Evidence in Default: Rejecting Default Models of Animal Minds

Dacey, Mike

doi:10.1086/714799

“…For example, Heyes (2012) argues that one should not treat associative mechanisms as the "default winner" over cognitive mechanisms (p. 2695). Later work also echoes this sentiment (Andrews & Huss, 2014;Mikhalevich, 2015;Bausman & Halina, 2018;Dacey, 2021;Starzak & Gray, 2021;Halina, 2022).…”

Section: The Associative-cognitive Distinctionmentioning

confidence: 78%

Morgan’s Canon and the Associative-Cognitive Distinction Today: A Survey of Practitioners

Voudouris,

Farrar,

Cheke

et al. 2024

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Philosophers of science and mind have paid increasing attention to the field of comparative psychology. Two recurring points of discussion in the literature are the methodological value of Morgan's Canon and the distinction between associative and cognitive processes. Although the existing literature regularly makes claims about the beliefs and attitudes of comparative psychologists, there are few empirical studies verifying these claims. This paper fills this gap by presenting and analysing the views of over two hundred comparative psychologists on Morgan’s Canon and the associative-cognitive distinction. We found that while there is some agreement between the claims in the existing literature and the views of the practitioners surveyed here (e.g., that Morgan’s Canon is a parsimony principle), there are also surprising points of divergence (e.g., practitioners do not view the associative-cognitive distinction as a version of Morgan’s Canon). We intend for this study to inform existing philosophical and theoretical work on perennial questions regarding how to study animal minds and behaviour.

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“…Taking the first option, associationists and mentalists could -and sometimes do -appeal to a principle of parsimony or simplicity. This is unsatisfactory for many reasons unearthed in debate about Morgan's Canon (Dacey 2016(Dacey , 2021Fitzpatrick 2008Fitzpatrick , 2017. For example, there are a variety of different kinds of explanatory simplicity (ontological, iterative, phylogenetic, uniformity, ease of use), they are not highly correlated, and there is no wellestablished epistemic basis for prioritising one kind over another.…”

Section: Cognitive Complexity the Results Ofmentioning

confidence: 99%

Social learning in models and minds

Yon,

heyes

2024

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After more than a century in which social learning was blackboxed by evolutionary biologists, psychologists and economists, there is now a thriving industry in cognitive neuroscience producing computational models of learning from and about other agents. This is a hugely positive development. The tools of computational cognitive neuroscience are rigorous and precise. They have the potential to prise open the black box. However, we argue that, from the perspective of a scientific realist, these tools are not yet being applied in an optimal way. To fulfil their potential, the shiny new methods of cognitive neuroscience need to be better coordinated with old-fashioned, contrastive experimental designs. Inferences from model complexity to cognitive complexity, of the kind made by those who favour lean interpretations of behaviour (‘associationists’), require social learning to be tested in challenging task environments. Inferences from cognitive complexity to social specificity, made by those who favour rich interpretations (‘mentalists’), call for non-social control experiments. A parsimonious model that fits current data is a good start, but carefully designed experiments are needed to distinguish models that tell us how social learning could be done from those that tell us how it is really done.

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“…Taking the first option, associationists and mentalists could-and sometimes do-appeal to a principle of parsimony or simplicity. This is unsatisfactory for many reasons unearthed in debate about Morgan's Canon (Dacey, 2016(Dacey, , 2021Fitzpatrick, 2008Fitzpatrick, , 2017. For example, there are a variety of different kinds of explanatory simplicity (ontological, iterative, phylogenetic, uniformity, ease of use), they are not highly correlated, and there is no well-established epistemic basis for prioritising one kind over another.…”

Section: Model Complexity > Cognitive Complexity > Social Specificitymentioning

confidence: 99%

Social learning in models and minds

Yon,

Heyes

2024

Synthese

1

0

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After more than a century in which social learning was blackboxed by evolutionary biologists, psychologists and economists, there is now a thriving industry in cognitive neuroscience producing computational models of learning from and about other agents. This is a hugely positive development. The tools of computational cognitive neuroscience are rigorous and precise. They have the potential to prise open the black box. However, we argue that, from the perspective of a scientific realist, these tools are not yet being applied in an optimal way. To fulfil their potential, the shiny new methods of cognitive neuroscience need to be better coordinated with old-fashioned, contrastive experimental designs. Inferences from model complexity to cognitive complexity, of the kind made by those who favour lean interpretations of behaviour (‘associationists’), require social learning to be tested in challenging task environments. Inferences from cognitive complexity to social specificity, made by those who favour rich interpretations (‘mentalists’), call for non-social control experiments. A parsimonious model that fits current data is a good start, but carefully designed experiments are needed to distinguish models that tell us how social learning could be done from those that tell us how it is really done.

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Evidence in Default: Rejecting Default Models of Animal Minds

Cited by 6 publications

References 34 publications

Morgan’s Canon and the Associative-Cognitive Distinction Today: A Survey of Practitioners

Morgan’s Canon and the Associative-Cognitive Distinction Today: A Survey of Practitioners

Social learning in models and minds

Social learning in models and minds

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