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bioRxiv2016

Expanding perspectives on cognition in humans, animals, and machines

Over the past decade neuroscience has been attacking the problem of cognition with increasing vigor. Yet, what exactly is cognition, beyond a general signifier of anything seemingly complex the brain does? Here, we briefly review attempts to define, describe, explain, build, enhance and experience cognition. We highlight perspectives including psychology, molecular biology, computation, dynamical systems, machine learning, behavior and phenomenology. This survey of the landscape reveals not a clear target for explanation but a pluralistic and evolving scene with diverse opportunities for grounding future research. We argue that rather than getting to the bottom of it, over the next century, by deconstructing and redefining cognition, neuroscience will and should expand rather than merely reduce our concept of the mind.

Animal Behavior and Cognition↗

bioRxiv2017

Emphasizing the ‘positive’ in positive reinforcement: Using non-binary rewarding for training monkeys on cognitive tasks

Non-human primates constitute an indispensable model system for studying higher brain functions at the neurophysiological level. They can be trained on highly demanding cognitive tasks, and studies involving these animals elucidated the neuronal mechanisms of various cognitive and executive functions, such as visual attention, working memory, and decision-making. The training of behavioral tasks used to study these processes builds on reinforcement learning and involves many discrete stages. It may takes several months, but frequently lasts a year or longer. The training is usually based on applying a liquid reward as the reinforcer to strengthen the desired behavior, and absence of the reward if the animals response was wrong. We here propose an alternative, non-binary rewarding scheme that aims to minimize unrewarded behavior. We show the potential of this alternative scheme to significantly speed up the training of an animal at various stages, without trade-off in accessible task difficulty or task performance.

animal behavior and cognition↗

bioRxiv2023

Artificial neural networks for model identification and parameter estimation in computational cognitive models

1Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate. 2 Author SummaryComputational cognitive models occupy an important position in cognitive science research, as they offer a simple way of quantifying cognitive processes (such as how fast someone learns, or how noisy they are in choice selection), and testing which cognitive theories offer a better explanation of the behavior. To relate cognitive models to the behavioral data, researchers rely on statistical tools that require estimating the likelihood of observed data under the assumptions of the cognitive model. This is, however, not possible to do for all models as some models present significant challenges to likelihood computation. In this work, we use artificial neural networks (ANNs) to bypass likelihood computation and approximation altogether, and demonstrate the success of this approach applied to model parameter estimation and model comparison. The proposed method is a contribution to ongoing development of modeling tools which will enable cognitive researchers to test a broader range of theories of cognition.

animal behavior and cognition↗

bioRxiv2026

Emergent smartphone temporal structures reflect cognitive constraints

Fine-grained temporal structures emerge in smartphone behavioral recordings over multi-day periods. Complex systems research suggests that emergent temporal structures reflect underlying resource constraints of the system. Here we test whether cognitive abilities measured through speeded tasks (spanning fractions of a second) are reflected in emergent smartphone temporal structures spanning days, revealing how cognitive resource limitations shape naturalistic behavior. We analyzed smartphone tap interval patterns accumulated over several days and used decision tree regression models to predict performance in simple and choice reaction time tasks from these patterns. Simple reaction time was poorly predicted (R2 = 0.003), indicating that basic sensorimotor constraints play only a marginal role in shaping real-world behavioral timing. In contrast, choice reaction time was moderately predictable (R2 = 0.4), demonstrating that higher-order cognitive constraints prominently influence naturalistic temporal organization. Notably, while task performance operates at sub-second timescales, predictive temporal patterns in smartphone behavior spanned milliseconds to several seconds and was accumulated over days, revealing the broad, multi-scale influence of cognitive resource constraints on everyday behavior. Both predicted and measured choice reaction times showed age-related decline, but the decline was more pronounced in predicted values, suggesting that age-related cognitive changes may be amplified in naturalistic contexts. These findings demonstrate that emergent temporal structures in smartphone use can reveal how cognitive processes measured using speeded tasks manifest, or fail to manifest, in real-world behavior. Complex-systems approaches can bridge laboratory and naturalistic assessments of cognition, revealing which cognitive processes meaningfully constrain real-world behavior.

animal behavior and cognition↗

bioRxiv2022

Resource-Rational Account of Sequential Effects in Human Prediction

An abundant literature reports on sequential effects observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume nonstationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from .05 to .95. Although in this task the environment is static and the Bayesian belief converges, subjects predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this attractive effect, but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a repulsive effect, observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.

animal behavior and cognition↗

bioRxiv2026

Social inequity aversion and fairness preference in polar bears

Decision-making is key to survival, with choices and behaviour typically shaped by evolutionary pressures to enhance fitness and minimize loss. Inequity aversion, the tendency to respond negatively to unequal reward distributions, is therefore unexpected, as acts of fairness may be costly to the individual in the short term. In social, cooperative species, however, fairness may enhance long-term benefits through stable social interactions. Inequity aversion is believed to promote cooperative social structures and has only been demonstrated in certain social species. Yet, solitary species have not been tested, and whether this behaviour depends on social cooperative structures is unknown. Examining solitary species could clarify whether inequity aversion reflects an adaptation to cooperation or a general cognitive capacity linked to social comparison and reward evaluation. We studied inequity aversion in polar bears (Ursus maritimus), a largely solitary species, using three complementary, non-invasive cognition tests on seven bears from two zoos in the Netherlands. (1) An impunity experiment assessing effort-based inequity, in which bears performed a standardized action to obtain food rewards. (2) A choice-based experiment testing active selection between equal and unequal reward distributions. (3) A no-task control experiment assessing responses to unequal reward distribution without effort. Under effort-based inequity, bears reduced task performance and increased task latency when a partner received a superior reward. Unequal reward distributions did not affect reward acceptance independent of effort. In the choice-based paradigm, bears preferentially selected equal reward distributions with a partner present. Although generalizability to wild populations is limited, these findings demonstrate behavioural aversion of social inequity in a non-cooperative species. Inequality aversion may therefore not be restricted to social, cooperative animals but instead reflect a broadly distributed cognitive mechanism that emerges under relevant social conditions. These findings challenge current theories and advance our understanding of the cognitive foundations of fairness-related behaviour in animals.

animal behavior and cognition↗

bioRxiv2025

Hybrid Neural-Cognitive Models Reveal Flexible Context-Dependent Information Processing in Reversal Learning

Reversal learning tasks provide a key paradigm for studying behavioral flexibility, requiring individuals to update choices in response to shifting reward contingencies. While reinforcement learning (RL) models have been widely used to provide interpretable explanations of human behavior on similar learning tasks, recent work has revealed that they often fail to fully account for the complexity of learning dynamics. In contrast, artificial neural networks (ANNs) often achieve substantially higher predictive accuracy, but lack the interpretability afforded by classical RL models. To bridge this gap, we introduce HybridRNNs--neural-cognitive models that integrate RL-inspired structures with flexible recurrent architectures. Among them, Context-ANN incorporates latent reward history and choice perseverance, and demonstrates improved alignment with human behavior compared to traditional RL models across two datasets. While not perfectly replicating human strategies, Context-ANN achieves comparable predictive accuracy to generic RNNs and offers interpretable value representations. Additional analyses of hidden dynamics reveal structured, context-sensitive internal states that adapt following reversals. These results suggest that humans may rely on more flexible or context-sensitive learning strategies even in simple reversal tasks, and highlight the potential of HybridRNNs as cognitive models that balance interpretability and predictive accuracy.

animal behavior and cognition↗

bioRxiv2019

Innovation in solitary bees is driven by exploration, shyness and activity levels

Behavioural innovation is widely considered an important mechanism by which animals respond to novel environmental challenges, including those induced by human activities. Despite its functional and ecological relevance, much of our current understanding of the innovation process comes from studies in vertebrates. Understanding innovation processes in insects has lagged behind partly because they are not perceived to have the cognitive machinery required to innovate. This perception is however challenged by recent evidence demonstrating sophisticated cognitive capabilities in insects despite their small brains. Here, we study the innovation capacity of a solitary bee (Osmia cornuta) in the laboratory by exposing naive individuals to an obstacle removal task. We also studied the underlying cognitive and non-cognitive mechanisms through a battery of experimental tests designed to measure learning, exploration, shyness and activity levels. We found that solitary bees can innovate, with 11 of 29 individuals (38%) being able to solve a new task consisting in lifting a lid to reach a reward. The propensity to innovate was uncorrelated with learning capacities, but increased with exploration, boldness and activity. These results provide solid evidence that non-social insects can innovate, and highlight the importance of interpreting innovation in the light of non-cognitive processes.

animal behavior and cognition↗

bioRxiv2021

Detour learning ability and the effect of novel sensory cues on learning in Australian bull ants, Myrmecia midas

Many animals navigate in a structurally complex environment which requires them to detour around physical barriers that they encounter. While many studies in animal cognition suggest that they are able to adeptly avoid obstacles, it is unclear whether a new route is learned to navigate around these barriers and, if so, what sensory information may be used to do so. We investigated detour learning ability in the Australian bull ant, Myrmecia midas, which primarily uses visual landmarks to navigate. We first placed a barrier on the ants natural path of their foraging tree. Initially, 46% of foragers were unsuccessful in detouring the obstacle. In subsequent trips, the ants became more successful and established a new route. We observed up to eight successful foraging trips detouring around the barrier. When we subsequently changed the position of the barrier, made a new gap in the middle of the obstacle, or removed the barrier altogether, ants mostly maintained their learned motor routine, detouring with a similar path as before, suggesting that foragers were not relying on barrier cues and therefore learned a new route around the obstacle. In additional trials, when foragers encountered new olfactory or tactile cues, or the visual environment was blocked, their navigation was profoundly disrupted. These results suggest that changing sensory information, even in modalities that foragers do not usually need for navigation, drastically affects the foragers ability to successful navigate. Subject CategoryNeuroscience and Cognition

animal behavior and cognition↗

bioRxiv2025

Charitability, Compulsion, & the Cost of Control

Human decision-makers have a well-established preference for free choice. We used a hierarchical gambling task to assess whether this preference extends to environments in which decision outcomes benefit others, specifically charitable organizations. In neurotypical individuals (n=100), we found a reduction in the preference for free choice across self- and charity-benefiting environments when freely chosen options yielded divergent sensory outcome distributions, but not when the outcome distributions of available options were identical - i.e., only when free choice afforded instrumental control. In individuals with obsessive-compulsive traits (n=108) this pattern was reversed, suggesting an unchanged preference for control in charity-benefiting environments. Computational cognitive modeling confirmed a selective reduction in the subjective utility of instrumental control, dissociable from that of free choice, outcome divergence, and monetary reward maximization, in neurotypical individuals gambling for charities. We interpret these results as reflecting cost-benefit analyses weighing the deployment of cognitive effort against the value of control.

animal behavior and cognition↗

bioRxiv2025

A Reinforcement Learning and Sequential Sampling Model Constrained by Gaze Data

Reinforcement learning models can be combined with sequential sampling models to fit choice-RT data. The combined models, known as RL-SSMs, explain a wide range of choice-RT patterns in repeated decision tasks. The present study shows how constraining an RL-SSM with eye gaze data can further enhance its predictive ability. Our model assumes that learned option values and relative gaze independently influence the accumulation of evidence prior to choice. We evaluated the model on data from two eye-tracking experiments (total N = 133) and find that it makes better out-of-sample predictions than other models with different ways of integrating values and gaze at the decision stage. Further, we show that it captures a variety of empirical effects, including the finding that choices become more accurate as the higher-value option receives a greater proportion of the total fixation time. The model can be used to understand how learned option values interact with visual attention to influence choice, joining together two major--but mostly separate--research traditions in the cognitive science of decision making.

animal behavior and cognition↗

bioRxiv2019

Search as a simple take-the-best heuristic

Humans commonly engage in a variety of search behaviours, for example when looking for an object, a partner, information, or a solution to a complex problem. The success or failure of a search strategy crucially depends on the structure of the environment and the constraints it imposes on the individuals. Here we focus on environments in which individuals have to explore the solution space gradually and where their reward is determined by one unique solution they choose to exploit. This type of environment has been relatively overlooked in the past despite being relevant to numerous real-life situations, such as spatial search and various problem-solving tasks.\n\nBy means of a dedicated experimental design, we show that the search behaviour of experimental participants can be well described by a simple heuristic model. Both in rich and poor solution spaces, a take-the-best procedure that ignores all but one cue at a time is capable of reproducing a diversity of observed behavioural patterns. Our approach, therefore, sheds lights on the possible cognitive mechanisms involved in human search.

animal behavior and cognition↗

bioRxiv2019

Short-term reciprocity in macaques social decision-making

Primates live in complex social environments, where individuals create meaningful networks by adapting their behavior according to past experiences with others. Although free-ranging primates do show signs of reciprocity, experiments in more controlled environments have mainly failed to reproduce such social dynamics. Hence, the cognitive and neural processes allowing monkeys to reciprocate during social exchanges remains elusive. Here, pairs of long-tailed macaques (Macaca fascicularis) took turns into a social decision task involving the delivery of positive (juice reward) or negative (airpuff) outcomes. By analyzing the contingencies of one partners past decisions on the others future decisions, we demonstrate the presence of reciprocity, but only for the exchange of negative outcomes. Importantly, to display this decisional bias, the monkey needs to witness its partners decisions, since non-social deliveries of the same outcome did not have such effect. Withholding of negative outcomes also predicted future social decisions, which suggest that the observed tit-for-tat strategy may not only be motivated by retaliation after receiving an airpuff but also by the gratefulness of not having received one. These results clarify the apparent dichotomy within the scientific literature of reciprocity in non-human primates and suggest that their social cognition comprise revenge and gratitude.

animal behavior and cognition↗

bioRxiv2021

Cue-motivated reward seeking is negatively regulated by expected reward magnitude in Pavlovian-instrumental transfer

The Pavlovian-instrumental transfer (PIT) paradigm is widely used to assay the motivational influence of reward-predictive cues, reflected by their ability to invigorate instrumental behavior. Leading theories assume that a cues motivational properties are tied to predicted reward value. We outline an alternative view which recognizes that reward-predictive cues may suppress rather than motivate instrumental behavior under certain conditions, an effect termed positive conditioned suppression. We posit that cues signaling imminent reward delivery tend to inhibit instrumental behavior, which is exploratory by nature, in order to facilitate efficient retrieval of the expected reward. According to this view, the motivation to engage in instrumental behavior during a cue should be inversely related to the value of the predicted reward, since there is more to lose by failing to secure a high-value reward than a low-value reward. We tested this hypothesis in rats using a PIT protocol known to induce positive conditioned suppression. In Experiment 1, cues signaling different reward magnitudes elicited distinct response patterns. Whereas the 1-pellet cue increased instrumental behavior, cues signaling 3 or 9 pellets suppressed instrumental behavior and elicited high levels of food-port activity. Experiment 2 found that reward-predictive cues suppressed instrumental behavior and increased food-port activity in a flexible manner that was disrupted by post-training reward devaluation. Further analyses suggest that these findings were not driven by overt competition between the instrumental and food-port responses. We discuss how the PIT task may provide a useful tool for studying cognitive control over cue-motivated behavior in rodents.

animal behavior and cognition↗

bioRxiv2026

Silent hippocampal engrams shape future memory processing

The hippocampus harbors silent memory traces even when memory recall is absent. It is unknown why these traces persist and whether they remain sensitive to future episodes. We trained mice on the hippocampal-dependent novel object place recognition task and tracked the transition of the engram from a latent to a silent phase, which still held a functional memory trace. When mice were exposed to a weaker, modified version of their original memories, silent engrams led to better consolidation of these new events than their latent predecessors. Bidirectional manipulation of the engrams state, as well as artificially prolonging its latent state, successfully reversed its effect on the second memory. The silent engram state consistently displayed higher neuronal reactivation during the second event compared to the latent one, which may explain its preferential effect on its consolidation. Our results reveal previously unknown cognitive and activation dynamics for this behaviorally silent state.

animal behavior and cognition↗

bioRxiv2022

Long-term memory in frog-eating bats

Long-term memory has clear advantages but also has neurological and behavioral costs1-3. Given these opposing selection pressures, understanding how long memories last can shed light on how memory enhances or constrains animals abilities to exploit their niches. Although testing memory retention in wild animals is difficult, it is important because captive conditions do not reflect the complex cognitive demands of wild environments, and long-term captivity changes the brain4 (past studies on nonhuman long-term memory are reviewed in Table S1). Here, we trained wild-caught frog-eating bats (Trachops cirrhosus) to find prey by flying to a novel acoustic cue, released them back into the wild, and then re-captured some of them 1-4 years later. When re-tested, all eight experienced bats that previously learned the novel prey sounds flew to those sounds within seconds, whereas 17 naive bats tested with the same sounds showed weak responses. Experienced bats also showed behavior indicating generalization of memories between novel sounds and rewards over time. The frog-eating bats remarkably long memory for novel acoustic cues indicates that an ability to remember rarely encountered prey may be advantageous for this predator, and suggests hitherto unknown cognitive abilities in bats. O_TBL View this table: org.highwire.dtl.DTLVardef@15a8294org.highwire.dtl.DTLVardef@132eb3forg.highwire.dtl.DTLVardef@1f29583org.highwire.dtl.DTLVardef@135dff2org.highwire.dtl.DTLVardef@19cfa_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable S1:C_FLOATNO O_TABLECAPTIONStudies of animal memory durations documenting memories of months or years. Memory duration = the longest duration demonstrated in the study. Wild or captive = where animals spent the time of memory retention; some "captive" animals were originally wild caught. Context = the ecological context and memory task being tested. 2-AFC = Two-alternative forced choice. C_TABLECAPTION C_TBL

animal behavior and cognition↗

bioRxiv2019

Sex differences in cognitive performance, style and domain relationships in mosquitofish (Gambusia affinis)

Given that the sexes often differ in their ecological and sexual selection pressures, sex differences in cognitive properties are likely. While research on sexually dimorphic cognitionoften focuses on performance, it commonly overlooks how sexes diverge across multiple cognitive tasks (cognitive domains) and in behaviors associated with cognitive performance (cognitive style). We tested male and female western mosquitofish (Gambusia affinis) in three cognitive tasks: associative learning (numerical discrimination), cognitive flexibility (detour task), and spatio-temporal learning (shuttlebox). We characterized statistical relationships between cognitive performances and cognitive style during the associative learning task with measures of anxiety, boldness, exploration, reaction time, and activity. We found sex differences in performance, cognitive style, and the relationships between cognitive domains. Females outperformed males in spatio-temporal learning task, while the sexes performed equally in associate learning and cognitive flexibility assays. Females (but not males) exhibited a fast-exploratory cognitive style during associative learning trials. Meanwhile, only males showed a significant positive relationship between domains (associative learning and cognitive flexibility). We propose that these sexually dimorphic cognitive traits result from strong sexual conflict in this taxon; and emphasize the need to explore suites of sex-specific cognitive traits and broader comparative work examining sexual selection and cognition. HighlightsO_LIMales and females perform at similar levels in associative learning and cognitive flexibility assays, but females tend to outperform males on a spatio-temporal learning task. C_LIO_LIFemale performance in associative learning trials (numerical discrimination task) can be predicted by cognitive style behaviors (exploration, reaction time, and activity); whereas male performance cannot. C_LIO_LIMales, but not females, show a predictive relationship between associative learning and cognitive flexibility performance. C_LIO_LIOur results demonstrate that sex differences in cognition extend beyond performance into cognitive style and domain relationships, suggesting that investigations into animal personality and cognition require more comprehensive characterization. C_LI

animal behavior and cognition↗

bioRxiv2021

Verapamil/Curcumin treatment attenuates the behavioral alterations observed in Williams Syndrome mice by regulation of MAPK pathway and Microglia overexpression

Williams-Beuren syndrome (WBS) is a rare neurodevelopmental disorder characterized by a distinctive cognitive phenotype for which there currently are not any effective treatments. We investigated the progression of behavioral deficits present in CD (complete deletion) mice, a rodent model of WBS, after chronic treatment with curcumin, verapamil and a combination of both. These compounds have been proven to have beneficial effects over different cognitive aspects of various murine models and thus, may have neuroprotective effects in WBS. Treatment was administered orally dissolved in drinking water. A set of behavioral tests demonstrated the efficiency of combinatorial treatment. Some histological and molecular analyses were performed to analyze the effects of treatment and its underlying mechanism in CD mice. Behavioral improvement correlates with the molecular recovery of several affected pathways regarding MAPK signaling, in tight relation with the control of synaptic transmission. Moreover, CD mice showed an increased activated microglia density in different brain regions, which was prevented by treatment. Therefore, results show that treatment prevented behavioral deficits by recovering altered gene expression in cortex of CD mice, reducing activated microglia and normalizing Bdnf expression levels. These findings unravel the mechanisms underlying the beneficial effects of this novel treatment on behavioral deficits observed in CD mice, and suggest that the combination of curcumin and verapamil could be a potential candidate to treat the cognitive impairments in WBS patients.

animal behavior and cognition↗