Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 77,820 bioRxiv papers from 337,227 authors.
Most downloaded bioRxiv papers, all time
in category animal behavior and cognition
1,214 results found. For more information, click each entry to expand.
24,457 downloads animal behavior and cognition
This tutorial introduces the reader to Gaussian process regression as an expressive tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial aims to provide an accessible introduction to these techniques. We will introduce Gaussian processes which generate distributions over functions used for Bayesian non-parametric regression, and demonstrate their use in applications and didactic examples including simple regression problems, a demonstration of kernel-encoded prior assumptions and compositions, a pure exploration scenario within an optimal design framework, and a bandit-like exploration-exploitation scenario where the goal is to recommend movies. Beyond that, we describe a situation modelling risk-averse exploration in which an additional constraint (not to sample below a certain threshold) needs to be accounted for. Lastly, we summarize recent psychological experiments utilizing Gaussian processes. Software and literature pointers are also provided.
8,920 downloads animal behavior and cognition
Variation across dog breeds presents a unique opportunity for investigating the evolution and biological basis of complex behavioral traits. We integrated behavioral data from more than 17,000 dogs from 101 breeds with breed-averaged genotypic data (N = 5,697 dogs) from over 100,000 loci in the dog genome. Across 14 traits, we found that breed differences in behavior are highly heritable, and that clustering of breeds based on behavior accurately recapitulates genetic relationships. We identify 131 single nucleotide polymorphisms associated with breed differences in behavior, which are found in genes that are highly expressed in the brain and enriched for neurobiological functions and developmental processes. Our results provide insight into the heritability and genetic architecture of complex behavioral traits, and suggest that dogs provide a powerful model for these questions.
7,463 downloads animal behavior and cognition
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.
6,284 downloads animal behavior and cognition
Researchers and educators have long wrestled with the question of how best to teach their clients be they human, animal or machine. Here we focus on the role of a single variable, the difficulty of training, and examine its effect on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks, in which ambiguous stimuli must be sorted into one of two classes. For all of these gradient-descent based learning algorithms we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this 'Eighty Five Percent Rule' for artificial neural networks used in AI and biologically plausible neural networks thought to describe human and animal learning.
6,159 downloads animal behavior and cognition
Many microbes induce striking behavioral changes in their animal hosts, but how they achieve this is poorly understood, especially at the molecular level. Mechanistic understanding has been largely constrained by the lack of a model system with advanced tools for molecular manipulation. We recently discovered a strain of the behavior-manipulating fungal pathogen Entomophthora muscae infecting wild Drosophila, and established methods to infect D. melanogaster in the lab. Lab-infected flies manifest the moribund behaviors characteristic of E. muscae infection: hours before death, they climb upward, extend their proboscides and affix in place, then raise their wings, clearing a path for infectious spores to launch from their abdomens. We found that E. muscae invades the fly nervous system, suggesting a direct means by which the fungus could induce behavioral changes. Given the vast molecular toolkit available for D. melanogaster, we believe this new system will enable rapid progress in understanding the mechanistic basis of E. muscaes behavioral manipulation in the fly.
5,585 downloads animal behavior and cognition
Fabrice de Chaumont, Elodie Ey, Nicolas Torquet, Thibault Lagache, Stéphane Dallongeville, Albane Imbert, Thierry Legou, Anne-Marie Le Sourd, Philippe Faure, Thomas Bourgeron, Jean-Christophe Olivo-Marin
Preclinical studies of psychiatric disorders require the use of animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we present a real-time method for behavior analysis of mice housed in groups that couples computer vision, machine learning and Triggered-RFID identification to track and monitor animals over several days in enriched environments. The system extracts a thorough list of individual and collective behavioral traits and provides a unique phenotypic profile for each animal. On mouse models, we study the impact of mutations of genes Shank2 and Shank3 involved in autism. Characterization and integration of data from behavioral profiles of mutated female mice reveals distinctive activity levels and involvement in complex social configuration.
4,983 downloads animal behavior and cognition
Habits form a crucial component of behavior. In recent years, key computational models have conceptualized habits as arising from model-free reinforcement learning (RL) mechanisms, which typically select between available actions based on the future value expected to result from each. Traditionally, however, habits have been understood as behaviors that can be triggered directly by a stimulus, without requiring the animal to evaluate expected outcomes. Here, we develop a computational model instantiating this traditional view, in which habits develop through the direct strengthening of recently taken actions rather than through the encoding of outcomes. We demonstrate that this model accounts for key behavioral manifestations of habits, including insensitivity to outcome devaluation and contingency degradation, as well as the effects of reinforcement schedule on the rate of habit formation. The model also explains the prevalent observation of perseveration in repeated-choice tasks as an additional behavioral manifestation of the habit system. We suggest that mapping habitual behaviors onto value-free mechanisms provides a parsimonious account of existing behavioral and neural data. This mapping may provide a new foundation for building robust and comprehensive models of the interaction of habits with other, more goal-directed types of behaviors and help to better guide research into the neural mechanisms underlying control of instrumental behavior more generally.
4,566 downloads animal behavior and cognition
The ability to perceive and recognise a reflected mirror image as self (mirror self-recognition, MSR) is considered a hallmark of cognition across species. Although MSR has been reported in mammals and birds, it is not known to occur in any other major taxon. A factor potentially limiting the ability to test for MSR is that the established assay for MSR, the mark test, shows an interpretation bias towards animals with the dexterity (or limbs) required to touch a mark. Here, we show that the cleaner wrasse fish, Labroides dimidiatus, passes through all phases of the mark test: (i) social reactions towards the reflection, (ii) repeated idiosyncratic behaviours towards the mirror (contingency testing), and (iii) frequent observation of their reflection. When subsequently provided with a coloured tag, individuals attempt to remove the mark in the presence of a mirror but show no response towards transparent marks, or to coloured marks in the absence of a mirror. This remarkable finding presents a challenge to our interpretation of the mark test – do we accept that these behavioural responses in the mark test, which are taken as evidence of self-recognition in other species, mean that fish are self-aware? Or do we conclude that these behavioural patterns have a basis in a cognitive process other than self-recognition? If the former, what does this mean for our understanding of animal intelligence? If the latter, what does this mean for our application and interpretation of the mark test as a metric for animal cognitive abilities?
4,218 downloads animal behavior and cognition
Benedict C Jones, Amanda C Hahn, Claire I Fisher, Hongyi Wang, Michal Kandrik, Chengyang Han, Vanessa Fasolt, Danielle Morrison, Anthony J Lee, Iris J Holzleitner, Kieran J O’Shea, Craig Roberts, Anthony C Little, Lisa M DeBruine
Although widely cited as strong evidence that sexual selection has shaped human facial attractiveness judgments, evidence that preferences for masculine characteristics in men's faces are related to women's hormonal status is equivocal and controversial. Consequently, we conducted the largest ever longitudinal study of the hormonal correlates of women's preferences for facial masculinity (N=584). Analyses showed no compelling evidence that preferences for facial masculinity were related to changes in women's salivary steroid hormone levels. Furthermore, both within-subject and between-subject comparisons showed no evidence that oral contraceptive use decreased masculinity preferences. However, women generally preferred masculinized over feminized versions of men's faces, particularly when assessing men's attractiveness for short-term, rather than long-term, relationships. Our results do not support the hypothesized link between women's preferences for facial masculinity and their hormonal status.
4,028 downloads animal behavior and cognition
Behavior is a unifying organismal process where genes, neural function, anatomy and environment converge and interrelate. Here we review the current state and discuss the future impact of accelerating advances in technology for behavioral studies, focusing on rodents as an exemplar. We frame our perspective in three dimensions: degree of experimental constraint, dimensionality of data, and level of description. We argue that "big behavioral data" presents challenges proportionate to its promise and describe how these challenges might be met through opportunities afforded by the two rival conceptual legacies of 20th century behavioral science, ethology and psychology. We conclude that although "more is not necessarily better", copious, quantitative and open behavioral data has the potential to transform and unify these two disciplines and to solidify the foundations of others, including neuroscience, but only if the development of novel theoretical frameworks and improved experimental designs matches the technological progress.
3,965 downloads animal behavior and cognition
There is a growing number of dogs kept as companion animals, and the methods by which they are trained range broadly from those using mostly positive punishment and negative reinforcement (aversive-based methods) to those using primarily positive reinforcement (reward-based methods). Although the use of aversive-based methods has been strongly criticized for negatively affecting dog welfare, these claims do not find support in solid scientific evidence. Previous research on the subject lacks companion dog-focused research, investigation of the entire range of aversive-based techniques (beyond shock-collars), objective measures of welfare, and long-term welfare studies. The aim of the present study was to perform a comprehensive evaluation of the short- and long-term effects of aversive- and reward-based training methods on companion dog welfare. Ninety-two companion dogs were recruited from three reward-based (Group Reward, n=42) and four aversive-based (Group Aversive, n=50) dog training schools. For the short-term welfare assessment, dogs were video recorded for three training sessions and six saliva samples were collected, three at home (baseline levels) and three after the training sessions (post-training levels). Video recordings were then used to examine the frequency of stress-related behaviors (e.g., lip lick, yawn) and the overall behavioral state of the dog (e.g., tense, relaxed), and saliva samples were analyzed for cortisol concentration. For the long-term welfare assessment, dogs performed a cognitive bias task. Dogs from Group Aversive displayed more stress-related behaviors, spent more time in tense and low behavioral states and more time panting during the training sessions, showed higher elevations in cortisol levels after training and were more ‘pessimistic’ in the cognitive bias task than dogs from Group Reward. These findings indicate that the use of aversive-based methods compromises the welfare of companion dogs in both the short- and the long-term.
3,589 downloads animal behavior and cognition
Pose estimation is crucial for many applications in neuroscience, biomechanics, genetics and beyond. We recently presented a highly efficient method for markerless pose estimation based on transfer learning with deep neural networks called DeepLabCut. Current experiments produce vast amounts of video data, which pose challenges for both storage and analysis. Here we improve the inference speed of DeepLabCut by up to tenfold and benchmark these updates on various CPUs and GPUs. In particular, depending on the frame size, poses can be inferred offline at up to 1200 frames per second (FPS). For instance, 278 x 278 images can be processed at 225 FPS on a GTX 1080 Ti graphics card. Furthermore, we show that DeepLabCut is highly robust to standard video compression (ffmpeg). Compression rates of greater than 1,000 only decrease accuracy by about half a pixel (for 640 x 480 frame size). DeepLabCut's speed and robustness to compression can save both time and hardware expenses.
3,445 downloads animal behavior and cognition
Quantitative behavioral measurements are important for answering questions across scientific disciplines\---|from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings\---|including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
3,280 downloads animal behavior and cognition
Dogs are hypersocial with humans, and their integration into human social ecology makes dogs a unique model for studying cross-species social bonding. However, the proximal neural mechanisms driving dog-human social interaction are unknown. We used fMRI in 15 awake dogs to probe the neural basis for their preferences for social interaction and food reward. In a first experiment, we used the ventral caudate as a measure of intrinsic reward value and compared activation to conditioned stimuli that predicted food, praise, or nothing. Relative to the control stimulus, the caudate was significantly more active to the reward-predicting stimuli and showed roughly equal or greater activation to praise versus food in 13 of 15 dogs. To confirm that these differences were driven by the intrinsic value of social praise, we performed a second imaging experiment in which the praise was withheld on a subset of trials. The difference in caudate activation to the receipt of praise, relative to its withholding, was strongly correlated with the differential activation to the conditioned stimuli in the first experiment. In a third experiment, we performed an out-of-scanner choice task in which the dog repeatedly selected food or owner in a Y-maze. The relative caudate activation to food- and praise-predicting stimuli in Experiment 1 was a strong predictor of each dog's sequence of choices in the Y-maze. Analogous to similar neuroimaging studies of individual differences in human social reward, our findings demonstrate a neural mechanism for preference in domestic dogs that is stable within, but variable between, individuals. Moreover, the individual differences in the caudate responses indicate the potentially higher value of social than food reward for some dogs and may help to explain the apparent efficacy of social interaction in dog training.
3,276 downloads animal behavior and cognition
Absolute pitch (AP) refers to the rare ability to name the pitch of a tone without external reference. It is widely believed that acquiring AP in adulthood is impossible, since AP is only for the selected few with rare genetic makeup and early musical training. In three experiments, we trained adults to name pitches for 12 to 40 hours. Within the training period, 14% of the participants were able to name twelve pitches at 90% accuracy or above, a performance level comparable with typical AP possessors. At the group level, performance enhancement, learning generalization and sustainability were observed as in typical perceptual learning studies. The findings suggest that AP continues to be learnable in adulthood. The genesis of AP may be better explained by the amount and type of perceptual experience.
3,165 downloads animal behavior and cognition
Behaviour is the ultimate output of an animal's nervous system and choosing the right action at the right time can be critical for survival. The study of the organisation of behaviour in its natural context, ethology, has historically been a primarily qualitative science. A quantitative theory of behaviour would advance research in neuroscience as well as ecology and evolution. However, animal posture typically has many degrees of freedom and behavioural dynamics vary on timescales ranging from milliseconds to years, presenting both technical and conceptual challenges. Here we review 1) advances in imaging and computer vision that are making it possible to capture increasingly complete records of animal motion and 2) new approaches to understanding the resulting behavioural data sets. With the right analytical approaches, these data are allowing researchers to revisit longstanding questions about the structure and organisation of animal behaviour and to put unifying principles on a quantitative footing. Contributions from both experimentalists and theorists are leading to the emergence of a physics of behaviour and the prospect of discovering laws and developing theories with broad applicability. We believe that there now exists an opportunity to develop theories of behaviour which can be tested using these data sets leading to a deeper understanding of how and why animals behave.
2,877 downloads animal behavior and cognition
Rodent defense behavior assays have been widely used as preclinical models of anxiety to study possibly therapeutic anxiety-reducing interventions. However, some proposed anxiety-modulating factors - genes, drugs and stressors - have had discordant effects across different studies. To reconcile the effect sizes of purported anxiety factors, we conducted systematic review and meta-analyses of the literature on ten anxiety-linked interventions, as examined in the elevated plus maze, open field and light-dark box assays. Diazepam, 5-HT1A receptor gene knockout and overexpression, SERT gene knockout and overexpression, pain, restraint, social isolation, corticotropin-releasing hormone and Crhr1 were selected for review. Eight interventions had statistically significant effects on rodent anxiety, while Htr1a overexpression and Crh knockout did not. Evidence for publication bias was found in the diazepam, Htt knockout, and social isolation literatures. The Htr1a and Crhr1 results indicate a disconnect between preclinical science and clinical research. Furthermore, the meta-analytic data confirmed that genetic SERT anxiety effects were paradoxical in the context of the clinical use of SERT inhibitors to reduce anxiety.
2,651 downloads animal behavior and cognition
Human romantic partners tend to have similar physical traits, but the mechanisms causing this homogamy are controversial. One potential explanation is direct matching to own characteristics. Alternatively, studies showing similarity between parent and partner support positive sexual imprinting, where individuals are more likely to choose mates with the physical characteristics of their other-sex parent. This interpretation has been strongly criticized because the same pattern could also be caused by sex-linked heritable preferences, where similarity in appearance between an individual's partner and their other-sex parent is caused by similarity in preferences between the individual and their same-sex parent. The relationships among own, parents' and same-sex partner's eye color provide an elegant test of these hypotheses, which each postulate a different best predictor of partner's eye color. While the matching hypothesis predicts this will be own eye color, the sex-linked heritable preference hypothesis predicts this will be the other-sex parent's eye color and the positive sexual imprinting hypothesis predicts this will be the partner-sex parent's eye color. Here we show that partner eye color was best predicted by the partner-sex parent's eye color. Our results provide clear evidence against matching and sex-linked heritable preference hypotheses, and support the positive sexual imprinting hypothesis of the relationship between own and partner's eye color.
2,350 downloads animal behavior and cognition
Habitual reliance on tool use is a marked behavioral difference between wild robust (genus Sapajus) and gracile (genus Cebus) capuchin monkeys. Despite being well studied and having a rich repertoire of social and extractive foraging traditions, Cebus sp have rarely been observed engaging in tool use and have never been reported to use stone tools. In contrast, habitual tool use and stone-tool use by Sapajus is widespread. We discuss factors which might explain these differences in patterns of tool use between Cebus and Sapajus. We then report the first case of habitual stone-tool use in a gracile capuchin: a population of white-faced capuchins (Cebus capucinus imitator) in Coiba National Park, Panama who habitually rely on hammerstone and anvil tool use to access structurally protected food items in coastal areas including Terminalia catappa} seeds, hermit crabs, marine snails, terrestrial crabs, and other items. This behavior has persisted on one island in Coiba National Park since at least 2004. From one year of camera trapping, we found that stone tool use is strongly male-biased. Of the 205 unique camera-trap-days where tool use was recorded, adult females were never observed to use stone-tools, although they were frequently recorded at the sites and engaged in scrounging behavior. Stone-tool use occurs year-round in this population, and over half of all identifiable individuals were observed participating. At the most active tool use site, 83.2% of days where capuchins were sighted corresponded with tool use. Capuchins inhabiting the Coiba archipelago are highly terrestrial, under decreased predation pressure and potentially experience resource limitation compared to mainland populations-- three conditions considered important for the evolution of stone tool use. White-faced capuchin tool use in Coiba National Park thus offers unique opportunities to explore the ecological drivers and evolutionary underpinnings of stone tool use in a comparative within- and between-species context.
2,341 downloads animal behavior and cognition
Progress in understanding how individual animals learn will require high-throughput standardized methods for behavioral training but also advances in the analysis of the resulting behavioral data. In the course of training with multiple trials, an animal may change its behavior abruptly, and capturing such events calls for a trial-by-trial analysis of the animal's strategy. To address this challenge, we developed an integrated platform for automated animal training and analysis of behavioral data. A low-cost and space-efficient apparatus serves to train entire cohorts of mice on a decision-making task under identical conditions. A generalized linear model (GLM) analyzes each animal's performance at single-trial resolution. This model infers the momentary decision-making strategy and can predict the animal's choice on each trial with an accuracy of ~80%. We also assess the animal's detailed trajectories and body poses within the apparatus. Unsupervised analysis of these features revealed unusual trajectories that represent hesitation in the response. This integrated hardware/software platform promises to accelerate the understanding of animal learning.
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