Macaques are risk-averse in a freely moving foraging task

Eisenreich, Benjamin R.; Hayden, Benjamin Y.; Zimmermann, Jan

doi:10.1038/s41598-019-51442-z

Cited by 40 publications

(36 citation statements)

References 91 publications

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“…Three of the subjects had previously served as subjects on standard neuroeconomic tasks, including a set shifting task (45) and several simple choice tasks (46)(47)(48)(49)(50). Training also included experience with foraging tasks (51,52), including one study using the large cage apparatus (53). One subject was naive to all experimental procedures.…”

Section: Data Collectionmentioning

confidence: 99%

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OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques

Bala

Eisenreich

Yoo

et al. 2020

Preprint

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The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and several fields of medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure its behavior, specifically, its pose (position of multiple major body landmarks) in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a novel deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 precisely calibrated and synchronized machine vision cameras that encircle an open 2.45m×2.45m×2.75m enclosure. The resulting multiview image streams allow for novel data augmentation via 3D reconstruction of hand-annotated images that in turn train a robust view-invariant deep neural network model. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track two monkey social interactions without human intervention. We also make the training data (195,228 images) and trained detection model publicly available. deep learning | behavioral tracking | rhesus macaque | convolutional pose machineCorrespondence: Benjamin Hayden,

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Section: Data Collectionmentioning

confidence: 99%

“…This process ensured that subjects were comfortable with the environment. We then trained subjects to use the specially designed juice dispenser (53).…”

Section: Data Collectionmentioning

confidence: 99%

OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques

Bala

Eisenreich

Yoo

et al. 2020

Preprint

Self Cite

120

View full text Add to dashboard Cite

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“…First, individual foragers tend to exhibit much more behavioural variability than they should [6][7][8][9][10]. This is true even in carefully controlled computerized tasks [11][12][13]. A decision-maker that shows variability will harvest less reward than one that does not because it is often suboptimal on individual trials.…”

Section: Introductionmentioning

confidence: 99%

Behavioural variability contributes to over-staying in patchy foraging

Cash-Padgett

Hayden

2020

Biol. Lett.

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Foragers often systematically deviate from rate-maximizing choices in two ways: accuracy and precision. That is, they use suboptimal threshold values and also show variability in their application of those thresholds. We hypothesized that these biases are related and, more specifically, that foragers' widely known accuracy bias––over-staying––could be explained, at least in part, by their imprecision. To test this hypothesis, we analysed choices made by three rhesus macaques in a computerized patch foraging task. Confirming previously observed findings, we found high levels of variability. We then showed, through simulations, that this variability changed optimal thresholds, meaning that a forager aware of its own variability should increase its leaving threshold (i.e. over-stay) to increase performance. All subjects showed thresholds that were biased in the predicted direction. These results indicate that over-staying in patches may reflect, in part, an adaptation to behavioural variability.

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“…Three of the subjects had previously served as subjects on standard neuroeconomic tasks, including a set shifting task 47 and several simple choice tasks 48 – 52 . Training also included experience with foraging tasks 53 , 54 , including one study using the large cage apparatus 55 . One subject was naive to all experimental procedures.…”

Section: Methodsmentioning

confidence: 99%

Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio

et al. 2020

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The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure behavior in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 machine vision cameras that encircle an open 2.45 m × 2.45 m × 2.75 m enclosure. The resulting multiview image streams allow for data augmentation via 3D-reconstruction of annotated images to train a robust view-invariant deep neural network. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track social interactions.

show abstract

Macaques are risk-averse in a freely moving foraging task

Cited by 40 publications

References 91 publications

OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques

OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques

Behavioural variability contributes to over-staying in patchy foraging

Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio

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