Cognition
Katarzyna Wojczulanis-Jakubas
09 January, 2022
Introduction
Animal cognition encompasses the mental capacities of non-human animals, thus conditioning and learning (lecture #4) and processing and applying the acquired knowledge/skills. Cognition domains can be presented in a form of Bloom’s pyramid.
Bloom’s taxonomy is a set of three hierarchical models used to classify educational learning objectives into levels of complexity and specificity. The cognitive domains have been the primary focus of most traditional education and are frequently used to structure curriculum learning objectives, assessments and activities. The models were named after Benjamin Bloom who chaired the committee of educators that devised the taxonomy. Defined as such, the term that is frequently used in human education system but given the commons it can be easily applied for cognition in animals.
We already know for sure that animals do learn, and they do it in various ways (habituation/sensitization, conditioning, trials and errors, latent learning, social learning and finally, teaching). What we still do not entirely understand (and perhaps will never be able to fully comprehend, see the book of Franz de Waal (2016): Are we smart enough to know how smart animals are?) is how similar is human and animals cognition, and could/can animals do what we believe is human-only thing to do.
Whatever we study on, we should always keep in mind Morgan’s canon (see the previous lecture #4).
Lower order cognition: recall (e.g a dog can find a bone which it digged down few days ago) know facts, predict close future (e.g. there is an award after a trick).
Higher-order cognition (basically it is thinking and language):
calculations and arithmetic operations (computations),
abstracting/concept formation – understanding the meaning of a given subject, identifying common (and different) issues, generalizing and recognizing patterns,
judgment - making statements about a certain state of affairs,
decision-making - in a situation with at least two options, choosing one of them based on some (, which is not equivalent to taking action,
organising and planning
problem solving - “activity aimed at reducing the discrepancy between the current state and the desired state, involving the implementation of a planned sequence of cognitive operations”.
Calculations and computations
Do animals care about numbers? Well, for sure they do care how much food they can eat (although it is more the question of quantity)? But can day do same stuff we do with the quantities – assess them, and perform arithmetic operations? Let’s start with an anecdotal example of Hans, a clever horse.
Clever Hans (der Kluge Hans in German) was a horse that was supposed to be able to do lots of difficult mathematical sums and solve complicated problems. For example, if asked a question such as: “What is 12 plus 12”, the horse would tap its hoof 24 times. Von Osten (Hans’ owner, math teacher and amateur horse trainer) travelled around the country with Hans, showing off his clever horse to the public (and earing money ;). Later, it was discovered that the horse was giving the right answers by watching the reactions of the owner (and other people) who were watching him/asking the questions (for now so called the clever Hans effect). The people did not give the hints deliberately but their unconscious behaviour was influencing the “answer”
The clever Hans effect. It is used in psychology to describe when an animal or a person senses what someone wants them to do, even though they are not deliberately being given signals. It is important to take this effect into account when testing animals’ intelligence or human intelligence. An animal may need to be separated from its trainer if their true intelligence is to be observed. On the other hand, an animal may be upset when it cannot see its trainer, so it might not give the right answer. This problem can often be solved by creating a situation in which the trainer does not know the right answer.
Systems of number processing:
All of the systems are observed in human and animals. There are some evidence they work in a similar way, although obviously, there are also differences.
There are four systems:
1) The approximate number system (ANS)
2) Parallel individuation system (PIS)
3) Subitising (from latin: subito which means just/suddenly)
4) Sequence ordering/ordinality
Approximate number system
Approximate number system (ANS) - imprecise, used to estimate quantities. It is based on the effect of distance and size of numbers, i.e. comparing numbers is easier and more precise when the distance between them is smaller and when the values of numbers are smaller. It is based on Weber-Fechner law (it is the ratio of numbers that matters, not their absolute value).
The Weber–Fechner laws - are two related hypotheses in the field of psychophysics, known as Weber’s law and Fechner’s law. Both laws relate to human perception, more specifically the relation between the actual change in a physical stimulus and the perceived change. This includes stimuli to all senses: vision, hearing, taste, touch, and smell. Weber states that, “the minimum increase of stimulus which will produce a perceptible increase of sensation is proportional to the pre-existent stimulus,” while Fechner’s law is an inference from Weber’s law (with additional assumptions) which states that the intensity of our sensation increases as the logarithm of an increase in energy rather than as rapidly as the increase.
For example:
On each side, the lower square contains 10 more dots than the upper one. However the perception is different: (The ratio is 0.50). On the left side, the difference between upper and lower square is clearly visible. On the right side, both squares look almost the same. (The ratio is 0.92).
ANS is one of the most basic systems, and there are many evidence this is the system that animals operate with. As a matter of fact, it has been found that the same brain areas in humans and animals are activated, when numbers are processed within this system, see the relevant video on dogs experiment (Autlet 2019).
Another example is a study on babbons (see the video), that can differentiate the amount of food (based on ANS). In this experiment, researchers asked naive olive baboons (Papio anubis) to discriminate between number pairs containing small (<4), large (>4), or span (small vs. large) numbers of food items presented simultaneously or sequentially. They found that baboons successfully discriminated all pair types at above chance levels. In addition, performance significantly correlated with the ratio between the numerical values. Although performance was better for simultaneous trials than sequential trials, evidence favoring analog numerical representation emerged from both conditions, and was present even in the first exposure to number pairs (Barnard et al. 2013).
The y axis – a gradient – from the most different (0) to more similar (1). Effect of numerical ratio on monkeys’ accuracy during their first exposure to numerical discrimination. The numerical ratio is the smaller number in a given pair divided by the larger number. Each data point represents a different number pair. The trend line represents a linear trend of decreasing accuracy with finer numerical ratios. From: (Barnard et al. 2013).
Why it is important to be able to differentiate different quantities, well the food as on baboons example is a good argument, and another one could be numerical representation of a social group.
Copyright: from BBC website:
For example, in chimpanzes when an intruder in the group’s territory is attacked only if the intruder is alone and the attacking party consists of at least three males - a ratio of 1 to 3, a fight will ensue. As a matter of fact, the concept of social numerical superiority exists in many primate species and demonstrates an understanding of numerical strength, at least comparatively.
Parallel individuation system
Parallel individuation system (PIS), also called object tracking system is a non-symbolic cognitive system that supports the representation of numerical values from zero to three (in infants) or four (in adults and non-human animals). Unlike the approximate number system, which is not precise and provides only an estimation of the number, the parallel individuation system is an exact system and encodes the exact numerical identity of the individual items (Hyde 2011). The parallel individuation system has been attested in human adults (of course ;) but also in non-human animals (Hyde 2011), see examples below.
Nevertheless, it is hard to examine in animals. Animals first need to be trained long enough to associate numbers with a particular quantitative representation - this is usually attributed to simple associative learning rather than an accurate understanding of numbers. Then, to provide conclusive evidence for the existence of PIS in animals, researchers need to find a situation in which individuals are performing some kind of arithmetic calculation. The closest to get that was (Jordan et al. 2005) who studied the issue in rhesus monkey. In this study, the monkeys were be able to associate auditory stimuli of a certain number of single vocalisations with the correct number of individuals. This did not require them to learn Arabic numerals, but it did require the ability to select the exact number for the number of voices they heard, rather than simply comparing quantities by sight or within a sequence.
Faces and Voices during Concur- rent Coo Vocalizations (A) Still frames extracted from a stimulus set used in Experiment 1. (B) Coo vocalizations are tonal, harmonically rich calls produced in affiliative contexts. The first panel shows the time-amplitude waveforms of the coo vocalizations from the individuals depicted in (A). The second panel shows the distinct but overlapping power spectra of the concurrent coos. From (Jordan et al. 2005)
Parallel individuation system was also demonstrated in an experiment on fish. Guppies were tested on their preference of social groups of different size, under the assumption that they have a preference for bigger size groups. In this experiment, fish successfully discriminated between numbers from 1 to 4 but after this number their performance decreased (Agrillo et al 2012). Not all studies find confirmation of PIS, however, for example New Zeland robins showed no difference in their understanding of small (1 to 4) and larger (above 4) amounts (Hunt et al. 2008).
More like an anegdote, it is worth to mention here an example of cormorants, that are believed to be able to count up to 7. This is based on the report of (Egremont and Rothschild 1979) that refers to Chinese tradition of fishing with cormorants.
The report says that: “After each cormorant had caught seven fish — and no bird was allowed to return unsuccessfully to its perch — the knots holding their neck bands were loosened and the birds were rewarded by being allowed to fish for themselves. The eighth fish was by long tradition the cormorant’s fish. The procedure must have been followed faithfully in this particular region for decades, for V. Wyndham-Quin had made careful and more extensive observations of the same phenomenon in 1914. Once these birds have retrieved their tally of seven fish (or to put it more precisely, seven successful sallies have been completed) they stubbornly refuse to move again until their neck ring is loosened. They ignore an order to dive and even resist a rough push or knock, sitting glumly and motionless on their perches……… One is forced to conclude that these highly intelligent birds can count up to seven.” (Egremont and Rothschild 1979)
Cormorant fishing is a traditional fishing method in which fishermen use trained cormorants to fish in rivers/lakes. Historically, cormorant fishing has taken place in Japan and Chine as well as Greece, N Macedona, and, briefly, England and France; currently under threat only in China. To control the birds, the fishermen tie a snare near the base of the bird’s throat. This prevents the birds from swallowing larger fish, which are held in their throat, but the birds can swallow smaller fish. When a cormorant has caught a fish in its throat, the fisherman brings the bird back to the boat and has the bird spit the fish up. Though cormorant fishing once was a successful industry, its primary use today is to serve the tourism industry.
Subitizing
Subitizing (lat. subito - suddenly) - quick and effortless determination of the number of sets up to 4 elements, connected with non-linguistic and non-arithmetic perception of small sets. Most people are not able to immediately (“at a glance”) assess precisely whether there are e.g. 9, 10 or 11 elements in a given set, but they are able to assess whether there are 2, 3 or 4 elements.
Subitizing in humans. It is probably a genetically transmitted skill that occurs in humans from birth. Infants pay attention to the change in number of sets 2 and 3 elements (i.e. infants looked longer when a 2-element set was replaced by a 3-element set or vice versa than when a 2- (or 3-) element set was replaced by another set of the same number). However, similar effects were not observed when the visual stimulus was changed from a 4-element set to a 6-element set (Rutkowski 1980).
Subitizing has also influenced the development of numerals in many different languages. Since the natural, innate human ability is to determine the number of sets of up to 4 elements, the numerals from 1 to 4 in many languages have a clearly different structure from the numerals from 5 upwards - 1-4 belong to the basic lexis of the language, from 5 upwards they function according to clearly different principles. In some languages, numerals above 4 have not developed at all (e.g. New Guinea, Amazon): there are numerals from 1 to 4, and all numbers above 4 are defined as “a lot”.
Subitizing is not limited to visual perception, but also extends to tactile perception (observers had to list the number of fingertips stimulated) and auditory perception. A few controversies, but in general it is known that the three modules function similarly (Katzin et al 2019)
Subitizing in primates is evident in many experiments. Rhesus monkeys have been shown to be able to distinguish the number of apples in a container even when the sizes of apple slices were manipulated (some larger, but fewer slices). Although this can be attributed to PIS, the act of comparing groups of small numbers suggests that this is subitizing after all, especially as the experiment broke down when the numbers reached above four (Hauser et al. 2000).
The results from experiment 1. Fifteen subjects were run in each condition in experiment 1 and the y-axis plots the number of subjects picking the larger (striped bars) over the smaller number (black bars) of apple slices. Statistical signi¢cance was tested with a one-tailed sign test, with signi¢cance set at the p50.05 level. Condition A involved the presentation of one slice of apple (one-eighth of an apple; F?food) versus one rock (NF?non-food). All other conditions in experiment 1 involved the presentation of di¡erent food quantities, some sets di¡ering byonlyone apple slice (conditions A^F), while others di¡ered by as much as two or more times the quantityin the other box (conditions G^J). All quantities were presented sequentially. From: (Hauser et al. 2000).
Sequence ordering/ordinality
Sequence ordering/ordinality - the ability to recognise consecutive symbols or quantities. Instead of simply determining whether a value is greater or less than another, as in ANS, ordinality requires more sophisticated recognition of a specific order of numbers or elements in a set. In this case, Weber’s law no longer applies because values only increase incrementally, often by only one.
Primates (again;) showed ordering for both arrays of items and Arabic numerals. When presented with arrays of 1-4 items, rhesus macaques were able to consistently touch the arrays in ascending order. After this test, they were presented with arrays containing a larger number of items and were able to extrapolate the task by touching new arrays in ascending order as well. Moreover, the rate at which the monkeys performed this task was the same as in adults (Brannon and Terrace 2000).
One experiment, known colloquially as the ‘chimpanzee challenge’ involved teaching chimpanzees to remember the correct order of Arabic numerals from 1 to 9, and then to press them in that order as they disappeared scattered across the screen. Not only were the chimps able to recognise the correct order of the scattered digits, but they were also able to recall the correct order after the digits disappeared from the screen. What’s more, they were able to do this faster and more accurately than adult humans. Without a visual representation of the quantity a digit represented, this task implied a more advanced cognitive ability - distinguishing symbols based on how they related to each other in a series, see the video
Another good example is Alex (see the video), who understood not only the numbers but also the zero concept (see below) (Pepperberg and Gordon 2005).
Alex (May 1976 – 6 September 2007) was a male grey parrot and the subject of a thirty-year experiment by animal psychologist Irene Pepperberg (University of Arizona/ Harvard University and Brandeis University). When Alex was about one year old, Pepperberg bought him at a pet shop. The name Alex was an acronym for avian language experiment, or avian learning experiment. Alex was reported to show the intelligence of a five-year-old human in some respects.
Zero concept
Although now zero/null seems to be obvious thing, as a matter of fact it is not that straightforward. It is kind of abstraction that even for human being had to be defined. Recognition that some animals display awareness of the concept of zero leads to the conclusion that the capability for numerical abstraction arose early in the evolution of species.
Four stages of understanding:
Stage One: Understanding zero as the absence of something, such as the absence of food on a plate. This first level is probably switched on early in visual processing.
Stage Two: Understanding zero as “nothing” vs. “something”, such as the presence or absence of light in a room. “Nothing” is thus treated as a meaningful behavioural category.
Stage Three: Understanding that zero can have a numerical value and belongs to the lower end of the positive number line. For example: 0 < 1 < 2 < 3 etc. (where < means “less than”).
Stage Four: Understanding that zero can be assigned a symbolic representation that can be used in modern mathematics and calculations, for example: 1 - 1 = 0.
Apart from aforementioned Alex example (Pepperberg and Gordon 2005), it is good to present here the experiments on bees (video and references: Horward et al 2018.
Problem solving
We have already touch this topic while mentioning the learning through insight (see the previous lecture #4). As a matter of fact the insight is not only learning but also solving a problem.
Problem solving - a set of activities engaged in to achieve a given goal. It can be performed in various ways (e.g. by trial and error), sometimes quite specific if based on insight, the latter requires appropriate processing of information. There is strong evidence that the neocortex (an evolutionarily ‘new’ part of the brain), and in particular the frontal lobes, play an important role in this kind of process (Alvarez and Emory, 2006), so those species that have a neocortex may use such a mechanism. There is some debate, however, as to the cross-species nomenclature for neocortex. In birds, for instance, there are clear examples of problem solving with high-ordered cognitive abilities (that are thought to be neocortical in nature), despite the lack of the distinctive six-layer neocortical structure. In a similar manner, reptiles, such as turtles, have primary sensory cortices.
There are few types of solution, or in other words various ways to solve the problem (i.e. there is always more than one way to skin a cat ;)
Cause and effects analysis
A careful analysis of the situation, with understanding of cause and effects lead to a solution of the task, e.g. Caledonian crows and Aesop fable paradigm (experiment, see the video and references: Jelbert et al 2015).
Aesop fable: The Crow and the pitcher - the story concerns a thirsty crow that comes upon a pitcher with water at the bottom, beyond the reach of its beak. After failing to push it over, the bird drops in pebbles one by one until the water rises to the top of the pitcher, allowing it to drink. The most often provided moral of the story emphasises the virtue of ingenuity (thoughtfulness is superior to brute strength). Other interpretations of the moral stress the crow’s persistence.
Innovation
Solution with innovation - coming up with something “out of the ordinary”. Could be happening simply by chance but also based on trial/errors and/or insight, e.g. Barbadian finches living in a city are better at solving problems than their wild counterparts (probably due to experiencing “non-standard” circumstances associated with city life; see the video and references: Audet et al. 2016.
Problem solving. In both problem-solving tasks, the latency to succeed is lower in urban individuals compared with their rural counterparts (lid-drawer P = 0.0338; tunnel P = 0.0206). From: Audet et al. 2016
Tool usage
Using too of any kind in order to achieve a goal (acquiring food and water, grooming, defense, communication, recreation or construction). Originally thought to be a skill possessed only by humans but it has been already demonstrated that a wide range of animals, including mammals, birds, fish, cephalopods, and insects, do use tools. Tools used by animals may be used directly and indirectly, some tools (including their creation) require a sophisticated level of cognition.
This is truely amazing cognitive performance of animals. Some animals go beyong that, and even make their tool on their own. It has been demonstrated both, for instance that Caledonian crows can do that (e.g. Bayern et al. 2018), and this is true both laboratory conditions (video) and in the wild (video).
Since this kind of relevance on animal behaviour are very exciting, new evidence on tool usage are getting lot of attention (both of researchers/experts of the area and public), and they are often published in high-ranked scientific journals. Pretty recent report of seabirds supposedly using a stick to scratch themselves has got lot of interest (Fayet et al. 2020); see the videos attached to the paper (video1, video2, video3) Unfortunately, the finding (both its relevance and the way it was reported) has been heavily questioned (Auersperg et al. 2020; Fayet et al. response to Auersperg et al 2020 concerns: Fayet et al. 2020b; Sándor and Miklósi 2020; von Bayern et al. 2020). This specific case demonstrates how carefull we should be while interpreting animal behaviour (especially when looking at cognition)!
Concepts and categories
Concepts enable humans and animals to organise the world into functional groups; these groups may consist of perceptually similar objects or events, different things that share a common function, relations such as same vs different, or relations between relations such as analogies.
As often in studies on animal cognition methodology is complicated, and challenging. Most work are based on visual stimuli (easy to construct and manipulate), but there are also works with auditory and other stimuli. The most common species examined are birds (because excellent vision and easily conditioned to respond to visual targets, e.g. pigeons), but other animals are also considered.
In a typical vision-based experiment, a bird or other animal stands in front of a computer monitor on which a large number of pictures appear sequentially, and the test subject is rewarded for pecking or touching a picture depicting an item in a particular category and is not rewarded for items not in that category. Alternatively, the subject may be offered a choice between two or more pictures. Many experiments end with the presentation of items that have never been seen before; successful sorting of these items shows that the animal has not simply learned many specific stimulus-response associations but makes some kind of mental categories.
Using this kind of methodology, Levenson et al. (2015) demonstrated that pigeons were able to distinguish benign from malignant human breast histopathology images. Importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. Obviously, the experiment was performed after appropriate training with differential food reinforcement. As a matter of fact the training was very much important here, as birds’ histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Anyway and overall, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images.
An alternative method, sometimes used to study relational concepts, is pattern matching. In this task, the animal sees one object and then chooses between two or more alternatives, one of which is the same as the object presented initially; the animal is then rewarded for choosing the matching stimulus.
Perceptual categorisation is said to occur when a person or animal reacts in a similar way to a number of stimuli that share common characteristics. For example, a squirrel climbs a tree when it sees Rex, Shep or Trixie, suggesting that it categorises all three as something to be avoided. This sorting of cases into groups is crucial for survival. Among other things, an animal must categorise if it is to apply learning about one object (e.g. Rex bit me) to new instances of that category (dogs can bite).
Many animals readily classify objects based on perceived differences in shape or colour. For example, bees or pigeons quickly learn to select any red object and reject any green object if red leads to a reward and green does not (e.g. see the aforementioned experiments with bees being trained for examining the concept of zero).
In a classic study, Herrnstein and Loveland (1964) trained pigeons to respond to the presence or absence of humans in photographs. The birds easily learned to peck photos that contained a partial or full view of a human and to avoid pecking photos without humans, despite the vast differences in form, size and colour of both humans and photos without humans.
In more recent study researchers found that, as nest visits were repeated,the brown skua (Stercorarius antarcticus; an Antarctic species), parents responded at further distances and were more likely to attack the nest intruder. Also, we demonstrated that seven out of seven breeding pairs of skuas selectively responded to a human nest intruder with aggression and ignored a neutral human who had not previously approached the nest (video). The results indicate that Antarctic skuas, a species that typically inhabited in human-free areas, are able to recognize individual humans who disturbed their nests (Lee et al. 2016). This findings generally support the high cognitive abilities hypothesis.
Response distance of skuas to the approaching humans (black dots and a linear line; average ± SE) increased with the number of nest visit (a) and probability of being attacked (binary coded as ‘‘0’’ or ‘‘1’’), involving physical threatening on human head, increased (gray bars from seven nests were observed values and black dots were predicted values) with the number of nest visit (b). Experimental design to test the discriminatory ability (c): a pair of humans in the discrimination experiments with (neutral human and nest intruder) and the faces of neutral humans (WY and JW) and nest intruders (YD and SH). Two examples of the experiments on skuas (red circles) were presented (d). In total seven trials (one trial in each nest), all seven skua pairs consistently followed the intruders and exhibited aggression (color figure online). From Lee et al. 2016.
This experiment is particularly interesting given human difficulties in recognition of faces in different human races (known as own-race bias, ORB).
Own-race bias (alternative terms: the cross-race , cross-race bias, other-race bias, other-race effect) is the tendency to more easily recognize faces that belong to one’s own racial group. In social psychology, the cross-race effect is described as the “in-group advantage,” whereas in other fields, the effect can be seen as a specific form of the “in-group advantage” since it is only applied in interracial or inter-ethnic situations. The cross-race effect is thought to contribute to difficulties in cross-race identification, as well as implicit racial bias. Recent studies suggest that the level of exposure to other-race faces accounts for only a small part of ORB. In addition, the present results also support the notion that different neural mechanisms may be involved in processing own- and other-race faces, with internal features of own-race faces being processed more effectively, whereas external features dominate representations of other-race faces (Wong et al. 2020).
Natural categories
Again, what we consier to be straightforward is not that obvious when we try to recognize the issue in animals. A good example of that is the study of (Avarguès-Weber et al. 2011) on bees. In the set of two experiments researchers were investigating whether an insect, the honeybee (Apis mellifera), can form a conceptual representation of an above/below spatial relationship. In experiment 1, bees were trained with differential conditioning to choose a variable target located above or below a black bar that acted as constant referent throughout the experiment. In experiment 2, two visual stimuli were aligned vertically, one being the referent, which was kept constant throughout the experiment, and the other the target, which was variable. In both experiments, the distance between the target and the referent, and their location within the visual field was systematically varied. In both cases, bees succeeded in transferring the learned concept to novel stimuli, preserving the trained spatial relation, thus showing an ability to manipulate this relational concept independently of the physical nature of the stimuli. Absolute location of the referent into the visual field was not a low-level cue used by the bees to solve the task. The honeybee is thus capable of conceptual learning despite having a miniature brain, showing that such elaborated learning form is not a prerogative of vertebrates (Avarguès-Weber et al. 2011).
Example of the conditioning and testing procedure. Half of the bees were rewarded on the ‘target above bar’ relation whereas the other half was rewarded on the ‘target below bar’ relation. The transfer test was not rewarded. From: Avarguès-Weber et al. 2011.
Functional or associative categories
Perceptually unrelated stimuli can be responded to as elements of a single class if they have a common use or lead to common consequences. An example is the often-cited study of Vaughan (1988). Vaughan divided a large set of unrelated pictures into two arbitrary sets, A and B. The pigeons were given food for pecking the pictures in set A, but not for pecking the pictures in set B. After they had learned the task reasonably well, the settings were reversed: the items in set B led to food, while the items in set A did not. Then the settings were reversed again, and then again, and so on. Vaughan found that after 20 or more reversals, associating a reward with several pictures in one set caused the birds to respond to other pictures in that set without further reward, as if they thought “if these pictures in set A bring food, then the others in set A must also bring food”. That is, the birds now categorised the pictures in each set as functionally equivalent.
Rule learning
Rule application has sometimes been thought of as a skill reserved for humans, but again many experiments have shown evidence of simple rule learning in primates, as well as in other animals. Studies show that a ‘rule’ consists of the order in which a series of events occur. The use of a rule is evident if the animal learns to distinguish between different orders of events and to transfer this distinguishability to new events arranged in the same order.
For example, Murphy et al. (2008) trained rats to discriminate visual sequences. One group was rewarded with ABA and BAB, where A=“bright light” and B=“dim light”. Other trioles of stimuli were not rewarded. Rats learned the visual sequence, although both bright and dim light were equally associated with reward.
Similar sequence learning has also been demonstrated in birds and other animals. In two experiments, domesticated horses (Equus callabus) and chickens (Gallus domesticus) showed the same propensity, rule learning (based on categorizations). In both experiments, subjects encountered either a structured or unstructured sequence of food quantities in a runway paradigm. In both experiments, subjects exposed to structured patterns of food quantities learned to track sequences of food quantities more efficiently than those exposed to patterns lacking such structure by running fast for large food quantities and slowly for small food quantities. These results provide evidence that horses and chickens track simple sequences similarly to humans and rats (Kundey et al. 2010).
Runaway paradigm – one of the research methods to study animals strength of motivation to do something. Time needed to run away from the situation, and or the speed of running away is measured . An alternative is choice tests, when an animal go for the preferred option.
Decision making
Different strategies to make a decision have been developed during the evolutionary process (to eat, reproduce, rest, etc), and they are modified by the animal’s experience, and cognitive bias (Harding et al 2004).
Cognitive bias - cognitive bias refers to a systematic pattern of deviation from the norm or rationality in judgement, whereby conclusions about other people or situations may be drawn in an illogical way. Cognitive bias is sometimes illustrated by the answer to the question “Is the glass half empty or half full?” Choosing the answer “half empty” is supposed to indicate pessimism, while choosing “half full” indicates optimism.
There is some evidence of cognitive bias in many species, including rats, dogs, rhesus macaques, sheep, starlings and honeybees. In one of the first studies examining the issue, the rats that were considered. Researchers found that those tickled during routine control responded differently to rats that were not so treated - those tickled were more optimistic while making their decision (Rygula et al. 2012).
Specifically, at the beginning of the experiment the rats were trained in operant Skinner boxes to press one lever in response to one tone to receive a food reward and to press another lever in response to a different tone to avoid punishment by electric foot shock. After attaining a stable level of discrimination performance, the animals were subjected to either handling or playful, experimenter-administered manual stimulation – tickling. This procedure has been confirmed to induce a positive affective state in rats, and the 50-kHz ultrasonic vocalisations (rat laughter) emitted by animals in response to tickling have been postulated to index positive emotions akin to human joy. During the tickling and handling sessions, the numbers of emitted high-frequency 50-kHz calls were scored. Immediately after tickling or handling, the animals were tested for their responses to a tone of intermediate frequency, and the pattern of their responses to this ambiguous cue was taken as an indicator of the animals’ optimism. This findings indicate that tickling induced positive emotions which are directly indexed in rats by laughter, can make animals more optimistic (Rygula et al. 2012). This is one of the first evidence for a relationship between directly measured positive affective state and decision-making under uncertainty, in an animal model.
Theory of mind
Theory of mind in animals is an extension to non-human animals of the philosophical and psychological concept of thery of mind (ToM), sometimes known as mentalisation or mind-reading. It involves an inquiry into whether animals have the ability to attribute mental states (such as intention, desires, pretending, knowledge) to themselves and others, including recognition that others have mental states that are different from their own. To investigate this issue experimentally, researchers place animals in situations where their resulting behaviour can be interpreted as supporting ToM or not, as you can imagine is a hellishly difficult taks.
The existence of theory of mind in animals is controversial. On the one hand, one hypothesis proposes that some animals have complex cognitive processes which allow them to attribute mental states to other individuals, sometimes called “mind-reading”. A second, more parsimonious, hypothesis proposes that animals lack these skills and that they depend instead on more simple learning processes such as associate learning (lecture #4) or in other words, they are simply behaviour-reading.
Several studies have been designed specifically to test whether animals possess theory of mind by using interspecific or intraspecific communication. Several taxa have been tested including primates, birds and canines. Positive results have been found; however, these are often qualified as showing only low-grade ToM, or rejected as not convincing by other researchers.
Specific categories of behaviour are sometimes used as evidence of animal ToM, including imitation, self-recognition, social relationships, deception, role-taking (empathy) perspective-taking, teaching and co-operation (Towner 2010), however, there is no full agreement on that . Some researchers focus on animals’ understanding of intention, gaze, perspective, or knowledge, i.e. what another being has seen. Several experimental methods have been developed which are widely used or suggested as appropriate tests for nonhuman animals possessing ToM. Some studies look at communication between individuals of the same species (intraspecific) whereas others investigate behaviour between individuals of different species interspecific.
Knower-Guesser
The Knower-Guesser method has been used in many studies relating to animal ToM. Animals are tested in a two-stage procedure. At the beginning of each trial in the first discrimination training stage, an animal is in a room with two humans. One human, designated the “Guesser,” leaves the room, and the other, the “Knower,” baits one of several containers. The containers are screened so that the animal can see who does the baiting, but not where the food has been placed. After baiting, the Guesser returns to the room, the screen is removed, and each human points directly at a container. The Knower points at the baited container, and the Guesser at one of the other containers, chosen at random. The animal is allowed to search one container and to keep the food if it is found.
This methods was recently applied in a study on the topic with dogs. Domestic dogs (Canis familiaris) with human informants are an ideal model for this kind of studies, because they show high sensitivity towards human eye contact, they have proven able to assess the attentional state of humans in food-stealing or food-begging contexts, and they follow human gaze behind a barrier when searching for food. With 16 dogs, reserachers found that dogs preferred to follow the pointing of a human who witnessed a food hiding event over a human who did not (the Guesser–Knower task). They also extended this finding with a further, critical control for behaviour-reading: two informants showed identical looking behaviour, but due to their different position in the room, only one had the opportunity to see where the food was hidden by a third person. Preference for the Knower in this critical test provides solid evidence for geometrical gaze following and perspective taking in dogs (Catala et al. 2017).
Average percentage of choice responses made to the Knower per block of four trials in the three tests (GP, GA and GLA). The dashed line indicates chance responding. Bars indicate one standard error (SE). From Catala et al. 2017
Geometrical gaze – a basis of perception of gaze direction; many social and cognitive functions in animals (and humans) depend on the ability to quickly and accurately judge another individual’s direction of gaze (Todorović 2006). This is widely studied for humans, and for that several effects have been recognized; the effects are basically illusions of particular gaze, and so should be considered in animals studies to avoid an incorrect interpretation of animal behaviour.
Mona Lisa effect is the illusion that the subject of a painting follows you with her gaze, despite where you stand. But da Vinci’s famous painting doesn’t have that quality.
Wollaston effect - the direction of a person’s gaze is determined by integrating local information from the eyes with the rotation of the head. This interaction produces a striking illusion, known as the Wollaston effect, where a person’s gaze is pulled in the direction of the head’s rotation.
Mirror gaze effect – only recently described; involves a portrait that gazes at the observer, which is reflected in a mirror. Setting up such an arrangement, the mirror image of such a portrait will also gaze at the observer. This outcome involves an apparent paradox, for the following reason: if gaze direction were embodied by a real physical object, say a straight wire running between the portrait’s bridge of the nose and the observer’s bridge of the nose, then the mirror image of the wire would run between the bridge of the nose of the mirror image of the portrait and the bridge of the nose of the mirror image of the observer. Thus if perceived gaze direction would behave like an ordinary physical direction, then the gaze of the mirror image of the portrait would be directed at the mirror image of the observer.
Competitive feeding paradigm
The competitive feeding paradigm approach is considered by some as evidence that animals have some understanding of the relationship between “seeing” and “knowing”.
At the beginning of each trial in the paradigm, a subordinate animal (the individual thought to be doing the mind-reading) and a dominant animal are kept on opposite sides of a test arena which contains two visual barriers. In all trials, a researcher enters the enclosure and places food on the subordinate’s side of one of the visual barriers (one baiting event), and in some trials the researcher re-enters the enclosure several seconds later and moves the food to the subordinate’s side of the other visual barrier (second baiting event). The door to the subordinate’s cage is open during any baiting by the researcher. The conditions vary according to whether the dominant’s door is open or closed during the baiting events, and therefore whether the subordinate individual can see the dominant. After baiting, both of the animals are released into the test arena, with the subordinate being released several seconds before the dominant. If the animals possess ToM, it is expected that subordinates are more likely to gain the food, and more likely to approach the food under several circumstances: (1) When the dominant’s door is closed during trials with a single baiting event; (2) when the dominant’s door is open during a first baiting event but closed during a second; (3) in single baiting event trials with the dominant’s door open, subordinates are more likely to get the food when they compete at the end of the trial with a dominant individual who did not see the baiting (Heyes 2015).
Goggles Method
In one suggested protocol, chimpanzees are given first-hand experience of wearing two mirrored visors. One of the visors is transparent whereas the other is not. The visors themselves are of markedly different colours or shapes. During the subsequent test session, the chimpanzees are given the opportunity to use their species-typical begging behaviour to request food from one of the two humans, one wearing the transparent visor and the other wearing the opaque. If chimpanzees possess ToM, it would be expected they would beg more often from the human wearing the transparent visor.
False Belief Test
A method used to test ToM in human children has been adapted for testing non-human animals. The basis of the test is to track the gaze of the animal. One human hides an object in view of a second human who then leaves the room. The object is then removed. The second human returns whereupon they will mistakenly look for the object where they last saw it. If the animal stares first and longest at the location where the human last saw the object, this suggests they expect him to believe it is still hidden in that place (see the video).
Biological constraints
Animals vary greatly in their cognitive and learning abilities, reflecting their evolutionary history and instinctive behaviour in the wild.
For example, dogs and rats easily learn to avoid being electrocuted from the floor by moving to another part of the experimental chamber when they hear a sound preceding the shock; this is an appropriate response to a dangerous situation. Hedgehogs instinctively curl into a ball when threatened. This might seem to indicate the hedgehog’s inability to learn, but the hedgehog’s instinctive response to danger is to curl up into a ball, a reaction that interferes with possible escape behaviour in this situation.
Instinctual drift is another factor that may affect the interpretation of cognitive research. Instinctual drift is the tendency of an animal to revert to instinctive behaviour that may interfere with learned responses. In other words, they will behave in accordance with evolutionary contingencies, as opposed to the operant contingencies of their training. These behaviors are often unnecessary, and seldom useful during the training for purpose of a study (Breland and Breland 1961). For example, a raccoon prairie dog taught to put coins in a box reverted to its instinctive behaviour of rubbing the coins with its paws, as it would do when foraging for food.
The ability of animals to process and respond to stimuli is correlated with brain size. Animals with small brains tend to have simple behaviours that are less dependent on learning than those with large brains. Vertebrates, especially mammals, have large brains and complex behaviours that change with experience. A formula called the encephalization quotient (EC) expresses the relationship between brain size and body size.
For mammals:
Ew(brain) = 0.12 * w(body)^2/3.
For some other classes of vertebrates a power of 3/4 rather than 2/3 is sometimes used, and for many groups of invertebrates the formula may not give meaningful results.
Encephalization quotient (EQ), encephalization level (EL), or just encephalization is a relative brain size measure that is defined as the ratio between observed to predicted brain mass for an animal of a given size, based on nonlinear regression on a range of reference species. It has been used as a proxy for intelligence and thus as a possible way of comparing the intelligences of different species. For this purpose it is a more refined measurement than the raw brain-to-body mass ratio, as it takes into account allometric effects. Expressed as a formula, the relationship has been developed for mammals and may not yield relevant results when applied outside this group.
Evolution of cognitive abilities
Research methods
The study of the evolution of cognition is done through a comparative-cognitive approach, in which cognitive ability is studied and compared between closely related and distantly related species. For example, a researcher may wish to examine the relationship between spatial memory and food hiding behaviour. By studying two closely related animals (cicadas and jays) and/or two distantly related animals (jays and squirrels), hypotheses can be made about when and how this cognitive ability evolved.
Higher cognitive processes have evolved in many closely and distantly related animals. Some of these examples are considered convergent evolution, while others most likely shared a common ancestor that possessed higher cognitive functions. For example, great apes and cetaceans most likely shared a common ancestor with a high level of cognitive ability, and as these species diverged they all possessed this trait. Corvids (the corvid family) and great apes show similar cognitive abilities in some areas, such as tool handling. This ability is most likely an example of convergent evolution, due to their distant relatedness.
Convergent evolution is the independent evolution of similar features in species of different periods or epochs in time. Convergent evolution creates analogous structures that have similar form or function but were not present in the last common ancestor of those groups. The opposite of convergence is divergent evolution, where related species evolve different traits. Convergent evolution is similar to parallel evolution, which occurs when two independent species evolve in the same direction and thus independently acquire similar characteristics.
Sex and relationships
Many animals have complex mating rituals that require higher levels of understanding to assess. Birds are well known for their intense mating displays, including swan dances that can last for hours or even days (lecture #2). They are often coordinated with each other.
Higher levels of cognition may have evolved to facilitate longer lasting relationships. Animals that form pairs and share parental responsibilities produce offspring that are more likely to survive and reproduce, increasing the fitness of these individuals (lecture #2). Cognitive requirements for this type of mating include the ability to distinguish individuals from their group and resolve social conflicts.
Cognitive abilities may be as much important in fitness as any other trait. In hummingbirds, spatial memory (the ability to recall the position of a rewarding feeder) was considered in respect to possession of territory (which is a good proxy for males fitness). It turned out that apart body size and lift power, cognitive abilities (spatial memory) of individuals were also important. Interestingly, although cognitive abilities had a stronger effect on fitness. This findings lend support for cognition as a sexual selection target (Araya-Salas et al. 2018).
Three-dimensional surfaces of the predicted probability of territory ownership (z axis) for the combinations of the three traits that showed a significant effect. Probability of territory ownership was predicted based on the best mixed-effects model in the model selection procedure (two predictors at a time in the x and y axes). Values were predicted for all possible combinations of predictors (across 30 equally spaced values within the observed range) and random effect levels. From Araya-Salas et al. 2018.
Finding/caching food
Cognition has enabled individuals to access food and resources that were previously inaccessible. For example, a genetic mutation for colour vision has allowed greatly increased efficiency in finding and acquiring fruit. The behaviour of storing food in caches in some birds and mammals is an example of a behaviour that may have evolved along with higher cognitive processes. This ability to store food for later consumption allows these animals to take advantage of temporary surpluses in food availability. Corvids have shown an uncanny ability to create and remember the locations of up to hundreds of caches. For example, a jay caching food (video).
Corvids are prolific cachers (or hoarders), burying food such as acorns in several thousand locations over the course of a year. When food becomes scarce during winter and spring, they remember where they buried their caches and retrieve the food items. However, pilfering of caches is commonplace. As a result, they are often trying to minimize other birds stealing their food and maximize the food that they steal. In the first experiment, shown in this video, the researchers gave the jay options to hide food in substrates which varied in the amount of noise they made (a tray containing noisy gravel and a tray containing quiet sand). The bird’s preferences for using these different substrates were tested when they were alone, when they had another bird that could see and hear them and when there was another bird that could hear but could not see them. The researchers found that if a Eurasian jay is caching and hears but does not see another bird nearby it will hide its cache in the less noisy substrate (for this study, sand rather than gravel). This is presumably done to avoid drawing unwanted attention from potential thieves that might then try to view the location of the cache (from: Cambridge University youtube channel) (video).
Importantly, there is evidence that this is not just instinctive behaviour, but an example of future planning. Jays hid a variety of food probably understanding the need for dietary diversity (de Kort et al. 2005).
Some suggest that the higher cognitive processes involved in acquiring food require a large brain, which in turn requires a large metabolic input. These two processes (greater access to food and increasing brain energy requirements) may have caused the evolution of these two traits.
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Social life
It is believed that social life co-evolved with higher cognitive processes. There is a hypothesis that higher cognitive functions have evolved to mitigate the negative effects of living in social groups. For example, the ability to recognise individual group members could solve the problem of cheating. If individuals in a group can track cheaters, they can punish them or exclude them from the group. There is also a positive correlation between relative brain size and aspects of sociality in some species. There are many benefits of living in social groups, such as division of labour and protection, but animals must have high levels of cognition to reap these benefits (Dunbar and Shultz 2007; Emery et al. 2007).