soft inheritance (see Pigliucci 2007; Laland et al.
2015). Nevertheless, research on animal culture is flourishing with
observational and experimental studies revealing both its cognitive
underpinnings and effects (see Whiten 2022 for a recent review).


### 1.4 Cognition in the Wild and Captivity



As noted, comparative cognition is a field that draws on methods from
a variety of disciplines, including ethology, experimental psychology,
and neuroscience. Philosophers and scientists have debated the
relative merits of these methods for understanding animal minds. A
central method in ethology is fieldwork on wild animals, for instance,
while experimental psychology relies primarily on laboratory studies
on captive animals. Wild and captive animals often lead different
kinds of lives. For example, captive chimpanzees tend to have regular
access to food, extensive experience with human objects (including
objects designed for enrichment), and live in a relatively small
space. Wild chimpanzees typically live in territories that are many
square kilometers in size, spend much time foraging, and must overcome
life-threatening problems not typically found in captivity, such as
competition with neighboring groups and predators (Boesch 2022). In
comparative cognition, many researchers stress the importance of
observing animals in their natural environment. From an ethological
perspective, this is crucial for understanding how animal minds
evolved because "it is in this situation that natural selection
acts" (Healy & Hurly 2003: 326).



Other researchers note that control conditions like those found in the
laboratory are necessary for investigating animal minds. For example,
Heyes and Dickinson (1990) argue that identifying the mental states
responsible for a given action requires determining



> 
> 
> what the animal would have done if its circumstances had been
> different in certain, specifiable respects from those in which the
> action actually occurred. (1990: 88; but see Allen & Bekoff 1997:
> chapter 9)
> 
> 
> 



Observing animals in their natural environment often precludes varying
the environment in systematic ways, while laboratory studies are
designed to control for alternative explanations and nuisance
variables (see
 section 2).
 Some researchers also argue that "unnatural" environments
might direct development and learning in ways that supports the
emergence of new or enhanced cognitive capacities. Captive great apes,
for example, develop problem-solving and communicative abilities not
found in their wild conspecifics, such as tool use, pointing, and sign
language (Tomasello & Call 2008; Bandini & Harrison 2020).
Studying animals outside of their natural environment might provide
insight into their cognitive potential when appropriately scaffolded
by the environment.



Overall, most researchers acknowledge that a plurality of methods is
needed for understanding animal minds. For example, Michael Tomasello
and Josep Call write regarding primate cognition research that,



> 
> 
> fieldwork is primary. It tells us what animals do; it sets the
> problem. But then if we wish to figure out what is the nature of the
> cognitive skill, if any, underlying some activity in the wild, we need
> experiments. (2008: 451)
> 
> 
> 



Kristin Andrews documents different possible sources of bias in the
field and laboratory. For example, in the field, ethograms might lead
to confirmation bias, while in the laboratory, the importance of the
relationships between human experimenters and study participants is
often underemphasized (see Andrews 2020: 42-61). Andrews
concludes that comparative psychologists should aim to employ and
integrate a variety of methods for studying animal minds, rather than
seek to eliminate bias. Colin Allen and Marc Bekoff (1997) similarly
emphasize the need for an interdisciplinary approach:



> 
> 
> Science is not likely to make complete contact with the nature of
> animal minds at any single point--many methods will be useful,
> and competing hypotheses should be evaluated. (1997: 180)
> 
> 
> 


## 2. Eliminating Alternative Explanations



Comparative cognition researchers often test hypotheses about animal
minds through behavioral studies or experiments. These studies are
designed to determine whether a species, group, or individual animal
behaves as one would expect on the assumption that a given hypothesis
is true. Often these hypotheses specify to some degree the cognitive
processes thought to underlie a given suite of behaviors. As
Shettleworth notes, researchers seek



> 
> 
> not just to confirm that animals are (or are not) capable of doing
> something "clever" but to discover *how* they do
> what they do. (Shettleworth 2013: 4)
> 
> 
> 



In this section, we consider the role that statistical nulls,
alternative explanations, epistemic values, and non-epistemic values
play in the evaluation of hypotheses in comparative cognition.


### 2.1 Null Hypotheses in Comparative Cognition



A central element of experimental work in the sciences involves
controlling for extraneous or "nuisance" variables.
Comparative cognition is like other sciences in employing experimental
and statistical methods to control for such variables (Bausman &
Halina 2018; Dacey 2023). The aim of an experiment is generally to
determine whether there is a relationship between the independent and
dependent variables. For example, crows drop nuts onto roads and
retrieve the cracked nuts (Shettleworth 2010a). Are they doing this
because they've learned that cars can be used as
"nutcrackers"? Or has the behavior of dropping nuts from a
height evolved over many generations (perhaps before the advent of
cars)? As a first step towards answering these questions, one might
experimentally investigate whether the presence of approaching cars on
a road (the independent variable) affects nut-dropping behavior in
crows (the dependent variable) (Cristol et al. 1997). To detect this
effect, however, one must control for the "noise" created
by extraneous variables--i.e., those variables that are also
likely to affect the dependent variable. In the case of foraging
crows, extraneous variables may include time of day, season, proximity
to nut trees, etc. In an experiment, the statistical null hypothesis
typically holds that any differences observed in the dependent
variable can be attributed to extraneous variables and thus there is
no evidence that the independent variable has had an effect (see Sani
& Todman 2006). Whether an observed difference in the dependent
variable can be attributed to the independent variable (i.e., whether
the difference is statistically significant) depends on one's
test statistic, which reflects the variability of the data due to
known extraneous variables. If proximity to nut trees can explain the
difference observed in nut-dropping behavior, for example, then there
is insufficient evidence to conclude that nut-dropping behavior has
been affected by the presence or absence of approaching cars.



The term "null hypothesis" is sometimes used more broadly
to refer to an alternative or competing hypothesis, rather than a
statistical null hypothesis. For example, associative learning,
behavior reading, and hypotheses that deny animals
"special" human-like cognitive abilities are often
referred to as "null hypotheses" (see Hanus 2016;
Dickinson 2012; Andrews & Huss 2014; Mikhalevich 2015). These
hypotheses are typically contrasted with those that attribute complex
or human-like cognitive abilities to animals like causal reasoning or
theory of mind. One problem with this broad use is that the term
"null" suggests that a hypothesis should be epistemically
privileged (in the sense that it must be rejected before accepting any
alternative hypothesis) when in many of these cases it is not clear
the purported null should be privileged in this way. As Andrews and
Huss (2014) write:



> 
> 
> the onus is on the skeptic to explain why the skeptical hypothesis and
> not the optimistic hypothesis that animals do have psychological
> properties is the proper null hypothesis. (2014: 720-721)
> 
> 
> 



Statistical null hypotheses are epistemically privileged in the sense
that they must be rejected before one can conclude that the variable
of interest (the independent variable) has had an effect. This
inference strategy is justified in the context of statistical
hypothesis testing due to the nature of experimental design and
inferential statistics (Bausman & Halina 2018). However, the
features that justify this strategy in the statistical case are not
present in hypothesis evaluation more generally (the latter lacks a
test statistic, for example). Instead, one must supply compelling
reasons for preferring one hypothesis (such as associative learning)
over another (such as causal reasoning). Such reasons may be
theoretical (e.g., that one hypothesis has epistemic virtues like
predictive power that the other lacks) or empirical (e.g., that one
hypothesis already has independent empirical support, while the other
does not). The next three sections discuss hypothesis evaluation in
comparative cognition in this broader sense. For additional discussion
of statistical null hypotheses, null modeling, and default models, see
Bausman 2018, Zhang 2020, and Dacey 2023.


### 2.2 Cognitive and Associative Explanations



As noted above, to provide compelling evidence that an animal has
cognitive capacities like episodic memory or theory of mind,
researchers aim to eliminate plausible alternative explanations for
behavior. This research strategy is not unique to comparative
cognition but is found across the sciences. For example, Julian Reiss
argues that a hypothesis is warranted insofar as one has eliminated
alternative hypotheses. That is, the strength of the warrant depends
on how many alternatives have been eliminated and how compelling or
salient those alternatives are (Reiss 2015). In comparative cognition,
associative learning is regularly advanced as a salient alternative
that must be eliminated before concluding that an animal has complex
cognition. As Starzak and Gray (2021) write,



> 
> 
> Over and over again the familiar refrain is, "do animals have
> complex human-like cognitive abilities or can their behavior be
> explained in terms of simpler processes such as associative
> learning?". (2021: 2)
> 
> 
> 



What is associative learning? Broadly, associative learning is a class
of learning mechanisms characterized by a change in association
between two or more variables. Two common forms of associative
learning are classical (Pavlovian) conditioning and operant
(instrumental) conditioning. In classical conditioning, a stimulus
comes to elicit a response in an organism because it has become
associated with another stimulus. For example, drawing on the studies
of the Russian psychologist Ivan Pavlov, dogs naturally salivate in
response to the taste of food (this response does not require training
or conditioning, so is an *unconditioned response* to an
*unconditioned stimulus*). However, one can pair another
stimulus, like the sound of a bell, with the arrival of food. Over
time, dogs will learn to associate the food with the sound of the
bell, such that the sound of the bell will become sufficient on its
own to elicit salivation (the sound has become a *conditioned
stimulus* leading to a *conditioned response*). In operant
conditioning, an organism's behavior changes in response to the
consequences of that behavior. For example, a cat might behave in many
ways while interacting with a puzzle box (which, say, contains food
the cat likes) with some behaviors resulting in the cat successfully
opening the puzzle box by accident. Over time, the successful
behaviors will be strengthened, and the unsuccessful behaviors
weakened, such that the cat will be able to quickly open the puzzle
box as a result of operant conditioning alone. The tendency for
behaviors to be strengthened or weakened in response to their positive
and negative consequences respectively is known as Thorndike's
law of effect after the American psychologist Edward Thorndike.



It is often possible to formulate plausible associative learning
accounts that can accommodate data originally believed to support
hypotheses like causal reasoning or theory of mind (for examples, see
Taylor, Medina, et al. 2010; Heyes 2012; Halina 2022). In many cases,
this is taken to undermine the original interpretation of the data
because associative learning is taken to be preferred as an
explanation of animal behavior over capacities that require
"complex" or human-like cognitive abilities (Buckner 2011;
Hanus 2016). One justification for this preference is that associative
learning is "simpler" than alternative cognitive
explanations and thus should be preferred all else being equal (see
 section 2.3).
 Philosophers have argued, however, that appealing to simplicity alone
in this context is not enough. Instead, one must show that the
associative and cognitive explanations in question can in fact be
ordered by complexity and that there are good reasons for preferring
the simpler explanation in a particular case (e.g., because the more
complex cognitive ability presupposes or requires the simpler
associative ability) (Heyes 2012; Meketa 2014; Dacey 2016, 2017). A
second justification for preferring associative learning as an
explanation is that it is phylogenetically widespread. If associative
learning is phylogenetically widespread, then it is reasonable to
assume that many animals will use it to solve physical and social
cognition tasks. Irina Mikhalevich (publishing as Meketa 2014) refers
to this as the "taxonomic ubiquity argument" (2014: 737).
One concern with this approach is that if it is standard practice to
believe that associative learning trumps complex cognition (in cases
where both are consistent with the data), then this practice might
itself result in associative learning appearing more taxonomically
ubiquitous. Thus, relying on such results to justify associative
learning as a preferred hypothesis appears to beg the question (Meketa
2014, but see Heyes 2012).



The above discussion presumes that associative learning and complex
cognition are mutually exclusive--that one or the other, but not
both, are needed to explain a behavior of interest. One explanation
for this assumption is that it is simply part of typical definitions
of complex cognition and associative learning. For example, Amanda
Seed and colleagues note that complex cognition is often
"defined by exclusion, rather than by some positive assessment
of the mechanisms underpinning it" (Seed, Emery, & Clayton
2009: 402). Such an approach identifies a flexible or novel behavior
as best explained by complex cognition when it "cannot easily be
explained in terms of simple conditioning, or hardwired action
patterns" (Seed et al. 2009: 410). Under this view, complex
cognition and associative learning are mutually exclusive by
definition. However, researchers have expressed concerns with the
associative-cognitive distinction (Allen 2006; Buckner 2017). One
concern is that associative learning models are now sophisticated
enough to capture paradigm cases of cognitive processes (see Buckner
2011, forthcoming; Dickinson 2012). Another concern is that cognition
does not lend itself to precise definition or clear categorization.
Allen (2017), for example, advances a "relaxed pluralism"
about cognition, allowing for multiple incompatible accounts. Those
who reject the associative-cognitive distinction often urge
researchers to focus on more specific capacities instead. As David
Papineau and Cecilia Heyes write,



> 
> 
> research should refocus on specific explanations of how animals do
> specific things, rather than on the presence or absence of some
> general or ideal form of rationality that contrasts with associative
> mechanisms. (2006: 187)
> 
> 
> 



Dacey (2016) also argues that the concept of "association"
is best understood as a "highly abstract filler term" that
can be implemented by a wide range of cognitive mechanisms (2016:
3763). Understood this way, associative learning and complex cognition
are not mutually exclusive: both may be needed to explain a given
behavior.


### 2.3 The Role of Epistemic Values



Epistemic values can be broadly characterized as those features (of a
theory or a theory in relation to evidence) that scientists value
because they're believed to lead to epistemic goods like truth
and understanding. Heather Douglas distinguishes between those
epistemic values that are minimal criteria versus ideal desiderata.
Minimal criteria are those values that are epistemically necessary:
they are "genuinely truth assuring" and their absence
indicates "something is wrong with our theory" (Douglas
2013: 799). These include features like internal consistency and
empirical adequacy. In contrast, ideal desiderata are not required,
but are useful and often provide assurance that we are on the right
track or that, if we're not on the right track, we will find out
sooner rather than later. Ideal desiderata include values like
simplicity, unification, and novel prediction. For example, a simpler
theory may be easier to use and a theory capable of making novel
predictions might assure researchers that the theory is not
overfitting the available data (Douglas 2009a, 2009b; Douglas &
Magnus 2013).



Comparative cognition researchers also appeal to epistemic values when
evaluating theories and hypotheses. Researchers minimally expect
theories to be internally consistent and empirically adequate. When
two competing theories are both consistent with the available
empirical data, researchers evaluate them with respect to other
epistemic values. For example, in the context of chimpanzee theory of
mind research, Tomasello and Call (2006) note that two
hypotheses--theory of mind and learned behavioral
rules--account for the available experimental data. However, they
argue that theory of mind provides a unified explanation of the
existing data, while the claim that chimpanzees rely on learned
behavioral rules requires positing a unique behavioral rule for each
experimental result. They also express concern that behavioral rules
are ad hoc. Fletcher & Carruthers (2013) concur, maintaining
that



> 
> 
> the behavior-rule account is only capable of "predicting"
> new findings after they are discovered, postulating a novel
> behavior-rule for the purpose. (2013: 88)
> 
> 
> 



Here, we find researchers arguing in favor of one explanatory theory
over another based on epistemic values like predictive power,
unification, and coherence.



One epistemic value that has received a lot of attention from
scientists and philosophers working in comparative cognition is
simplicity or parsimony (Dacey 2016). As we saw in the previous
section, the epistemic value of simplicity is often attributed to
associative learning, leading to debates about whether associative
learning is truly simple and, if so, what this means for theory
choice. However, another reason why simplicity has received a lot of
attention is its connection to Morgan's Canon--a
methodological principle widely adopted in research on animal
cognition. This principle holds that when there are two or more
plausible explanations for an animal's behavior, psychologists
should favor the explanation that appeals to "lower"
rather than "higher" psychical faculties (Fitzpatrick
2008).



Many philosophers have rejected Morgan's Canon as a useful
methodological principle. First, they argue that what counts as
"sophisticated" is often ambiguous (Sober 2005;
Fitzpatrick 2008; Meketa 2014). Simplicity has been used to
distinguish between sensory and conceptual reasoning,
stimulus-response mechanisms and conscious thought, associative and
non-associative learning. Moreover, in all these cases, the contrast
seems problematic (Andrews & Huss 2014). Second, if Morgan's
Canon is interpreted as no more than the dictum that "simpler is
better or more likely to be true", then it is a poor research
guide. There are numerous parsimony considerations one can make in
comparative research (ontological parsimony, explanatory parsimony,
evolutionary parsimony): often these considerations pull in different
directions and rarely do they favor the conclusion urged by
Morgan's Canon (Sober 2005; Fitzpatrick 2008; Dacey 2016).
Finally, philosophers of science have argued that justifying
simplicity as a virtue is context dependent--that is, it is not a
virtue that applies across the board (see Longino 2008). As Elliott
Sober writes:



> 
> 
> When a scientist uses the idea [of parsimony], it has meaning only
> because it is embedded in a very specific context of inquiry. Only
> because of a set of background assumptions does parsimony connect with
> plausibility in a particular research problem. What makes parsimony
> reasonable in one context therefore may have nothing in common with
> why it matters in another. (Sober 1990 [1994: 140])
> 
> 
> 



If Sober is correct, then determining whether one should prefer the
simpler explanation will depend on the case at hand. Insofar as
Morgan's Canon is a general methodological principle, meant to
hold across a wide range of disparate cases or a "blanket bias
towards endorsing lower explanations", it should be rejected,
according to this view (Fitzpatrick 2008: 243). Comparative cognition
researchers have reached similar conclusions. For example, Tomasello
and Call (2006) write,



> 
> 
> we are not strong proponents either of parsimony (unless one clearly
> defines the criteria for parsimony) or of Morgan's
> Canon--certainly not as substitutes for grappling with data when
> there is plenty of it. (2006: 381)
> 
> 
> 



It is worth noting that not all accounts of Morgan's Canon
characterize it as a simplicity principle. For example, Simon
Fitzpatrick and Grant Goodrich (2017) argue that when one looks at
Conwy Lloyd Morgan's own formulation of the canon, it does not
take the form of a simplicity principle. Indeed, Morgan explicitly
rejected simplicity as a criterion for choosing between competing
explanations. Instead, he held that one should choose the explanation
that best coheres with our observations and broader background
knowledge. One such piece of background knowledge for Morgan was that
"higher" faculties evolve from "lower" ones;
thus, the former will be rarer in nature than the latter, and this
should inform our choice of explanation. Similarly, Adrian Currie
(2021) argues that Morgan's Canon is best understood as holding
that evolutionary ancient and highly evolvable traits are more likely
to be found across the tree of life. Thus, explanations that appeal to
such "lower" traits should be preferred over those that do
not. Under this view, whether a cognitive trait is simple or complex
is not relevant--it is its expected taxonomic distribution and
evolvability that matters.


### 2.4 The Role of Non-Epistemic Values



Philosophers have argued that non-epistemic values, such as practical
and ethical ones, play an important role in science. Such values are
needed to evaluate the risk of uncertainty associated with a
hypothesis (see Rudner 1953; Douglas 2009a). Hypotheses are not
deductively entailed by the evidence. Instead, one must determine the
appropriate trade-off between false positive and false negatives for
any given test. A test with a high bar for hypothesis acceptance will
result in more false negatives, while a test with a low bar for
hypothesis acceptance will result in more false positives. Determining
the right balance between errors often requires taking non-epistemic
factors into account. For example, if a false negative has
consequences we wish to avoid as a society (e.g., death due to lack of
treatment), while a false positive for the same test does not (e.g.,
the treatment is harmless and has negligible economic costs), then we
should err on the side of the false positive. Of course, often the
calculus is not this simple and numerous social and economic factors
must be considered.



The results of comparative cognition research are used to inform laws
and welfare policies concerning animals. For example, in December
2013, a group of lawyers, scientists, and policy experts filed a
petition for a writ of habeas corpus in a New York State Supreme Court
(Grimm 2013). A writ of habeas corpus is a court order to
"produce the body". It requires any person or institution
holding a prisoner to bring the captive to court and justify her
imprisonment and treatment. The writ was filed on behalf of Tommy, a
male chimpanzee. At the time of the filing, Tommy was seen living
alone in a dark shed. If recognized by the court, the writ would
require Tommy's holder to justify Tommy's captivity and
treatment. This was the first time a habeas corpus had been filed on
behalf of a nonhuman animal and, if successful, would represent the
first case of an animal being given the right not to be treated as
property in the United States. The case on behalf of Tommy was made in
part by drawing on cognitive evidence. The plaintiffs argued that the
cognitive abilities of chimpanzees are such that solitary confinement
causes harm. Numerous comparative cognition researchers and
philosophers have testified in support of this case (See
 Nonhuman Rights Project: Client, Tommy (Chimpanzee);
 Andrews, Comstock, et al. 2018). Thus, how we treat and think we
ought to treat animals often depends on our knowledge of their
cognitive abilities (Bekoff & Gruen 1993). Such knowledge can be
used to prevent negative states of mind, such as loneliness, anxiety,
and distress, as well as restore and promote positive mental
states.



Should the methods of comparative cognition take non-epistemic values
into account? Jonathan Birch (2018) argues that when there are
"clear policy applications in view" comparative cognition
researchers should adjust their standards to reflect the moral
consequences of error (2018: 1028). Birch advances a criterion to help
determine when an animal welfare scientist (*X*) should accept
the hypothesis that some species (*S*) has a mental state
(*M*), given a particular policy context (*P*) (Birch
2018). Under this view, a welfare scientist should accept that some
species has a mental state in a particular policy context *P*
if and only if the expected sum of the possible welfare outcomes given
the scientist's background knowledge and the decision to affirm
the hypothesis that *S* has *M* in *P* is
*greater* than the expected sum of the possible welfare
outcomes given the scientist's background knowledge and the
decision not to affirm the hypothesis that *S* has *M*
in *P*. One concern with precautionary approaches such as this
one is that it is challenging to agree on where to set the burden of
proof in any given case. For example, Birch (2017) proposes to set the
evidential bar for animal sentience at "at least one credible
indicator of sentience in at least one species of that order"
(2017: 5). Other researchers, however, have objected that this
evidential bar is either too weak or too strong. For example, Michael
Woodruff (2017) argues that this evidential bar is too weak and should
be raised to include many more independent indicators of sentience to
decrease uncertainty regarding the principle's level of
scientific support. In contrast, Rachael Brown (2017b) argues that
Birch's evidential bar is too strong for those situations in
which there is no statistically significant evidence of a single
credible indicator of sentience, but instead "multiple, weak,
but convergent, lines of evidence that a species is sentient"
(2017b: 2). Despite these differences, there is general agreement that
expected welfare consequences should affect evidential standards in
those areas of comparative cognition that have clear policy
implications, and that the right evidential standard must be
determined on a case-by-case basis (see also Benz-Schwarzburg,
Monso, & Huber. 2020; Crump et al. 2022).


## 3. Methodological Challenges


### 3.1 Anthropomorphism



A perennial concern in animal cognition research is whether
researchers are being "anthropomorphic". Shettleworth
defines anthropomorphism as



> 
> 
> the attribution of human qualities to other animals, usually with the
> implication it is done without sound justification. (Shettleworth
> 2010b: 477)
> 
> 
> 



The term "human qualities" refers to those properties that
we readily accept as characteristic of humans. These may include
cognitive abilities such as future planning, empathizing, insightful
problem solving, and reliving past experiences. In comparative
cognition, researchers may attribute such states to animals based on
the available empirical evidence. In our everyday lives, we also often
attribute human-like states to animals (Serpell 2005). In both cases,
there's a question whether one's attributions are correct
or not.



Concerns surrounding anthropomorphism have dramatically influenced the
methods and conclusions drawn from animal studies throughout the
twentieth and twenty-first centuries. Behaviorism within animal
cognition research, for example, can be understood in part as a
response to concerns about anthropomorphism (Wynne 2007). For those
worried that we are misattributing human-like cognitive states to
nonhuman animals, one solution is to focus on describing observed
relationships between environmental cues and behavior instead.
Although traditional behaviorism is widely rejected today, there are
contemporary scholars who hold that behaviorism had something right in
its unwillingness to anthropomorphize. As Clive Wynne (2004)
warns:



> 
> 
> Old-time behaviourism may have imposed excessive constraints on animal
> psychology. But the reintroduction of anthropomorphism risks bringing
> back the dirty bathwater as we rescue the baby. (2004: 606)
> 
> 
> 



Should we be concerned about anthropomorphism in comparative
cognition? One reason to be concerned is that there are empirical
studies showing that humans have the tendency to over-attribute mental
states to objects in their environment. For example, in one classic
study, the psychologists Fritz Heider and Marianne Simmel (1944)
showed human participants a video of three shapes moving in various
directions and speeds. Despite the objects being two-dimensional
shapes, almost all participants described the scene in anthropomorphic
or human-like terms (e.g., as the shapes "fighting" or
"chasing" one another). Contemporary studies confirm that
humans are quick to attribute mental states to objects and agents
based on behavior and other cues (like the presence of eyes) (Fiala,
Arico, & Nichols 2011, 2014; Arico et al. 2011). As Dacey (2017)
argues, the human tendency to anthropomorphize may serve as a fast and
frugal heuristic allowing one to quickly anticipate the behavior of
other agents. However, if this heuristic activates even when no
psychological agent is present, then this may lead to numerous false
positive in animal cognition research. Dacey argues that the best
methodological approach is not a general prohibition against the
attribution of human-like mental states to animals, however, but
rather the application of methods known to effectively combat implicit
bias, like making counter-stereotypical information salient. For
example, selecting targets that are relatively unlikely to be
anthropomorphized (such as insects) and asking researchers to imagine
such targets behaving intelligently may help counter intuitive
anthropomorphism (Dacey 2017: 1158-1161).



A second argument in favor of avoiding the attribution of human-like
mental states to animals highlights that there are competing
explanations that can account for the behaviors in
question--explanations that do not appeal to sophisticated
cognitive abilities. For example, Shettleworth (2010b) discusses the
cases of animal insight, theory of mind, and mental time travel.
Research suggests we find these abilities in animals such as
chimpanzees and crows. However, Shettleworth argues that we should
resist this conclusion because there are alternative,
non-anthropomorphic, explanations for the behaviors observed in these
animals. For example, we can explain the apparent insightful
problem-solving behavior found in crows as part of their natural
behavioral repertoire or as being driven by cues in the environment.
And we can explain the apparent theory of mind abilities in
chimpanzees as instead arising from a set of learned and innate rules
about what to expect in social situations (see
 section 2).
 Crucially, in addition to arguing that sophisticated mental abilities
might not provide the best explanations for animal behavior,
Shettleworth argues that such abilities might also not provide the
best explanation for human behavior. She writes that empirical
research



> 
> 
> increasingly reveals an unexpected role in human behavior for simple,
> unconscious and sometimes irrational processes shared by other
> animals. Greater appreciation of such mechanisms in nonhuman species
> would contribute to a deeper, more truly comparative psychology.
> (Shettleworth 2010b: 477)
> 
> 
> 



Buckner (2013) additionally argues that humans have an inflated sense
of their own cognitive aptitudes; thus, using this sense as a guide
for evaluating other animals "loads the deck against animal
mentality" (2013: 853). He calls this tendency
"anthropofabulation" which combines anthropocentrism (next
section) with an exaggerated sense of human intelligence.



Some researchers argue that anthropomorphism has scientific benefits.
For example, "heuristic anthropomorphism" holds that we
can use our intuitive understanding of humans and animals to generate
concrete hypotheses about animal behavior and then test those
hypotheses empirically (de Waal 1999). In other words, we can use our
experience of being human--that is, of being a human
animal--to help us generate hypotheses about what it might be
like to be another animal, such as a crow or dog. Crucially, however,
the idea is not to simply impose a human perspective on other animals,
but to take what is known about an animal's behavior,
evolutionary history, ecological context, etc. into account. The idea
is to do this critically; that is, in a conjectural or provisional
way, which leaves the proposed hypothesis open to testing, revision or
rejection. As de Waal (1999) writes:



> 
> 
> While we should be reluctant to postulate capacities for which there
> is no evidence anywhere in a species' behavior, charges of
> anthropomorphism are meaningless without a precise critique of the
> hypotheses under consideration. In a Darwinian framework, there is no
> good reason to avoid concepts merely because they derive from the
> behavior of the species to which we belong. Application of these
> concepts to animals not only enriches the range of hypotheses to be
> considered, but it also changes the view of ourselves: the more
> human-like we permit animals to become the more animal-like we become
> in the process. (1999: 272).
> 
> 
> 



Andrews (2016, 2020) also argues for the importance of human folk
psychology in comparative cognition research. Folk psychology can be
broadly understood as our human commonsense understanding of
psychological phenomena (see entry on
 folk psychology as a theory).
 Folk psychology is a form of anthropomorphism insofar as it involves
applying human qualities to other animals. Andrews argues that folk
psychology is methodologically important for grouping animal behaviors
together into types. For example, in a now classic study, Whiten and
Byrne (1988) collected reports of tactical deception in nonhuman
primates. "Deception" is a folk psychological term used to
indicate the human act of deceiving or tricking another individual.
However, despite its folk-psychological origin, Andrews argues that
this term provided a useful starting point for individuating and
categorizing behaviors. Such categories were then analyzed, updated,
and refined based on empirical evidence. Identifying robust behavioral
types in this way is in turn important for investigating the cognitive
mechanisms responsible for behavior (Andrews 2016a).


### 3.2 Anthropocentrism



The term "anthropocentrism" describes the tendency to
locate human beings at the "center". Anthropocentrism can
broadly be understood as the claim that humans are special or
exceptional in some way. Some researchers hold that concerns about
anthropomorphism (see previous section) arise from a place of
anthropocentrism: "Cries of anthropomorphism are heard
particularly when a ray of light hits species other than our
own" (de Waal 1999: 256). In other words, the belief that humans
are special gives rise to a bias against attributing human qualities
to other animals. Brian Keeley (2004) argues that historically,
particularly in the theological context, some traits have been taken
to belong exclusively or categorically to one group, such as humans or
gods. For example, one might hold that only humans have souls. In this
case, to attribute a soul to a nonhuman animal is to make a category
mistake: nonhuman animals are simply not the types of things that can
have souls. Such an attribution will always be false. Keeley contrasts
this "categorical anthropomorphism" with
"situational anthropomorphism" (2004: 529, see Fisher
1996). While categorical anthropomorphism involves mistakenly
attributing a quality to something that simply cannot possess that
quality, situational anthropomorphism involves mistakenly attributing
a quality to an agent or system, but this quality is something that
the agent or system could possess in principle. If cognitive
capacities like insight and episodic memory are products of evolution
and development, then there is no reason in principle why other
animals should not have them. If on the other hand, such capacities
are products of cultural inheritance involving language, we should not
expect to find them in nonlinguistic species. In either case, it is an
empirical question, in the same way determining whether an animal is
omnivorous or land-dwelling is an empirical question. In the same way
that researchers should be concerned about anthropomorphism and false
positive attributions of mental states to animals, one should be
concerned about anthropocentrism and false negatives--the failure
to attribute human-like qualities to animal when the animal in fact
possesses them (Sober 2005, 2012). Such false negatives have been
termed "anthropodenial" and "anthropectomy" in
the literature (de Waal 1999, Andrews & Huss 2014).


### 3.3 Underdetermination



A major challenge in comparative cognition is that claims about
cognition are often underdetermined by behavioral evidence. As
Tomasello and Call note, "the exact same behavior may be
underlain by very different cognitive mechanisms" (2008: 451).
Broadly, a hypothesis is underdetermined when the available evidence
fails to indicate what we should believe about that hypothesis (see
entry on
 underdetermination of scientific theory).
 For example, if two hypotheses are equally supported by the available
data, we might not be able to choose between them (contrastive
underdetermination). Or if a hypothesis is found to be incompatible
with an empirical result, we might not know whether to reject that
hypothesis or some other background assumption instead (holist
underdetermination). Problems of underdetermination are found
throughout the sciences. However, this problem is particularly salient
in the cognitive sciences, given the opaque and complex nature of
cognitive systems.



We encountered an example of contrastive underdetermination when
discussing cognitive and associative accounts of behavior
 (section 2.2).
 There we saw that some competing hypotheses appear to be equally well
supported by the empirical data: for example, causal reasoning and
associative learning accounts of problem-solving behaviors in corvids
(see Taylor, Medina, et al. 2010). If it is true that,
"associative hypotheses can be constructed *post-hoc* for
every experimental outcome" (Starzak & Gray 2021: 4), then
simply finding that one's causal-reasoning hypothesis fits a
particular experimental outcome will not be sufficient to accept it
over the available associative hypotheses. Instead, one must appeal to
other factors, like epistemic values
 (section 2.3)
 to determine hypothesis choice.



Another major source of underdetermination in comparative cognition is
that the structure and function of target cognitive phenomena are
often uncertain and open to revision. For example, since the 1970s, a
significant amount of research has been dedicated to determining
whether nonhuman great apes like chimpanzees have "theory of
mind" (ToM) or the ability to attribute mental states to other
agents. In their classic paper initiating this research program, David
Premack and Guy Woodruff write:



> 
> 
> we speculate about the possibility that the chimpanzee may have a
> "theory of mind", one not markedly different from our own.
> (Premack & Woodruff 1978: 515)
> 
> 
> 



However, they add that a chimpanzee's ToM may differ from human
ToM in important respects--e.g., in the type of mental states
inferred. Moreover, throughout work on animal ToM, researchers'
understanding of the target phenomenon and relevant background
assumptions have changed in response to new findings. For example,
rather than rejecting the hypothesis that chimpanzees attribute
perceptual states to other agents, researchers have interpreted some
negative results as due to other factors (e.g., lack of ecological
validity or poor experimental design) (Hare et al. 2000; Kaminski et
al. 2004; Brauer et al. 2007). This has then led to proposals
regarding what additional experimental controls are needed to
successfully detect a relationship between the independent and
dependent variables (see
 section 2.1).
 Philosophers have noted that such revised understandings of the
target phenomenon play an important role in the biological and
cognitive sciences (Bechtel 2008; Bechtel & Richardson 1993
[2010]). Nevertheless, revising the phenomenon in this way leads to
underdetermination: in the face of conflicting data, it is unclear
whether researchers should reject the target hypothesis or revise it
(an instance of holistic underdetermination).


### 3.4 Signatures and Dimensions



One way to overcome problems of underdetermination is to provide
additional constraints on hypothesis construction and evaluation.
There have been several recent proposals in the literature on how to
do this. For example, Alex Taylor and colleagues argue that
comparative cognition researchers are currently too focused on whether
animals succeed at a particular experimental task (Taylor, Bastos, et
al. 2022). In their view, the problem with this approach is that it
fails to adequately constrain the hypothesis space--there are
simply too many plausible hypotheses that could account for such
success. Given this, researchers should instead seek to identify



> 
> 
> the full range of information processing patterns including errors,
> limits, and biases (whether neutral, adaptive, or maladaptive) shown
> by an agent, so as to constrain the cognitive hypothesis space
> effectively. (Taylor, Bastos, et al. 2022: 3)
> 
> 
> 



Taylor and colleagues refer to this as "signature testing"
(in contrast to "success testing"). A
"signature" is any pattern of evidence that constrains the
hypothesis space with respect to a phenomenon of interest (it can be
weakly or strongly diagnostic depending on how much it constrains the
hypothesis space). A successful hypothesis should be able to account
for all signatures of a cognitive process, not just an animal's
successful performance on an experimental task.



Starzak and Gray (2021) similarly urge researchers to develop more
fine-grained accounts of how to conceptualize cognitive phenomena. For
example, with respect to the cognitive phenomenon of causal
understanding, they write:



> 
> 
> in thinking about the nature of causal understanding we should think
> about the extent to which organisms can differ with respect to the
> kind of information they can pick up; with respect to the different
> sources of causal information they can exploit; with respect to the
> way they can process this information and integrate different types of
> information or information stemming from different sources; and with
> respect to the flexibility with which they can use this information to
> guide behavior. (2021: 9)
> 
> 
> 



Like signature testing, this multidimensional approach is more nuanced
than asking whether an animal "has causal understanding"
*tout court*. It also helps address the issue that associative
learning and complex cognition are not necessarily mutually exclusive
 (section 2.2).
 Rather than asking whether an experimental result is best explained
by appealing to associative learning or causal understanding, one can
appeal to both accounts: associative learning might explain how causal
information is acquired in some cases and cognitive models might
explain how causal information is integrated. Researchers have
advocated for a similar multidimensional approach in the context of
work on behavioral innovation (Brown 2022) and animal consciousness
(Birch, Schnell, & Clayton 2020, see section on consciousness in
entry on
 animal cognition).
 The upshot is that a more fine-grained approach to cognition and
behavior may help constrain the hypothesis space in such a way that
avoids major problems of underdetermination.


### 3.5 Reproducibility



In psychology, reproducibility and replication typically refer to
redoing an experiment to assess its reliability. If a study is
reliable, then running the study again should produce the same
results. If a replication fails, then this may mean that the original
result was a product of measurement error, sampling error, imprecise
manipulation, questionable research practices, or other factors (for
an overview, see Romero (2019) and the entry on
 reproducibility of scientific results).
 Efforts to replicate studies have increased over the past two decades
across a wide range of fields, including medicine, computer science,
and psychology (Bohannon 2014). This has led to what some describe as
a "replication crisis" because a surprising number of
studies have failed to replicate. For example, an effort to replicate
100 studies by 270 psychologists as part of the Open Science
Collaboration found that only 38% of the original results were
reproduced unambiguously, with some attempted replications finding an
effect opposite to that of the original study (Bohannon 2015).
Findings such as these have led researchers to ask whether comparative
cognition also suffers from a replication crisis, and if so, what can
be done to improve the field (Brecht et al. 2021). In a recent survey,
for example, comparative cognition researchers were asked their views
on replications (Farrar, Ostojic, & Clayton 2021). Out of 210
respondents, the majority agreed (34.8%) or strongly agreed (54.8%)
that replications are important to perform in animal cognition
research. The majority also disagreed (55.5%) or strongly disagreed
(23.6%) that enough replications were already performed in the
field.



Despite the above survey results, comparative cognition has been
described as "an absolute beacon for replication efforts"
(Beran 2018: 2). Beran (2018) notes that a standard in the field has
been to conduct a series of experiments with the first experiment
consisting of a replication of the study that inspired the work and
subsequent experiments dedicated to extending that experiment (2018:
2). Halina (2021a) also argues that replications in comparative
cognition are common. She adopts Edouard Machery's resampling
account of replication (Machery 2020), showing how under this view
successful replications occur frequently in areas such as chimpanzee
theory of mind research. If replication success were a clear indicator
of reliability, then this would be good news for comparative
cognition. The picture is however complicated by several factors.
First, failed replications are difficult to interpret. Often a
replication study will differ from the original study in several
respects: in such cases, it is possible to attribute failure to these
differences (what Colaco, Bickle, and Walters (2022) refer to
as "mismatch explanations"). In chimpanzee theory of mind
research, for example, failed replications have regularly been
explained by appealing to changes in the experimental setting (Halina
2021a). As we saw in
 section 3.3,
 revisions to background assumptions are an important part of science.
However, such revisions mean that failed replications are often not
interpreted as undermining reliability (Nosek, Spies, & Motyl
2012). As Alexandria Boyle (2021) argues, for this and other reasons,
replications in comparative cognition are



> 
> 
> poorly placed to deliver clear judgments about the reliability of
> comparative cognition's methods or its scientific bona fides.
> (2021: 296, see also Feest 2019)
> 
> 
> 



Another factor complicating the picture is the "file-drawer
problem", which refers to the phenomenon that studies that fail
to find statistically significant results may be relegated to the file
drawer (i.e., not published or communicated to the larger scientific
community). In the survey cited above, comparative cognition
researchers were also asked "What percent of the studies that
you have performed have been published and/or you think will be
published?" (Farrar, Ostojic, & Clayton 2021). The
median response was 80% with a large spread (out of 210 responses, 23
said that they published 50% or fewer of their studies, while 17
reported publishing all their studies). Twenty-nine respondents cited
negative or uninteresting results as the reason for not publishing
(2021: 18). The file-drawer problem combined with mismatch
explanations of failed replications may create an impression of
reliability via the reporting of many successful replications when in
fact the record is mixed.



If replications on their own are not a route to more robust research,
what else could help? Beran (2018) emphasizes the importance of
pre-registration. Pre-registration involves publishing the methods and
statistical analyses that one will use in a study before data
collection. This prevents one from adjusting or
"massaging" elements of the experimental design and data
analyses with the aim of getting a positive result. Farrar, Voudouris,
and Clayton (2021) also advance several methods that comparative
cognition researchers could use to assess the reliability of small
sample research, such as statistically modeling variation. More
broadly, Felipe Romero (2020) argues that major changes to scientific
incentive structures are needed: in particular, a shift away from
rewarding novelty and towards rewarding replication and confirmation
work. Along these lines, Brecht et al. (2021) note that while there
are still many disincentives to conducting replication studies in
comparative cognition, the field is working towards improving this.
For example, the journal *Animal Behavior and Cognition* has
committed to publishing pre-registered studies (including
replications) regardless of the results. In addition, several new
global consortiums (such as ManyPrimates, ManyBirds, and ManyDogs)
have formed with the explicit aim of assessing reliability,
encouraging transparency, supporting large collaborations, and
fostering other open science practices (see
 Other Internet Resources).


## 4. Comparative Cognition and AI



There has been a large amount of collaborative work between
comparative cognition and artificial intelligence (AI) research over
the past decade. On the one hand, formal models developed in AI can be
used to predict and explain animal behavior in ways that move beyond
folk psychological accounts. On the other hand, the methods of
comparative cognition are well placed to inform AI research, given the
diversity of behavior and information-processing abilities found
across the tree of life. We'll consider both aspects here, while
also highlighting why animal-AI inferences should be handled with
care.



Colin Allen (2014) argues that comparative cognition would benefit
greatly from developing mathematically rigorous formal models. As we
have seen throughout this entry, comparative cognition researchers
often rely on intuitive, natural language concepts for constructing
accounts of animal minds like causal reasoning, mental time travel,
imagination, and self-recognition (see Schnell et al. 2021b).
Regarding this approach Allen (2014) writes:



> 
> 
> The conceptual framework guiding most work in comparative animal
> cognition (whether by ethologists or psychologists) is insufficiently
> formalized to support rigorous science in the long run. (2014: 82)
> 
> 
> 



One concern with more formal approaches, however, is that they will
fail to "scale up" to predict and explain the behavioral
phenomenon that are of interest to many comparative cognition
researchers, such as natural social and physical interactions. Work in
AI is starting to show that there are ways of closing this gap,
however. For example, Peter Battaglia and colleagues advance a
formalized mental model in the form of an "intuitive physics
engine" (IPE) analogous to the machine physics engines used in
interactive video games (Battaglia, Hamrick, & Tenenbaum. 2013,
see also Ullman et al. 2017). The IPE is probabilistic and
oversimplifies the nature of objects, such as their geometry and mass
density distribution, but has been developed to explain how people
quickly make inferences about physical scenes in a dynamic and noisy
world. Battaglia and colleagues found that this formal model does
indeed capture people's intuitions and prediction about physical
scenes (such as "will this tower of blocks fall?") across
a wide range of complex and novel scenarios. Moreover, the model
captures not just successful predictions, but also other signatures of
human judgment like illusions and biases (see
 section 3.4).



The above work uses AI to explain human cognition and behavior.
However, such methods are also being applied to nonhuman animals. For
example, much of contemporary AI depends on the tools of reinforcement
learning (RL). These techniques involve linking states of the
environment and an agent's actions in such a way that allows the
agent to maximize future rewards. The tools of RL were originally
inspired by animal learning research (Hassabis et al. 2017); however,
they have since been developed by AI researchers in ways that allow
fresh insights into animal behavior. For example, the
temporal-difference (TD) model has been used to explain a wide range
of results from animal classical conditioning studies (see Sutton
& Barto 1998: chapter 14). Similarly, Buckner (2018) draws on work
on Deep Convolutional Neural Networks (DCNNs) to explain the
relationship between sensory experience and representations of
abstract categories in humans and animals--a longstanding
question in philosophy of mind. Halina (2021b) also shows how one can
understand aspects of animal insightful problem solving (namely,
mental scenario building) through Monte Carlo tree search. Finally,
Bohn et al. (2022) advance a Bayesian computational model of great ape
communication, which they show accurately predicts the communicative
interactions of real-world chimpanzees living semi-wild in the
Chimfunshi Wildlife Orphanage in Zambia. These are a few examples of
researchers drawing on AI for formal models that can be successfully
applied to capture animal minds and behavior (see also van der Vaart
et al. 2012).



A major aim of AI research has been to build machines that
"learn and think like people" (Lake et al. 2017).
Currently, there are many AI systems that match or exceed humans on
various tasks, like the ability to play Chess and Go, and even make
scientific discoveries (Shevlin et al. 2019). However, how to build a
machine with domain-general intelligence or "common sense"
remains elusive. This challenge has led some researchers to argue that
animal cognition research offers the best path towards building
thinking machines. Animals exhibit many of the "building
blocks" of human common sense, such as an understanding of
objects and their affordances, space, and causality (Shanahan et al.
2020). Through the application of RL techniques, it is also possible
to train artificial agents in 3D virtual environments analogous to the
real world. Using this approach, one can combine different RL
architectures and training environments with the aim of encouraging
the development of domain-general abilities. As Murray Shanahan and
colleagues write,



> 
> 
> animal cognition supplies a compendium of well understood,
> nonlinguistic, intelligent behaviour; it suggests experimental methods
> for evaluation and benchmarking; and it can guide environment and task
> design. (2020: 863; see also Crosby 2020)
> 
> 
> 



Work such as this is already underway. For example, the Animal-AI
Testbed applies experimental protocols developed in comparative
cognition to test AI (Crosby, Beyret, & Halina 2019; Crosby,
Beyret et al. 2020). In 2019, the testbed included 300 tasks grouped
into 12 categories such as spatial elimination, delayed gratification,
numerosity, and tool use tasks. Konstantinos Voudouris and colleagues
(2022) compared AI performance on this testbed with children aged
6-10. They found that children and AIs performed similarly on
basic navigational tasks, but that children outperformed AIs on more
complex tasks like object permanence and detour tasks.



There is much to be gained from animal-AI comparisons. Such work may
represent the beginning of a transformation in the field of
comparative cognition--one that brings artificial systems into
the fold. It may also lead to the development of cognitive models that
bridge the gap between intuitive accounts of capacities like
imagination and causal reasoning on the one hand, and mathematical and
computational models on the other. However, when applying "rich
psychological concepts" like awareness, episodic memory, and
theory of mind to AI, one should also proceed with caution (Shevlin
& Halina 2019). Some such concepts have normative dimensions, for
example, indicating a potential moral agent or moral patient. These
normative dimensions are important to acknowledge before adopting a
term in the context of AI, particularly if that term is used
differently in the two contexts. AI findings may also dramatically
alter our understanding of some cognitive capacities. Rather than
adopting existing cognitive concepts, it may be fruitful in some cases
to develop a radically new approach in the context of AI. If
successful, such an approach could then be imported into comparative
cognition and potentially revolutionize our understanding of animal
minds.