Individual variation in cognitive performance: developmental and evolutionary perspectives

Thornton, Alex; Lukas, Dieter

doi:10.1098/rstb.2012.0214

Cited by 284 publications

(242 citation statements)

References 76 publications

(87 reference statements)

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“…Further tests with either aversive stimuli, or the use of multiple unreinforced stimuli during training are needed to confirm current results. Extant long-term datasets on cognitive abilities in animals may be sufficient to allow for cursory analyses of individual differences in cognitive abilities, when the same subjects have been used in multiple cognitive tests (Griffin et al, 2015;Thornton and Lukas, 2012;Vonk and Povinelli, 2011). Table 1.…”

Section: Discussionmentioning

confidence: 99%

Fast- and slow-exploring pigeons differ in how they use previously learned rules

Guillette

Baron

Sturdy

2017

Behavioural Processes

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Please cite this article as: Guillette, L.M., Baron, D.M., Sturdy, C.B., Spetch, M.L., Fastand slow-exploring pigeons differ in how they use previously learned rules.Behavioural Processes http://dx.doi.org/10. 1016/j.beproc.2016.07.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractSeveral studies report a correlation between exploratory behaviour and performance on tests of cognitive ability. Exploration may influence learning because less exploratory animals are less likely to come in contact with to-be-learned stimuli. Alternatively, the way information available in the environment is processed could influence the rate of exploration. Pigeons are one of the most-studied species used to examine the mechanisms underlying cognitive abilities, but have not been used to examine the relationship between these abilities and animal personality. Here, twelve pigeons were first tested in a novel environment to assess repeatability in exploratory behaviour. Pigeons were then trained to discriminate between two visual stimuli: lines oriented at 90° (vertical, the S+) and 135° (the S-). After training pigeons underwent generalization testing with ten additional visual line orientation stimuli. We found exploratory behaviour was related to generalization performance: fast-explorers had steeper generalization gradients compared to slow-explorers. This effect was only seen in the direction towards the S-. These results suggest that birds with different exploratory styles differ in how they use previously learned information. Further testing is needed to confirm which cue(s) (S+ or S-) control the behaviour of fast-explorers.

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Section: Discussionmentioning

confidence: 99%

Fast- and slow-exploring pigeons differ in how they use previously learned rules

Guillette

Baron

Sturdy

2017

Behavioural Processes

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“…To study social and cognitive influences on tool use, developmental approaches and fitness outcomes, thus, may be more fruitful than the focus on geographical differences. Only then we can begin to unravel how the ability for tool use evolved and relates to cognitive evolution and cumulative technology and culture (see also [95]). …”

Section: Discussionmentioning

confidence: 99%

If at first you don't succeed… Studies of ontogeny shed light on the cognitive demands of habitual tool use

Meulman

Seed

Mann

2013

Phil. Trans. R. Soc. B

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Many species use tools, but the mechanisms underpinning the behaviour differ between species and even among individuals within species, depending on the variants performed. When considering tool use ‘as adaptation’, an important first step is to understand the contribution made by fixed phenotypes as compared to flexible mechanisms, for instance learning. Social learning of tool use is sometimes inferred based on variation between populations of the same species but this approach is questionable. Specifically, alternative explanations cannot be ruled out because population differences are also driven by genetic and/or environmental factors. To better understand the mechanisms underlying routine but non-universal (i.e. habitual) tool use, we suggest focusing on the ontogeny of tool use and individual variation within populations. For example, if tool-using competence emerges late during ontogeny and improves with practice or varies with exposure to social cues, then a role for learning can be inferred. Experimental studies help identify the cognitive and developmental mechanisms used when tools are used to solve problems. The mechanisms underlying the route to tool-use acquisition have important consequences for our understanding of the accumulation in technological skill complexity over the life course of an individual, across generations and over evolutionary time.

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“…While associative learning seems to be a likely cognitive process contributing to individual variation in innovative problem-solving success [14 -18], identifying all of the cognitive components underlying innovation and its proxies remains a major challenge [17,19 -21]. At the same time, considerable debate surrounds whether and how individual differences in various cognitive abilities might ever be measured in evolutionary ecological studies, given the enormous difficulty associated with controlling for factors such as motivation, and given that some genes have pleiotropic effects on cognitive and purportedly non-cognitive traits [22][23][24][25]. A major challenge is to understand the adaptive significance of innovativeness and indeed of its underlying components [7].…”

Section: Introductionmentioning

confidence: 99%

Environmental and genetic determinants of innovativeness in a natural population of birds

Quinn

Cole

Reed

et al. 2016

Phil. Trans. R. Soc. B

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One contribution of 15 to a theme issue 'Innovation in animals and humans: understanding the origins and development of novel and creative behaviour'. Much of the evidence for the idea that individuals differ in their propensity to innovate and solve new problems has come from studies on captive primates. Increasingly, behavioural ecologists are studying innovativeness in wild populations, and uncovering links with functional behaviour and fitnessrelated traits. The relative importance of genetic and environmental factors in driving this variation, however, remains unknown. Here, we present the results of the first large-scale study to examine a range of causal factors underlying innovative problem-solving performance (PSP) among 831 great tits (Parus major) temporarily taken into captivity. Analyses show that PSP in this population: (i) was linked to a variety of individual factors, including age, personality and natal origin (immigrant or local-born); (ii) was influenced by natal environment, because individuals had a lower PSP when born in poor-quality habitat, or where local population density was high, leading to cohort effects. Links with many of the individual and environmental factors were present only in some years. In addition, PSP (iii) had little or no measurable heritability, as estimated by a Bayesian animal model; and (iv) was not influenced by maternal effects. Despite previous reports of links between PSP and a range of functional traits in this population, the analyses here suggest that innovativeness had weak if any evolutionary potential. Instead most individual variation was caused by phenotypic plasticity driven by links with other behavioural traits and by environmentally mediated developmental stress. Heritability estimates are population, time and context specific, however, and more studies are needed to determine the generality of these effects. Our results shed light on the causes of innovativeness within populations, and add to the debate on the relative importance of genetic and environmental factors in driving phenotypic variation within populations.

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Individual variation in cognitive performance: developmental and evolutionary perspectives

Cited by 284 publications

References 76 publications

Fast- and slow-exploring pigeons differ in how they use previously learned rules

Fast- and slow-exploring pigeons differ in how they use previously learned rules

If at first you don't succeed… Studies of ontogeny shed light on the cognitive demands of habitual tool use

Environmental and genetic determinants of innovativeness in a natural population of birds

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