Simplicity From Complexity in Vertebrate Behavior: Macphail (1987) Revisited

Fountain, Stephen B.; Dyer, Katherine H.; Jackman, Claire C.

doi:10.3389/fpsyg.2020.581899

Cited by 3 publications

(3 citation statements)

References 46 publications

(46 reference statements)

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“…Some theorists have argued that animals, at least the vertebrates, do not differ substantially in their underlying cognitive capabilities [ 26 ]. This claim has been updated to suggest that mechanisms for cognition are largely conserved, even if capability differs to an extent between species [ 27 ]. However, it seems plausible that, just as variable body morphology is selected to suit an ecological niche, so too should cognitive capability be selected to suit a cognitive niche [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

An Ecological and Neural Argument for Developing Pursuit-Based Cognitive Enrichment for Sea Lions in Human Care

Cook,

Reichmuth

2024

Animals

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While general enrichment strategies for captive animals attempt to elicit variable and species-typical behaviors, approaches to cognitive enrichment have been disappointingly one-size-fits-all. In this commentary, we address the potential benefit of tailoring cognitive enrichment to the “cognitive niche” of the species, with a particular focus on a reasonably well-studied marine carnivore, the sea lion. Sea lions likely share some cognitive evolutionary pressures with primates, including complex social behavior. Their foraging ecology, however, like that of many terrestrial carnivores, is based on the rapid and behaviorally flexible pursuit of avoidant prey. Unlike terrestrial carnivores, sea lions carry out this pursuit in a truly fluid three-dimensional field, computing and executing sensorimotor transformations from any solid angle to any other. The cognitive demands of flexible prey pursuit are unlikely to be fully elicited by typical stationary puzzle box style foraging enrichment devices or screen-based interactive games. With this species, we recommend exploring more water-based movement activities generally, and complex pursuit challenges specifically.

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Section: Introductionmentioning

confidence: 99%

An Ecological and Neural Argument for Developing Pursuit-Based Cognitive Enrichment for Sea Lions in Human Care

Cook,

Reichmuth

2024

Animals

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“…Behavior intrinsically involves a sequential pattern of movements, and behavioral events occur in probabilistic relationships with the environment (Fountain et al, 2020). Hidden Markov models (HMMs), stochastic time-series models, infer that an observed event sequence is driven by a series of transitions between hidden states (Leos- Barajas et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A layered, hybrid machine learning analytic workflow for mouse risk assessment behavior

Wang

Karbasi

Wang

et al. 2022

Preprint

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Accurate and efficient quantification of animal behavior facilitates the understanding of the brain. An emerging approach within machine learning (ML) field is to combine multiple ML-based algorithms to quantify animal behavior. These so-called hybrid models have emerged because of limitations associated with supervised (e.g., random forest, RF) and unsupervised (e.g., hidden Markov model, HMM) ML classifiers. For example, RF models lack temporal information across video frames, and HMM latent states are often difficult to interpret. We sought to develop a hybrid model, and did so in the context of a study of mouse risk assessment behavior. We utilized DeepLabCut to estimate the positions of mouse body parts. Positional features were calculated using DeepLabCut outputs and were used to train RF and HMM models with equal number of states, separately. The per-frame predictions from RF and HMM models were then passed to a second HMM model layer ("reHMM"). The outputs of the reHMM layer showed improved interpretability over the initial HMM output. Finally, we combined predictions from RF and HMM models with selected positional features to train a third HMM model ("reHMM+"). This reHMM+ layered hybrid model unveiled distinctive temporal and human-interpretable behavioral patterns. We applied this workflow to investigate risk assessment to trimethylthiazoline and snake feces odor, finding unique behavioral patterns to each that were separable from attractive and neutral stimuli. We conclude that this layered, hybrid machine learning workflow represents a balanced approach for improving the depth and reliability of ML classifiers in chemosensory and other behavioral contexts.

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“…Behavior intrinsically involves a sequential pattern of movements, and behavioral events occur in probabilistic relationships with the environment ( Fountain et al, 2020 ). Hidden Markov models (HMMs), stochastic time-series models, infer that an observed event sequence is driven by a series of transitions between hidden states ( Leos‐Barajas et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

A Layered, Hybrid Machine Learning Analytic Workflow for Mouse Risk Assessment Behavior

Karbasi

Wang

Meeks

2022

eNeuro

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Accurate and efficient quantification of animal behavior facilitates the understanding of the brain. An emerging approach within machine learning (ML) field is to combine multiple ML-based algorithms to quantify animal behavior. These so-called hybrid models have emerged because of limitations associated with supervised (e.g., random forest, RF) and unsupervised (e.g., hidden Markov model, HMM) ML models. For example, RF models lack temporal information across video frames, and HMM latent states are often difficult to interpret. We sought to develop a hybrid model, and did so in the context of a study of mouse risk assessment behavior. We utilized DeepLabCut to estimate the positions of mouse body parts. Positional features were calculated using DeepLabCut outputs and were used to train RF and HMM models with equal number of states, separately. The per-frame predictions from RF and HMM models were then passed to a second HMM model layer ("reHMM"). The outputs of the reHMM layer showed improved interpretability over the initial HMM output. Finally, we combined predictions from RF and HMM models with selected positional features to train a third HMM model ("reHMM+"). This reHMM+ layered hybrid model unveiled distinctive temporal and human-interpretable behavioral patterns. We applied this workflow to investigate risk assessment to trimethylthiazoline and snake feces odor, finding unique behavioral patterns to each that were separable from attractive and neutral stimuli. We conclude that this layered, hybrid ML workflow represents a balanced approach for improving the depth and reliability of ML classifiers in chemosensory and other behavioral contexts.Significance StatementIn this study, we integrate two widely-adopted machine learning (ML) models, random forest and hidden Markov model, to develop a layered, hybrid ML-based workflow. Our workflow not only overcomes the intrinsic limitations of each model alone, but also improves the depth and reliability of ML models. Implementing this analytic workflow unveils distinctive and dynamic mouse behavioral patterns to chemosensory cues in the context of mouse risk assessment behavioral experiments. This study provides an efficient and interpretable analytic strategy for the quantification of animal behavior in diverse experimental settings.

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Simplicity From Complexity in Vertebrate Behavior: Macphail (1987) Revisited

Cited by 3 publications

References 46 publications

An Ecological and Neural Argument for Developing Pursuit-Based Cognitive Enrichment for Sea Lions in Human Care

An Ecological and Neural Argument for Developing Pursuit-Based Cognitive Enrichment for Sea Lions in Human Care

A layered, hybrid machine learning analytic workflow for mouse risk assessment behavior

A Layered, Hybrid Machine Learning Analytic Workflow for Mouse Risk Assessment Behavior

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