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Most downloaded biology preprints, all time

in category animal behavior and cognition

1,724 results found. For more information, click each entry to expand.

81: Mapping the unknown: The spatially correlated multi-armed bandit
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Posted 06 Feb 2017

Mapping the unknown: The spatially correlated multi-armed bandit
1,496 downloads bioRxiv animal behavior and cognition

Charley M. Wu, Eric Schulz, Maarten Speekenbrink, Jonathan D. Nelson, Björn Meder

We introduce the spatially correlated multi-armed bandit as a task coupling function learning with the exploration-exploitation trade-off. Participants interacted with bi-variate reward functions on a two-dimensional grid, with the goal of either gaining the largest average score or finding the largest payoff. By providing an opportunity to learn the underlying reward function through spatial correlations, we model to what extent people form beliefs about unexplored payoffs and how that guides search behavior. Participants adapted to assigned payoff conditions, performed better in smooth than in rough environments, and--surprisingly--sometimes performed equally well in short as in long search horizons. Our modeling results indicate a preference for local search options, which when accounted for, still suggests participants were best-described as forming local inferences about unexplored regions, combined with a search strategy that directly traded off between exploiting high expected rewards and exploring to reduce uncertainty about the spatial structure of rewards.

82: Prediction of migratory routes of the invasive fall armyworm in eastern China using a trajectory analytical approach
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Posted 02 May 2019

Prediction of migratory routes of the invasive fall armyworm in eastern China using a trajectory analytical approach
1,477 downloads bioRxiv animal behavior and cognition

Xi-Jie Li, Ming-Fei Wu, Jian Ma, Bo-Ya Gao, Qiu-Lin Wu, Ai-Dong Chen, Jie Liu, Yu-Ying Jiang, Bao-Ping Zhai, Regan Early, Jason W. Chapman, Gao Hu

BACKGROUND: The fall armyworm (FAW), an invasive pest from the Americas, is rapidly spreading through the Old World, and has recently invaded the Indochinese Peninsula and southern China. In the Americas, FAW migrates from winter-breeding areas in the south into summer-breeding areas throughout North America where it is a major pest of corn. Asian populations are also likely to evolve migrations into the corn-producing regions of eastern China, where they will pose a serious threat to food security. RESULTS: To evaluate the invasion risk in eastern China, the rate of expansion and future migratory range was modelled by a trajectory simulation approach, combined with flight behaviour and meteorological data. Our results predict that FAW will migrate from its new year-round breeding regions into the two main corn-producing regions of eastern China (the North China and Northeast China Plains), via two pathways. The western pathway originates in Myanmar and Yunnan, and FAW will take four migration steps to reach the North China Plain by July. Migration along the eastern pathway from Indochina and southern China progresses faster, with FAW reaching the North China Plain in three steps by June and reaching the Northeast China Plain in July. CONCLUSION: Our results indicate that there is a high risk that FAW will invade the major corn-producing areas of eastern China via two migration pathways, and cause significant impacts to agricultural productivity. Information on migration pathways and timings can be used to inform integrated pest management strategies for this emerging pest.

83: GCalignR: An R package for aligning Gas-Chromatography data
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Posted 22 Feb 2017

GCalignR: An R package for aligning Gas-Chromatography data
1,466 downloads bioRxiv animal behavior and cognition

Meinolf Ottensmann, Martin Adam Stoffel, Hazel J. Nichols, Joseph I. Hoffman

Chemical cues are arguably the most fundamental means of animal communication and play an important role in mate choice and kin recognition. Consequently, gas chromatography (GC) in combination with either mass spectrometry (MS) or flame ionisation detection (FID) are commonly used to characterise complex chemical samples. Both GC-FID and GC-MS generate chromatograms comprising peaks that are separated according to their retention times and which represent different substances. Across chromatograms of different samples, homologous substances are expected to elute at similar retention times. However, random and often unavoidable experimental variation introduces noise, making the alignment of homologous peaks challenging, particularly with GC-FID data where mass spectral data are lacking. Here we present GCalignR, a user-friendly R package for aligning GC-FID data based on retention times. The package also implements dynamic visualisations to facilitate inspection and fine-tuning of the resulting alignments, and can be integrated within a broader workflow in R to facilitate downstream multivariate analyses. We demonstrate an example workflow using empirical data from Antarctic fur seals and explore the impact of user-defined parameter values by calculating alignment error rates for multiple datasets. The resulting alignments had low error rates for most of the explored parameter space and we could also show that GCalignR performed equally well or better than other available software. We hope that GCalignR will help to simplify the processing of chemical datasets and improve the standardization and reproducibility of chemical analyses in studies of animal chemical communication and related fields.

84: Convolutional Neural Network Analysis of Social Novelty Preference using DeepLabCut
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Posted 15 Aug 2019

Convolutional Neural Network Analysis of Social Novelty Preference using DeepLabCut
1,447 downloads bioRxiv animal behavior and cognition

Nicholas B. Worley, Anthony Djerdjaj, John P. Christianson

The description and quantification of social behavior in laboratory rodents is central to basic and translational research. Conventional ethological approaches to social behavior are fraught with challenges including bias, significant human effort and temporal accuracy. Here we show proof of principle that machine learning can be applied to laboratory tests of social decision making. Rats underwent social novelty preference tests which were scored both by hand and again by a convolutional neural network generated in the DeepLabCut computer vision package of Mathis and colleagues. The CNN generated temporally (30Hz) and locally (<5pixels) accurate identification of rat nose, eye and ear positions which were then used to compute social interaction and topography heat maps. In sum, hand- and computer-scoring were strongly correlated, and each identified significant preferences to interact with novel conspecifics which sets the stage for applying DeepLabCut analysis to other types of social interaction in the future.

85: DeepFly3D: A deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila
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Posted 20 May 2019

DeepFly3D: A deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila
1,435 downloads bioRxiv animal behavior and cognition

Semih Günel, Helge Rhodin, Daniel Morales, João Campagnolo, Pavan Ramdya, Pascal Fua

Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in 3-dimensional (3D) space. Deep neural networks can estimate 2-dimensional (2D) pose in freely behaving and tethered animals. However, the unique challenges associated with transforming these 2D measurements into reliable and precise 3D poses have not been addressed for small animals including the fly, Drosophila melanogaster . Here we present DeepFly3D, a software that infers the 3D pose of tethered, adult Drosophila —or other animals—using multiple camera images. DeepFly3D does not require manual calibration, uses pictorial structures to automatically detect and correct pose estimation errors, and uses active learning to iteratively improve performance. We demonstrate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly used 2D pose data. Thus, DeepFly3D enables the automated acquisition of behavioral measurements at an unprecedented level of resolution for a variety of biological applications.

86: pavo 2: new tools for the spectral and spatial analysis of colour in R
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Posted 26 Sep 2018

pavo 2: new tools for the spectral and spatial analysis of colour in R
1,426 downloads bioRxiv animal behavior and cognition

Rafael Maia, Hugo Gruson, John A. Endler, Thomas E. White

Biological colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in colour-based phenotypes has driven, and been driven by, improved techniques for quantifying colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists. Here we introduce pavo 2, the latest iteration of the R package pavo. This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of colour patterns. We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned. pavo 2 offers a flexible and reproducible environment for the analysis of colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.

87: Mobile phones: The effect of its presence on learning and memory
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Posted 20 Jun 2019

Mobile phones: The effect of its presence on learning and memory
1,399 downloads bioRxiv animal behavior and cognition

Clarissa T. Tanil, Min Hooi Yong

Our aim was to examine the effect of mobile phone’s presence on learning and memory among undergraduates. A total of 119 undergraduates completed a memory task and the Smartphone Addiction Scale (SAS). As predicted, those without phones had higher recall accuracy compared to those with phones. Results showed a significant negative relationship between phone conscious thought and memory recall but not for SAS and memory recall. Phone conscious thought significantly predicted memory accuracy. We found that the presence of a phone and high phone conscious thought affects one’s memory learning and recall, indicating the negative effect of a mobile phone proximity to our learning and memory.

88: Reward-predictive representations generalize across tasks in reinforcement learning
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Posted 30 May 2019

Reward-predictive representations generalize across tasks in reinforcement learning
1,398 downloads bioRxiv animal behavior and cognition

Lucas Lehnert, Michael L. Littman, Michael J. Frank

In computer science, reinforcement learning is a powerful framework with which artificial agents can learn to maximize their performance for any given Markov decision process (MDP). Advances over the last decade, in combination with deep neural networks, have enjoyed performance advantages over humans in many difficult task settings. However, such frameworks perform far less favorably when evaluated in their ability to generalize or transfer representations across different tasks. Existing algorithms that facilitate transfer typically are limited to cases in which the transition function or the optimal policy is portable to new contexts, but achieving “deep transfer” characteristic of human behavior has been elusive. Such transfer typically requires discovery of abstractions that permit analogical reuse of previously learned representations to superficially distinct tasks. Here, we demonstrate that abstractions that minimize error in predictions of reward outcomes generalize across tasks with different transition and reward functions. Such reward-predictive representations compress the state space of a task into a lower dimensional representation by combining states that are equivalent in terms of both the transition and reward functions. Because only state equivalences are considered, the resulting state representation is not tied to the transition and reward functions themselves and thus generalizes across tasks with different reward and transition functions. These results contrast with those using abstractions that myopically maximize reward in any given MDP and motivate further experiments in humans and animals to investigate if neural and cognitive systems involved in state representation perform abstractions that facilitate such equivalence relations. Author summary Humans are capable of transferring abstract knowledge from one task to another. For example, in a right-hand-drive country, a driver has to use the right arm to operate the shifter. A driver who learned how to drive in a right-hand-drive country can adapt to operating a left-hand-drive car and use the other arm for shifting instead of re-learning how to drive. Despite the fact that both tasks require different coordination of motor skills, both tasks are the same in an abstract sense: In both tasks, a car is operated and there is the same progression from 1st to 2nd gear and so on. We study distinct algorithms by which a reinforcement learning agent can discover state representations that encode knowledge about a particular task, and evaluate how well they can generalize. Through a sequence of simulation results, we show that state abstractions that minimize errors in prediction about future reward outcomes generalize across tasks, even those that superficially differ in both the goals (rewards) and the transitions from one state to the next. This work motivates biological studies to determine if distinct circuits are adapted to maximize reward vs. to discover useful state representations. ### Competing Interest Statement The authors have declared no competing interest.

89: Reference-dependent preferences arise from structure learning
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Posted 24 Jan 2018

Reference-dependent preferences arise from structure learning
1,394 downloads bioRxiv animal behavior and cognition

Lindsay E Hunter, Samuel J Gershman

Modern theories of decision making emphasize the reference-dependency of decision making under risk. In particular, people tend to be risk-averse for outcomes greater than their reference point, and risk-seeking for outcomes less than their reference point. A key question is where reference points come from. A common assumption is that reference points correspond to expectations about outcomes, but it is unclear whether people rely on a single global expectation, or multiple local expectations. If the latter, how do people determine which expectation to apply in a particular situation? We argue that people discover reference points using a form of Bayesian structure learning, which partitions outcomes into distinct contexts, each with its own reference point corresponding to the expected outcome in that context. Consistent with this theory, we show experimentally that dramatic change in the distribution of outcomes can induce the discovery of a new reference point, with systematic effects on risk preferences. By contrast, when changes are gradual, a single reference point is continuously updated.

90: Unsupervised data-driven stratification of mentalizing heterogeneity in autism
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Posted 15 Dec 2015

Unsupervised data-driven stratification of mentalizing heterogeneity in autism
1,392 downloads bioRxiv animal behavior and cognition

Michael V. Lombardo, Meng-Chuan Lai, Bonnie Auyeung, Rosemary J Holt, Carrie Allison, Paula Smith, Bhismadev Chakrabarti, Amber N. V. Ruigrok, John Suckling, Edward T. Bullmore, MRC AIMS Consortium, Christine Ecker, Michael C. Craig, Declan G. M. Murphy, Francesca Happé, Simon Baron-Cohen

Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n=715; n=251), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 42-65% of ASC adults show evidence for large impairments (Cohen's d = -1.03 to -11.21), while other subgroups are effectively unimpaired. These findings delineate robust natural subdivisions within the ASC population that may allow for more individualized inferences and accelerate research towards precision medicine goals.

91: Paramecium Learning: New Insights and Modifications
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Posted 27 Nov 2017

Paramecium Learning: New Insights and Modifications
1,387 downloads bioRxiv animal behavior and cognition

Abolfazl Alipour, Mohammadreza Dorvash, Yasaman Yeganeh, Gholamreza Hatam

Learning is a fundamental process in neural systems. However, microorganisms without a nervous system have been shown to possess learning abilities. Specifically, Paramecium caudatum has been previously reported to be able to form associations between lighting conditions and cathodal shocks in its swimming medium. We have replicated previous reports on this phenomenon and tested the predictions of a molecular pathway hypothesis on paramecium learning. Our results indicated that in contrast to the previous reports, paramecium can only associate higher light intensities with cathodal stimulation and it cannot associate lower light intensities with cathodal stimulation. These results found to be in line with the predictions of the previously proposed model for the molecular mechanisms of learning in paramecium which depends on the effects of cathodal shocks on the interplay between Cyclic adenosine monophosphate concentration and phototactic behavior of paramecium.

92: Automatic classification of behavior in zebrafish larvae
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Posted 10 May 2016

Automatic classification of behavior in zebrafish larvae
1,377 downloads bioRxiv animal behavior and cognition

Adrien Jouary, German Sumbre

Zebrafish larvae navigate the environment by discrete episode of propulsion called bouts. We introduce a novel method for automatically classifying zebrafish tail movements. We used a supervised soft-clustering algorithm to categorize tail bouts into 5 categories of movements: Scoot, Asymmetrical Scoot, Routine Turn, C Bend and Burst. Tail bouts were correctly classified with 82% chance while errors in the classification occurred mostly between similar categories. Although previous studies have performed categorization of behavior in free-swimming conditions, our method does not rely on the analysis of the trajectories and can be applied in both head-fixed and free-swimming conditions.

93: Beyond Brain Size
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Posted 02 Jun 2017

Beyond Brain Size
1,350 downloads bioRxiv animal behavior and cognition

CJ Logan, Shahar Avin, Neeltje Boogert, Andrew Buskell, Fiona R. Cross, Adrian Currie, Sarah Jelbert, Dieter Lukas, Rafael Mares, Ana F Navarrete, Shuichi Shigeno, Stephen H. Montgomery

Despite prolonged interest in comparing brain size and behavioral proxies of intelligence across taxa, the adaptive and cognitive significance of brain size variation remains elusive. Central to this problem is the continued focus on hominid cognition as a benchmark, and the assumption that behavioral complexity has a simple relationship with brain size. Although comparative studies of brain size have been criticized for not reflecting how evolution actually operates, and for producing spurious, inconsistent results, the causes of these limitations have received little discussion. We show how these issues arise from implicit assumptions about what brain size measures and how it correlates with behavioral and cognitive traits. We explore how inconsistencies can arise through heterogeneity in evolutionary trajectories and selection pressures on neuroanatomy or neurophysiology across taxa. We examine how interference from ecological and life history variables complicates interpretations of brain-behavior correlations, and point out how this problem is exacerbated by the limitations of brain and cognitive measures. These considerations, and the diversity of brain morphologies and behavioral capacities, suggest that comparative brain-behavior research can make greater progress by focusing on specific neuroanatomical and behavioral traits within relevant ecological and evolutionary contexts. We suggest that a synergistic combination of the bottom up approach of classical neuroethology and the top down approach of comparative biology/psychology within closely related but behaviorally diverse clades can limit the effects of heterogeneity, interference, and noise. We argue this shift away from broad-scale analyses of superficial phenotypes will provide deeper, more robust insights into brain evolution.

94: Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
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Posted 22 Aug 2020

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
1,345 downloads bioRxiv animal behavior and cognition

Anqi Wu, Estefany Kelly Buchanan, Matthew R Whiteway, Michael Schartner, Guido T. Meijer, Jean-Paul G Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan S Schaffer, Neeli Mishra, C Daniel Salzman, Dora E. Angelaki, The International Brain Laboratory, John P Cunningham, Liam Paninski

Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently. In this work, we improve on these methods (particularly in the regime of few training labels) by leveraging the rich spatiotemporal structures pervasive in behavioral video \---| specifically, the spatial statistics imposed by physical constraints (e.g., paw to elbow distance), and the temporal statistics imposed by smoothness from frame to frame. We propose a probabilistic graphical model built on top of deep neural networks, Deep Graph Pose (DGP), to leverage these useful spatial and temporal constraints, and develop an efficient structured variational approach to perform inference in this model. The resulting semi-supervised model exploits both labeled and unlabeled frames to achieve significantly more accurate and robust tracking while requiring users to label fewer training frames. In turn, these tracking improvements enhance performance on downstream applications, including robust unsupervised segmentation of behavioral \``syllables,'' and estimation of interpretable \``disentangled'' low-dimensional representations of the full behavioral video. Open source code is available at https://github.com/paninski-lab/deepgraphpose. ### Competing Interest Statement The authors have declared no competing interest.

95: BiteOscope: an open platform to study mosquito blood-feeding behavior
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Posted 20 Feb 2020

BiteOscope: an open platform to study mosquito blood-feeding behavior
1,302 downloads bioRxiv animal behavior and cognition

Felix J.H. Hol, Louis Lambrechts, Manu Prakash

Female mosquitoes need a blood meal to reproduce, and in obtaining this essential nutrient they transmit deadly pathogens. Although crucial for the spread of mosquito-borne diseases, our understanding of skin exploration, probing, and engorgement, is limited due to a lack of quantitative tools. Indeed, studies often expose human subjects to assess biting behavior. Here, we present the biteOscope, a device that attracts mosquitoes to a host mimic which they bite to obtain an artificial blood meal. The host mimic is transparent, allowing high-resolution imaging of the feeding mosquito. Using machine learning we extract detailed behavioral statistics describing the locomotion, pose, biting, and feeding dynamics of Aedes aegypti, Aedes albopictus, Anopheles stephensi, and Anopheles coluzzii. In addition to characterizing behavioral patterns, we discover that the common insect repellent DEET repels Anopheles coluzzii upon contact with their legs. The biteOscope provides a new perspective on mosquito blood feeding, enabling high-throughput quantitative characterization of the effects physiological and environmental factors have on this lethal behavior.

96: Quantitative Colour Pattern Analysis (QCPA): A Comprehensive Framework for the Analysis of Colour Patterns in Nature
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Posted 28 Mar 2019

Quantitative Colour Pattern Analysis (QCPA): A Comprehensive Framework for the Analysis of Colour Patterns in Nature
1,290 downloads bioRxiv animal behavior and cognition

Cedric P. van den Berg, Jolyon Troscianko, John A. Endler, N. Justin Marshall, Karen L. Cheney

1. To understand the function of colour signals in nature, we require robust quantitative analytical frameworks to enable us to estimate how animal and plant colour patterns appear against their natural background as viewed by ecologically relevant species. Due to the quantitative limitations of existing methods, colour and pattern are rarely analysed in conjunction with one another, despite a large body of literature and decades of research on the importance of spatiochromatic colour pattern analyses. Furthermore, key physiological limitations of animal visual systems such as spatial acuity, spectral sensitivities, photoreceptor abundances and receptor noise levels are rarely considered together in colour pattern analyses. 2. Here, we present a novel analytical framework, called the ‘Quantitative Colour Pattern Analysis’ (QCPA). We have overcome many quantitative and qualitative limitations of existing colour pattern analyses by combining calibrated digital photography and visual modelling. We have integrated and updated existing spatiochromatic colour pattern analyses, including adjacency, visual contrast and boundary strength analysis, to be implemented using calibrated digital photography through the ‘Multispectral Image Analysis and Calibration’ (MICA) Toolbox. 3. This combination of calibrated photography and spatiochromatic colour pattern analyses is enabled by the inclusion of psychophysical colour and luminance discrimination thresholds for image segmentation, which we call ‘Receptor Noise Limited Clustering’, used here for the first time. Furthermore, QCPA provides a novel psycho-physiological approach to the modelling of spatial acuity using convolution in the spatial or frequency domains, followed by ‘Receptor Noise Limited Ranked Filtering’ to eliminate intermediate edge artefacts and recover sharp boundaries following smoothing. We also present a new type of colour pattern analysis, the ‘Local Edge Intensity Analysis’ (LEIA) as well as a range of novel psycho-physiological approaches to the visualisation of spatiochromatic data. 4. QCPA combines novel and existing pattern analysis frameworks into what we hope is a unified, user-friendly, free and open source toolbox and introduce a range of novel analytical and data-visualisation approaches. These analyses and tools have been seamlessly integrated into the MICA toolbox providing a dynamic and user-friendly workflow. 5. QCPA is a framework for the empirical investigation of key theories underlying the design, function and evolution of colour patterns in nature. We believe that it is compatible with, but more thorough than, other existing colour pattern analyses.

97: Value-based decisions involve sequential sampling from memory
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Posted 22 Feb 2018

Value-based decisions involve sequential sampling from memory
1,289 downloads bioRxiv animal behavior and cognition

Akram Bakkour, Ariel Zylberberg, Michael N Shadlen, Daphna Shohamy

Deciding between two equally appealing options can take considerable time. This observation has puzzled economists and philosophers, because more deliberation only delays the reward. Here we show that this seemingly irrational behavior is explained by the constructive use of memory. Using functional brain imaging in humans, we show that how long it takes to decide between two familiar food items is related to activity in the hippocampus, within specific regions shown to be associated with the retrieval of long-term memories. Moreover, we show that value is partially constructed during deliberation to resolve preference, and this constructive process changes behavior and brain responses. These results render memory as a supplier of evidence in value-based decisions, resolving a central paradox of choice.

98: SLEAP: Multi-animal pose tracking
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Posted 02 Sep 2020

SLEAP: Multi-animal pose tracking
1,282 downloads bioRxiv animal behavior and cognition

Talmo Periera, Nathaniel Tabris, Junyu Li, Shruthi Ravindranath, Eleni S. Papadoyannis, Z Yan Wang, David M. Turner, Grace McKenzie-Smith, Sarah D Kocher, Annegret L. Falkner, Joshua W. Shaevitz, Mala Murthy

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation to quantify and model natural animal behavior. This has led to important advances in deep learning-based markerless pose estimation that have been enabled in part by the success of deep learning for computer vision applications. Here we present SLEAP (Social LEAP Estimates Animal Poses), a framework for multi-animal pose tracking via deep learning. This system is capable of simultaneously tracking any number of animals during social interactions and across a variety of experimental conditions. SLEAP implements several complementary approaches for dealing with the problems inherent in moving from single-to multi-animal pose tracking, including configurable neural network architectures, inference techniques, and tracking algorithms, enabling easy specialization and tuning for particular experimental conditions or performance requirements. We report results on multiple datasets of socially interacting animals (flies, bees, and mice) and describe how dataset-specific properties can be leveraged to determine the best configuration of SLEAP models. Using a high accuracy model (<2.8 px error on 95% of points), we were able to track two animals from full size 1024 × 1024 pixel frames at up to 320 FPS. The SLEAP framework comes with a sophisticated graphical user interface, multi-platform support, Colab-based GPU-free training and inference, and complete tutorials available, in addition to the datasets, at [sleap.ai][1]. ### Competing Interest Statement The authors have declared no competing interest. [1]: http://sleap.ai

99: Compositional Inductive Biases in Function Learning
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Posted 03 Dec 2016

Compositional Inductive Biases in Function Learning
1,265 downloads bioRxiv animal behavior and cognition

Eric Schulz, Joshua B Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel J Gershman

How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional.

100: Neuromodulatory circuit effects on Drosophila feeding behaviour and metabolism
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Posted 08 Nov 2016

Neuromodulatory circuit effects on Drosophila feeding behaviour and metabolism
1,264 downloads bioRxiv animal behavior and cognition

Anders Eriksson, Marlena Raczkowska, Rapeechai Navawongse, Deepak Choudhury, James C. Stewart, Yi Ling Tang, Zhiping Wang, Adam Claridge-Chang

Animals have evolved to maintain homeostasis in a changing external environment by adapting their internal metabolism and feeding behaviour. Metabolism and behaviour are coordinated by neuromodulation; a number of the implicated neuromodulatory systems are homologous between mammals and the vinegar fly, an important neurogenetic model. We investigated whether silencing fly neuromodulatory networks would elicit coordinated changes in feeding, behavioural activity and metabolism. We employed transgenic lines that allowed us to inhibit broad cellular sets of the dopaminergic, serotonergic, octopaminergic, tyraminergic and neuropeptide F systems. The genetically-manipulated animals were assessed for changes in their overt behavioural responses and metabolism by monitoring eleven parameters: activity; climbing ability; individual feeding; group feeding; food discovery; both fed and starved respiration; fed and starved lipid content; and fed/starved body weight. The results from these 55 experiments indicate that individual neuromodulatory system effects on feeding behaviour, motor activity and metabolism are dissociated.

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