The best of both worlds: Dual systems of reasoning in animals and AI

Kelly, Mark; Barron, Andrew B.

doi:10.1016/j.cognition.2022.105118

Cited by 8 publications

(6 citation statements)

References 88 publications

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“…Our model proposes a simple decision architecture that is capable of responding adaptively to the kinds of variable evidence and circumstances encountered in real-world situations. This type of model could prove of value in autonomous robotics applications ( de Croon et al, 2022 ; Kelly and Barron, 2022 ; Stankiewicz and Webb, 2021 ; Webb, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

How honey bees make fast and accurate decisions

MaBouDi

Marshall

Dearden

et al. 2023

eLife

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Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

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Section: Discussionmentioning

confidence: 99%

How honey bees make fast and accurate decisions

MaBouDi

Marshall

Dearden

et al. 2023

eLife

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“…Such a capacity would allow learning stimuli independent of the sensory background [Liu et al, 1999]. It would support both context-sensitive learning and context generalisations [Liu et al, 1999;Kelly and Barron, 2022]. Hence, processing information through degenerate pathways can fundamentally change the cognitive capacities of a system.…”

Section: Patterns Of Connectivity: Reverberation and Processing Acros...mentioning

confidence: 99%

The Relationship between Cognition and Brain Size or Neuron Number

Barron¹,

Mourmourakis²

2023

Brain Behav Evol

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The comparative approach is a powerful way to explore the relationship between brain structure and cognitive function. Thus far the field has been dominated by the assumption that a bigger brain somehow means better cognition. Correlations between differences in brain size or neuron number between species and differences in specific cognitive abilities exist, but these correlations are very noisy. Extreme differences exist between clades in the relationship between either brain size or neuron number and specific cognitive abilities. This means that correlations become weaker, not stronger, as the taxonomic diversity of sampled groups increases. Cognition is the outcome of neural networks. Here we propose that considering plausible neural network models will advance our understanding of the complex relationships between neuron number and different aspects of cognition. Computational modelling of networks suggests that adding pathways, or layers, or changing patterns of connectivity in a network can all have different specific consequences for cognition. Consequently, models of computational architecture can help us hypothesise how and why differences in neuron number might be related to differences in cognition. As methods in connectomics continue to improve and more structural information on animal brains becomes available we are learning more about natural network structures in brains, and we can develop more biologically plausible models of cognitive architecture. Natural animal diversity then becomes a powerful resource to both test the assumptions of these models and explore hypotheses for how neural network structure and network size might delimit cognitive function.

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“…A wider range of relationships can be recognized and learned, including learning of simple 'abstract' relationships [63,64]. Coupled fast scan and slower fixation systems can operate on the same information supporting forms of selective attention [65]. Five types of computational architecture.…”

Section: Five Types Of Computational Architecturementioning

confidence: 99%

“…A wider range of relationships can be recognized and learned, including learning of simple ‘abstract’ relationships [ 63 , 64 ]. Coupled fast scan and slower fixation systems can operate on the same information supporting forms of selective attention [ 65 ]. Additionally, the output of a sensorimotor transformation can be fed back into the system allowing use of an efference copy to cancel out the consequences of the movement of the sensory systems [ 66 ], as well as elementary forms of forward modelling of the consequences of choices and actions [ 67 ].…”

Section: Five Types Of Computational Architecturementioning

confidence: 99%

Transitions in cognitive evolution

2023

Self Cite

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The evolutionary history of animal cognition appears to involve a few major transitions: major changes that opened up new phylogenetic possibilities for cognition. Here, we review and contrast current transitional accounts of cognitive evolution. We discuss how an important feature of an evolutionary transition should be that it changes what is evolvable, so that the possible phenotypic spaces before and after a transition are different. We develop an account of cognitive evolution that focuses on how selection might act on the computational architecture of nervous systems. Selection for operational efficiency or robustness can drive changes in computational architecture that then make new types of cognition evolvable. We propose five major transitions in the evolution of animal nervous systems. Each of these gave rise to a different type of computational architecture that changed the evolvability of a lineage and allowed the evolution of new cognitive capacities. Transitional accounts have value in that they allow a big-picture perspective of macroevolution by focusing on changes that have had major consequences. For cognitive evolution, however, we argue it is most useful to focus on evolutionary changes to the nervous system that changed what is evolvable, rather than to focus on specific cognitive capacities.

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The best of both worlds: Dual systems of reasoning in animals and AI

Cited by 8 publications

References 88 publications

How honey bees make fast and accurate decisions

How honey bees make fast and accurate decisions

The Relationship between Cognition and Brain Size or Neuron Number

Transitions in cognitive evolution

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