Most downloaded biology preprints, since beginning of last month
in category animal behavior and cognition
1,685 results found. For more information, click each entry to expand.
1,163 downloads bioRxiv 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.
753 downloads bioRxiv animal behavior and cognition
Dominique Grandjean, Riad Sarkis, Jean-Pierre Tourtier, Clothilde Julien-Lecocq, Aymeric Benard, Vinciane Roger, Eric Levesque, Eric Bernes-Luciani, Bruno Maestracci, Pascal Morvan, Eric Gully, David Berceau-Falancourt, Jean-Luc Pesce, Bernard Lecomte, Pierre Haufstater, Gregory Herin, Joaquin Cabrera, Quentin Muzzin, Capucine Gallet, Hélène Bacqué, Jean-Marie Broc, Leo Thomas, Anthony Lichaa, Georges Moujaes, Michele Saliba, Aurore Kuhn, Mathilde Galey, Benoit Berthail, Lucien Lapeyre, Olivier Méreau, Marie-Nicolas Matteï, Audrey Foata, Louisa Bey, Anne-Sophie Philippe, Paul Abassi, Ferri Pisani, Marlène Delarbre, Jean-Marc Orsini, Anthoni Capelli, Steevens Renault, Karim Bachir, Anthony Kovinger, Eric Comas, Aymeric Stainmesse, Erwan Etienne, Sébastien Voeltzel, Sofiane Mansouri, Marlène Berceau-Falancourt, Brice Leva, Frederic Faure, Aimé Dami, Marc Antoine Costa, Jean-Jacques Tafanelli, Jean-Benoit Luciani, Jean-Jacques Casalot, Lary Charlet, Eric Ruau, Mario Issa, Carine Grenet, Christophe Billy, Loic Desquilbet
The aim of this study is to evaluate if the sweat produced by COVID-19 persons (SARS-CoV-2 PCR positive) has a different odour for trained detection dogs than the sweat produced by non COVID-19 persons. The study was conducted on 3 sites, following the same protocol procedures, and involved a total of 18 dogs. A total of 198 armpits sweat samples were obtained from different hospitals. For each involved dog, the acquisition of the specific odour of COVID-19 sweat samples required from one to four hours, with an amount of positive samples sniffing ranging from four to ten. For this proof of concept, we kept 8 dogs of the initial group (explosive detection dogs and colon cancer detection dogs), who performed a total of 368 trials, and will include the other dogs in our future studies as their adaptation to samples scenting takes more time. The percentages of success of the dogs to find the positive sample in a line containing several other negative samples or mocks (2 to 6) were 100p100 for 4 dogs, and respectively 83p100, 84p100, 90p100 and 94p100 for the others, all significantly different from the percentage of success that would be obtained by chance alone. We conclude that there is a very high evidence that the armpits sweat odour of COVID-19+ persons is different, and that dogs can detect a person infected by the SARS-CoV-2 virus. ### Competing Interest Statement The authors have declared no competing interest.
602 downloads bioRxiv animal behavior and cognition
Differences in human general intelligence or reasoning ability can be quantified with the psychometric factor g, because individual performance across cognitive tasks is positively correlated. g also emerges in mammals and birds, is correlated with brain size and may similarly reflect general reasoning ability and behavioural flexibility in these species. To exclude the alternative that these positive cross-correlations may merely reflect the general biological quality of an organism or an inevitable by-product of having brains it is paramount to provide solid evidence for the absence of g in at least some species. Here, we show that wild-caught cleaner fish Labroides dimidiatus, a fish species otherwise known for its highly sophisticated social behaviour, completely lacks g when tested on ecologically non-relevant tasks. Moreover, performance in these experiments was not or negatively correlated with an ecologically relevant task, and in none of the tasks did fish caught from a high population density site outperform fish from a low-density site. g is thus unlikely a default result of how brains are designed, and not an automatic consequence of variation in social complexity. Rather, the results may reflect that g requires a minimal brain size, and thus explain the conundrum why the average mammal or bird has a roughly 10 times larger brain relative to body size than ectotherms. Ectotherm brains and cognition may therefore be organized in fundamentally different ways compared to endotherms.
525 downloads bioRxiv animal behavior and cognition
Dominique Granjean, Dana H Al Marzooqi, Clothilde Lecoq-Julien, Hamad K Al Hammadi, Guillaume Alvergnat, Kalthoom M Al Blooshi, Salah K Al Mazrooei, Mohammed S Alhmoudi, Faisal M Al Ahbabi, Yasser S Mohammed, Nasser M Al Falasi, Noor M Almheiri, Sumaya M Al Blooshi, Quentin Muzzin, Loic Desquilbet
This study aimed to evaluate the sensitivity of 21 dogs belonging to different United Arab Emirates (UAE) Ministry of Interior (MOI), trained for COVID-19 olfactory detection. The study involved 17 explosives detection dogs, two cadaver detection dogs and two dogs with no previous detection training. Training lasted two weeks before starting the validation protocol. Sequential five and seven-cone line-ups were used with axillary sweat samples from symptomatic COVID-19 individuals (SARS-CoV-2 PCR positive) and from asymptomatic COVID-19 negative individuals (SARS-CoV-2 PCR negative). A total of 1368 trials were performed during validation, including 151 positive and 110 negative samples. Each line-up had one positive sample and at least one negative sample. The dog had to mark the positive sample, randomly positioned behind one of the cones. The dog, handler and data recorder were blinded to the positive sample location. The calculated overall sensitivities were between 71% and 79% for three dogs, between 83% and 87% for three other dogs, and equal to or higher than 90% for the remaining 15 dogs (more than two thirds of the 21 dogs). After calculating the overall sensitivity for each dog using all line-ups, matched sensitivities were calculated only including line-ups containing COVID-19 positive and negative samples strictly comparable on confounding factors such as diabetes, anosmia, asthma, fever, body pain, diarrhoea, sex, hospital, method of sweat collection and sampling duration. Most of the time, the sensitivities increased after matching. Pandemic conditions in the U.A.E., associated with the desire to use dogs as an efficient mass-pretesting tool has already led to the operational deployment of the study dogs. Future studies will focus on comparatives fields-test results including the impact of the main COVID-19 comorbidities and other respiratory tract infections.
460 downloads bioRxiv animal behavior and cognition
Considering animal welfare, animals should be kept in animal-appropriate and stress-free housing conditions in all circumstances. To assure such conditions, not only basic needs must be met, but also possibilities must be provided that allow animals in captive care to express all species-typical behaviors. Rack housing systems for snakes have become increasingly popular and are widely used; however, from an animal welfare perspective, they are no alternative to furnished terrariums. In this study, we therefore evaluated two types of housing systems for ball pythons ( Python regius ) by considering the welfare aspect animal behavior. In Part 1 of the study, ball pythons ( n = 35 ) were housed individually in a conventional rack system. The pythons were provided with a hiding place and a water bowl, temperature control was automatic, and the lighting in the room served as indirect illumination. In Part 2 of the study, the same ball pythons, after at least 8 weeks, were housed individually in furnished terrariums. The size of each terrarium was correlated with the body length of each python. The terrariums contained substrate, a hiding place, possibilities for climbing, a water basin for bathing, an elevated basking spot, and living plants . The temperature was controlled automatically, and illumination was provided by a fluorescent tube and a UV lamp . The shown behavior spectrum differed significantly between the two housing systems ( p < 0.05 ). The four behaviors basking, climbing, burrowing, and bathing could only be expressed in the terrarium. Abnormal behaviors that could indicate stereotypies were almost exclusively seen in the rack system. The results show that the housing of ball pythons in a rack system leads to a considerable restriction in species-typical behaviors; thus, the rack system does not meet the requirements for animal-appropriate housing.
448 downloads bioRxiv animal behavior and cognition
How do we mentally organize our memories of life events? Two episodes may be connected because they share a similar location, time period, activity, spatial environment, or social and emotional content. However, we lack an understanding of how each of these dimensions contributes to the perceived similarity of two life memories. We addressed this question with a data-driven approach, eliciting pairs of real-life memories from participants. Participants annotated the social, purposive, spatial, temporal, and emotional characteristics of their memories. We found that the overall similarity of memories was influenced by all of these factors, but to very different extents. Emotional features were the most consistent single predictor of overall memory similarity. Memories with different emotional tone were reliably perceived to be dissimilar, even when they occurred at similar times and places and involved similar people; conversely, memories with a shared emotional tone were perceived as similar even when they occurred at different times and places, and involved different people. A predictive model explained over half of the variance in memory similarity, using only information about (i) the emotional properties of events and (ii) the primary action or purpose of events. Emotional features may make an outsized contribution to event similarity because they provide compact summaries of an event's goals and self-related outcomes, which are critical information for future planning and decision making. Thus, in order to understand and improve real-world memory function, we must account for the strong influence of emotional and purposive information on memory organization and memory search.
384 downloads bioRxiv animal behavior and cognition
Scientific datasets are growing rapidly in scale and complexity. Consequently, the task of understanding these data to answer scientific questions increasingly requires the use of compression algorithms that reduce dimensionality by combining correlated features and cluster similar observations to summarize large datasets. Here we introduce a method for both dimension reduction and clustering called VAE-SNE (variational autoencoder stochastic neighbor embedding). Our model combines elements from deep learning, probabilistic inference, and manifold learning to produce interpretable compressed representations while also readily scaling to tens-of-millions of observations. Unlike existing methods, VAE-SNE simultaneously compresses high-dimensional data and automatically learns a distribution of clusters within the data \---| without the need to manually select the number of clusters. This naturally creates a multi-scale representation, which makes it straightforward to generate coarse-grained descriptions for large subsets of related observations and select specific regions of interest for further analysis. VAE-SNE can also quickly and easily embed new samples, detect outliers, and can be optimized with small batches of data, which makes it possible to compress datasets that are otherwise too large to fit into memory. We evaluate VAE-SNE as a general purpose method for dimensionality reduction by applying it to multiple real-world datasets and by comparing its performance with existing methods for dimensionality reduction. We find that VAE-SNE produces high-quality compressed representations with results that are on par with existing nonlinear dimensionality reduction algorithms. As a practical example, we demonstrate how the cluster distribution learned by VAE-SNE can be used for unsupervised action recognition to detect and classify repeated motifs of stereotyped behavior in high-dimensional timeseries data. Finally, we also introduce variants of VAE-SNE for embedding data in polar (spherical) coordinates and for embedding image data from raw pixels. VAE-SNE is a robust, feature-rich, and scalable method with broad applicability to a range of datasets in the life sciences and beyond. ### Competing Interest Statement The authors have declared no competing interest.
375 downloads bioRxiv animal behavior and cognition
Aberrant social behavior is a core feature of many neuropsychiatric disorders, yet the study of complex social behavior in freely moving rodents is relatively infrequently incorporated into preclinical models. This likely contributes to limited translational impact. A major bottleneck for the adoption of socially complex, ethology-rich, preclinical procedures are the technical limitations for consistently annotating detailed behavioral repertoires of rodent social behavior. Manual annotation is subjective, prone to observer drift, and extremely time-intensive. Commercial approaches are expensive and inferior to manual annotation. Open-source alternatives often require significant investments in specialized hardware and significant computational and programming knowledge. By combining recent computational advances in convolutional neural networks and pose-estimation with further machine learning analysis, complex rodent social behavior is primed for inclusion under the umbrella of computational neuroethology. Here we present an open-source package with graphical interface and workflow (Simple Behavioral Analysis, SimBA) that uses pose-estimation to create supervised machine learning predictive classifiers of rodent social behavior, with millisecond resolution and accuracies that can out-perform human observers. SimBA does not require specialized video acquisition hardware nor extensive computational background. Standard descriptive statistical analysis, along with graphical region of interest annotation, are provided in addition to predictive classifier generation. To increase ease-of-use for behavioural neuroscientists, we designed SimBA with accessible menus for pre-processing videos, annotating behavioural training datasets, selecting advanced machine learning options, robust classifier validation functions and flexible visualizations tools. This allows for predictive classifier transparency, explainability and tunability prior to, and during, experimental use. We demonstrate that this approach is flexible and robust in both mice and rats by classifying social behaviors that are commonly central to the study of brain function and social motivation. Finally, we provide a library of pose-estimation weights and behavioral predictive classifiers for resident-intruder behaviors in mice and rats. All code and data, together with detailed tutorials and documentation, are available on the SimBA GitHub repository. ### Competing Interest Statement The authors have declared no competing interest.
342 downloads bioRxiv animal behavior and cognition
A major question in evolutionary science is how did language evolve? Syntax, as the core of language, combines meaning-bearing units (words) into hierarchical structures, thereby creating new meanings. Some other mammals and birds combine meaning-bearing vocalisations, but no documented examples exist of non-human animals combining more than two meaning-bearing vocalisations. Was the two-unit threshold only surpassed in the hominid lineage? Here, we examine the positional patterning of vocal sequences of chimpanzees. We analysed 4826 vocal utterances of 46 wild adult female and male chimpanzees. We found a flexible system with 390 multi-unit vocal sequences, some showing positional or transitional regularities. Two-unit pairs embedded in three-unit sequences predictably occurred either in head or tail positions, and co-occurred with specific other elements. The capacity to organise vocal output beyond the two-unit level may thus exist in species other than humans and could be viewed as an important evolutionary step towards language.
332 downloads bioRxiv animal behavior and cognition
Objective quantification of animal behavior is crucial to understanding the relationship between brain activity and behavior. For rodents, this has remained a challenge due to the high-dimensionality and large temporal variability of their behavioral features. Inspired by the natural structure of animal behavior, the present study uses a parallel, and multi-stage approach to decompose motion features and generate an objective metric for mapping rodent behavior into the animal feature space. Incorporating a three-dimensional (3D) motion-capture system and unsupervised clustering into this approach, we developed a novel framework that can automatically identify animal behavioral phenotypes from experimental monitoring. We demonstrate the efficacy of our framework by generating an “autistic-like behavior space” that can robustly characterize a transgenic mouse disease model based on motor activity without human supervision. The results suggest that our framework features a broad range of applications, including animal disease model phenotyping and the modeling of relationships between neural circuits and behavior. ### Competing Interest Statement The authors have declared no competing interest.
319 downloads bioRxiv animal behavior and cognition
Our own unique character traits make our behavior consistent and define our individuality. Yet, this consistency does not entail that we behave repetitively like machines. Like humans, animals also combine personality traits with spontaneity to produce adaptive behavior: consistent, but not fully predictable. Here, we study an iconically rigid behavioral trait, insect phototaxis, that nevertheless also contains both components of individuality and spontaneity. In a light/dark T-maze, approximately 70% of a group of Drosophila fruit flies choose the bright arm of the T-Maze, while the remaining 30% walk into the dark. Taking the photopositive and the photonegative subgroups and re-testing them reveals the spontaneous component: a similar 70-30 distribution emerges in each of the two subgroups. Increasing the number of choices to ten choices, reveals the individuality component: flies with extremely negative first choices were more likely to show photonegative behavior in subsequent choices and vice versa. General behavioral traits, independent of light/dark preference, contributed to the development of this individuality. The interaction of individuality and spontaneity together explains why group averages, even for such seemingly stereotypical behaviors, are poor predictors of individual choices.
259 downloads bioRxiv animal behavior and cognition
Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms' sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously with real-time (60Hz) tracking performance for up to approximately 256 individuals and estimates 2D body postures and visual fields, both in open and closed-loop contexts. Additionally, TRex offers highly-accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5-46.7 times faster, and requires 2-10 times less memory, than comparable software (with relative performance increasing for more organisms and longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user interface. ### Competing Interest Statement The authors have declared no competing interest.
255 downloads bioRxiv animal behavior and cognition
Foraging is a common decision problem in natural environments. When new exploitable sites are always available, a simple optimal strategy is to leave a current site when its return falls below a single average reward rate. Here, we examined foraging in a more structured environment, with a limited number of sites that replenished at different rates and had to be revisited. When participants could choose sites, they visited fast-replenishing sites more often, left sites at higher levels of reward, and achieved a higher net reward rate. Decisions to exploit-or-leave a site were best explained with a computational model estimating separate reward rates for each site. This suggests option-specific information can be used to construct a threshold for patch leaving in some foraging settings, rather than a single average reward rate.
254 downloads bioRxiv animal behavior and cognition
Specific mate communication and recognition underlies reproduction and hence speciation. Mate communication evolves during adaptation to ecological niches and makes use of social signals and habitat cues. Our study provides new insights in Drosophila melanogaster premating olfactory communication, showing that female pheromone Z4-11Al and male pheromone cVA interact with food odour in a sex-specific manner. Furthermore, Z4-11Al, which mediates upwind flight attraction in both sexes, also elicits courtship in experienced males. Twin variants of the olfactory receptor Or69a are co-expressed in the same olfactory sensory neurons, and feed into the same glomerulus in the antennal lobe. Female pheromone Z4-11Al is perceived via Or69aB, while the food odorant (R)-linalool is a main ligand for the other variant, Or69aA. That Z4-11Al mediates courtship in experienced males, not (R)-linalool, is probably due to courtship learning. Behavioural discrimination is reflected by calcium imaging of the antennal lobe, showing distinct glomerular activation patterns by these two compounds. Male sex pheromone cVA is known to affect male and female courtship at close range, but does not elicit upwind flight attraction as a single compound, in to contrast to Z4-11Al. A blend of cVA and the food odour vinegar attracted females, while a blend of female pheromone Z4-11Al and vinegar attracted males instead. Sex-specific upwind flight attraction to blends of food volatiles and male and female pheromone, respectively, adds a new element to Drosophila olfactory premating communication and is an unambiguous paradigm for identifying the behaviourally active components, towards a more complete concept of food-pheromone odour objects. SummaryThe female-produced, species-specific volatile pheromone of D. melanogaster attracts both sexes from a distance, alone and in concert with food odorants, and elicits courtship in experienced males.
251 downloads bioRxiv animal behavior and cognition
An accurate model of the factors that contribute to individual differences in reading ability depends on data collection in large, diverse and representative samples of research participants. However, that is rarely feasible due to the constraints imposed by standardized measures of reading ability which require test administration by trained clinicians or researchers. Here we explore whether a simple, two-alternative forced choice, time limited lexical decision task (LDT), self-delivered through the web-browser, can serve as an accurate and reliable measure of reading ability. We found that performance on the LDT is highly correlated with scores on standardized measures of reading ability such as the Woodcock-Johnson Letter Word Identification test administered in the lab (r = 0.91, disattenuated r = 0.94). Importantly, the LDT reading ability measure is highly reliable (r = 0.97). After optimizing the list of words and pseudowords based on item response theory, we found that a short experiment with 76 trials (2-3 minutes) provides a reliable (r = 0.95) measure of reading ability. Thus, the self-administered, Rapid Online Assessment of Reading ability (ROAR) developed here overcomes the constraints of resource-intensive, in-person reading assessment, and provides an efficient and automated tool for effective online research into the mechanisms of reading (dis)ability. ### Competing Interest Statement The authors have declared no competing interest.
244 downloads bioRxiv animal behavior and cognition
Siamese fighting fish, Betta splendens, have been extensively studied due to their aggression and stereotypical displays. Many studies have focused on their characteristic opercular flaring, while the less aggressive and less energetically costly lateral display have been comparatively understudied. Many factors have been shown to influence aggression in Bettas, notably body length and the personality trait of boldness, however, the role that colour plays in determining an individuals aggressiveness is much less clear. The role of colour has only been briefly studied, and based on human interpretations of colour, i.e. limited to what the receivers eyes and sensory systems actually can process and discriminate, with results suggesting blue males are more aggressive than red males. Using male-male interactions, measuring opercular flaring and lateral display we found that colour and personality do play a role in determining the degree of aggressiveness in Betta splendens. Blue-finned males were more aggressive, performing longer lateral displays more frequently. Blue fins are a phenotype observed in wild type males and is likely selected for to allow visual cues to travel through the murky water they inhabit. Body mass was positively correlated with lateral display frequency, and opercular flare frequency and duration. Finally, neophobic individuals, individuals that were less willing to approach a novel object, were more aggressive, performing significantly more lateral displays. This indicates that personality may impact fighting strategy, with males either choosing to end conflicts quickly with more aggressive displays or to outlast their opponent with less energetically costly displays. ### Competing Interest Statement The authors have declared no competing interest.
232 downloads bioRxiv 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.
228 downloads bioRxiv 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.
212 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]. ### Competing Interest Statement The authors have declared no competing interest. : http://sleap.ai
210 downloads bioRxiv animal behavior and cognition
Insects can navigate efficiently in both novel and familiar environments, and this requires flexiblity in how they are guided by sensory cues. A prominent landmark, for example, can ellicit strong innate behaviours (attraction or menotaxis) but can also be used through learning as a specific directional cue to sustain navigation memory. However, the mechanisms that allow both pathways to co-exist, interact or override each other are largely unknown. Here we propose a model for behavioural integration based on the neuroanatomy of the central complex (CX) and adapted to control landmark guided behaviours. We consider a reward signal provided either by an innate attraction to landmarks or a long-term visual memory that modulates the formation of a local vector memory in the CX. Using an operant strategy for a simulated agent exploring a simple arena world with a single cue, we show how the short-term memory generated can support both innate and learned steering behaviour. In addition, we show how this architecture is consistent with observed effects of unilateral mushroom bodies (MB) lesions in ants that cause a reversion to innate behaviour. We suggest the formation of a directional memory in the CX can be interpreted as transforming rewarding (positive or negative) sensory signals into a geometrical attractiveness (or repulsion) mapping of the environment. We discuss how this scheme might represent an ideal way to combine multisensory information gathered during the exploration of an environment and support optimized cue integration.
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