The Life of Behavior

From Multimodal Sensorimotor Integration to Semantic Networks: A Phylogenetic Perspective on Speech and Language Evolution

This integrative perspective article delves into the crucial role of the superior temporal sulcus (STS) and adjacent perisylvian regions in multimodal integration and semantic cognition. Drawing from a wide range of neuroscientific evidence, including studies on nonhuman primates and human brain evolution, the article highlights the significance of the STS in linking auditory and visual modalities, particularly in the establishment of associative links between auditory inputs and visual stimuli. Furthermore, it explores the expansion of the human temporal lobe and its implications for the amplification of multisensory regions, emphasizing the role of these regions in the development of word-related concepts and semantic networks. We propose a posteroanterior gradient organization in the human temporal lobe, from low-level sensorimotor integration in posterior regions to higher-order, transmodal semantic control in anterior portions, particularly in the anterior temporal lobe. Overall, this perspective provides a comprehensive overview of the functional and evolutionary aspects of the STS and adjacent regions in multimodal integration and semantic cognition, offering valuable insights for future research in this field.

A neural correlate of individual odor preference in Drosophila

Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical origins of this individuality. Here, we demonstrate a neural correlate of Drosophila odor preference behavior in the olfactory sensory periphery. Namely, idiosyncratic calcium responses in projection neuron (PN) dendrites and densities of the presynaptic protein Bruchpilot in olfactory receptor neuron (ORN) axon terminals correlate with individual preferences in a choice between two aversive odorants. The ORN-PN synapse appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. Simulating microscale stochasticity in ORN-PN synapses of a 3062 neuron model of the antennal lobe recapitulates patterns of variation in PN calcium responses matching experiments. Conversely, stochasticity in other compartments of this circuit does not recapitulate those patterns. Our results demonstrate how physiological and microscale structural circuit variations can give rise to individual behavior, even when genetics and environment are held constant.

DomeVR: Immersive virtual reality for primates and rodents

Immersive virtual reality (VR) environments are a powerful tool to explore cognitive processes ranging from memory and navigation to visual processing and decision making-and to do so in a naturalistic yet controlled setting. As such, they have been employed across different species, and by a diverse range of research groups. Unfortunately, designing and implementing behavioral tasks in such environments often proves complicated. To tackle this challenge, we created DomeVR, an immersive VR environment built using Unreal Engine 4 (UE4). UE4 is a powerful game engine supporting photo-realistic graphics and containing a visual scripting language designed for use by non-programmers. As a result, virtual environments are easily created using drag-and-drop elements. DomeVR aims to make these features accessible to neuroscience experiments. This includes a logging and synchronization system to solve timing uncertainties inherent in UE4; an interactive GUI for scientists to observe subjects during experiments and adjust task parameters on the fly, and a dome projection system for full task immersion in non-human subjects. These key features are modular and can easily be added individually into other UE4 projects. Finally, we present proof-of-principle data highlighting the functionality of DomeVR in three different species: human, macaque and mouse.

Are glucagon-like peptide-1 receptor agonists anti-consummatory drugs?

Incretin-based treatments, such as glucagon-like peptide-1 receptor (GLP-1R) agonists (eg liraglutide and semaglutide), have rapidly transformed obesity treatment. The well-documented weight loss effect from these agents is considered to be primarily a result of their actions on food intake, but frequent anecdotal reports from varied sources have suggested that they might also broadly affect consummatory behavior, including alcohol and drugs of abuse, suggesting a potential modulatory effect on reward behavior. Herein, we critically review the extant literature on the behavioral effects of GLP-1R agonists in humans, including their impact on feeding behavior, alcohol/drug intake, and overall reward response. We also consider the physiological and neurobiological underpinnings of GLP-1 actions, with a focus on its distinct central and peripheral roles, as well as its relationships with the broader energy homeostasis network. We conclude with a discussion on the implications of this line of research on how behavior is conceptualized, and the potential future directions for research.

Video analysis of interaction in schizophrenia reveals functionally relevant abnormalities

OBJECTIVE

Deficits of dyadic social interaction seem to diminish social functioning in schizophrenia. However, most previous studies are of a limited ecological validity due to decontextualized experimental conditions far off from real-world interaction. In this pilot study, we thus exposed participants to a more real-world-like situation to generate new hypotheses for research and therapeutic interventions.

METHODS

Dyads of either participants with schizophrenia (n = 21) or control participants without mental disorder (n = 21) were presented with a 5-min emotionally engaging movie. The subsequent uninstructed dyadic interaction was videotaped and analyzed by means of a semi-quantitative, software-supported behavioral analysis.

RESULTS

The patients with schizophrenia showed significant abnormalities regarding their social interaction, such as more negative verbalizations, a more open display of negative affect and gaze abnormalities. Their interaction behavior was mostly characterized by neutral affect, silence and avoidance of direct eye contact. Neutral affect was associated with poorer psychosocial performance. Verbal intelligence and empathy were associated with positive interaction variables, which were also not impaired by psychotic symptom severity.

CONCLUSION

In this real-world-like dyadic interaction, participants with schizophrenia show distinct abnormalities that are relevant to psychosocial performance and consistent with a hypothesized lack of attunement to interaffective situations.

Ecological brain: reframing the study of human behaviour and cognition

The last decade has seen substantial advances in the capacity to record behaviour and neural activity in humans in real-world settings, to simulate real-world situations in laboratory settings and to apply sophisticated analyses to large-scale data. Along with these developments, a growing number of groups has begun to advocate for real-world neuroscience and cognitive science. Here, we review the arguments and the available methods for real-world research and outline an overarching framework that embeds key ideas proposed in the literature integrating them into a cyclic process of 'bringing the lab to the real world' (recording behavioural and neural activity in real-world settings) and 'bringing the real-world to the lab' (manipulating the environments in which behaviours occur in the laboratory) that combines exploratory and confirmatory research and is interdisciplinary (including those sciences concerned with the natural, built or virtual environment). We highlight the benefits brought by this framework emphasizing the greater potential for novel discovery, theory development and human-centred applications to the environment.

An Active-Inference Approach to Second-Person Neuroscience

Social neuroscience has often been criticized for approaching the investigation of the neural processes that enable social interaction and cognition from a passive, detached, third-person perspective, without involving any real-time social interaction. With the emergence of second-person neuroscience, investigators have uncovered the unique complexity of neural-activation patterns in actual, real-time interaction. Social cognition that occurs during social interaction is fundamentally different from that unfolding during social observation. However, it remains unclear how the neural correlates of social interaction are to be interpreted. Here, we leverage the active-inference framework to shed light on the mechanisms at play during social interaction in second-person neuroscience studies. Specifically, we show how counterfactually rich mutual predictions, real-time bodily adaptation, and policy selection explain activation in components of the default mode, salience, and frontoparietal networks of the brain, as well as in the basal ganglia. We further argue that these processes constitute the crucial neural processes that underwrite bona fide social interaction. By placing the experimental approach of second-person neuroscience on the theoretical foundation of the active-inference framework, we inform the field of social neuroscience about the mechanisms of real-life interactions. We thereby contribute to the theoretical foundations of empirical second-person neuroscience.

The behavior patterns of giraffes (Giraffa camelopardalis) housed across 18 US zoos

Interpreting animal behavior in the context of welfare can be inherently challenging given the limited behavior data available for many species housed in zoos. Describing common behavior patterns may help animal managers by providing additional background when assessing the individuals in their care. Although valuable, these efforts require a large, collaborative approach and have, consequently, been rare. Here, we share the behavior patterns of zoo-housed giraffes, an iconic and commonly housed megafauna in zoos. Behavior data were evaluated for 66 giraffes living across 18 AZA-accredited zoos using the ZooMonitor Community platform. Data were recorded during 10-minute observation sessions. Observations were conducted during daytime hours over the course of approximately one year at each zoo (mean total observed time per individual = 23.2 hr). The most common behaviors observed were feeding/ foraging behaviors, which accounted for 38.6% of the mean visible time budget across giraffes. Time spent in these behaviors varied by individual and ranged from 14.3% to 69.3% of visible time. Stereotypic behaviors occurred in all study individuals, with oral stereotypic behaviors being most common. Although prevalent, stereotypic behaviors varied considerably across giraffes, with some individuals exhibiting these behaviors only on a few occasions to an individual that exhibited these behaviors once every few minutes. This study provides a robust evaluation of giraffe behavior across zoos to present a picture of their common behavior patterns in managed care. We hope these multi-institutional behavior patterns can provide perspective to aid animal managers in evaluating giraffes in their care.

AI-enhanced analysis of naturalistic social interactions characterizes interaffective impairments in schizophrenia

Social deficits in schizophrenia have been attributed to an impaired attunement to mutual interaction, or "interaffectivity". While impairments in emotion recognition and facial expressivity in schizophrenia have been consistently reported, findings on mimicry and social synchrony are inconsistent, and previous studies have often lacked ecological validity. To investigate interaffective behavior in dyadic interactions in a real-world-like setting, 20 individuals with schizophrenia and 20 without mental disorder played a cooperative board game with a previously unacquainted healthy control participant. Facial expression analysis was conducted using Affectiva Emotion AI in iMotions 9.3. The contingency and state space distribution of emotional facial expressions was assessed using Mangold INTERACT. Psychotic symptoms, subjective stress, affectivity and game experience were evaluated through questionnaires. Due to a considerable between-group age difference, age-adjusted ANCOVA was performed. Overall, despite an unchanged subjective experience of the social interaction, individuals with schizophrenia exhibited reduced responsiveness to positive affective stimuli. Subjective game experience did not differ between groups. Descriptively, facial expressions in schizophrenia were generally more negative, with increased sadness and decreased joy. Facial mimicry was impaired specifically regarding joyful expressions in schizophrenia, which correlated with blunted affect as measured by the SANS. Dyadic interactions involving persons with schizophrenia were less attracted toward mutual joyful affective states. Only unadjusted for age, in the absence of emotional stimuli from their interaction partner, individuals with schizophrenia showed more angry and sad expressions. These impairments in interaffective processes may contribute to social dysfunction in schizophrenia and provide new avenues for future research.

The big mixup: Neural representation during natural modes of primate visual behavior

The primate brain has evolved specialized visual capacities to navigate complex physical and social environments. Researchers studying cortical circuits underlying these capacities have traditionally favored the use of simplified tasks and brief stimulus presentations in order to isolate cognitive variables with tight experimental control. As a result, operational theories about visual brain function have come to emphasize feature detection, hierarchical stimulus encoding, top-down task modulation, and functional segregation in distinct cortical areas. Recently, however, experimental paradigms combining natural behavior with electrophysiological recordings have begun to offer a distinctly different portrait of how the brain takes in and analyzes its visual surroundings. The present article reviews recent work in this area, highlighting some of the more surprising findings in domains of social vision and spatial navigation along with shifts in thinking that have begun to emanate from this approach.

Individualistic reward-seeking strategies that predict response to nicotine emerge among isogenic male mice living in a micro-society

Individual animals differ in their traits and preferences, which shape their social interactions, survival, and susceptibility to disease, including addiction. Nicotine use is highly heterogenous and has been linked to the expression of personality traits. Although these relationships are well documented, we have limited understanding of the neurophysiological mechanisms that give rise to distinct behavioral profiles and their connection to nicotine susceptibility. To address this question, we conducted a study using a semi-natural and social environment called "Souris-City" to observe the long-term behavior of individual male mice. Souris-City provided both a communal living area and a separate test area where mice engaged in a reward-seeking task isolated from their peers. Mice developed individualistic reward-seeking strategies when choosing between water and sucrose in the test compartment, which, in turn, predicted how they adapted to the introduction of nicotine as a reinforcer. Moreover, the profiles mice developed while isolated in the test area correlated with their behavior within the social environment, linking decision-making strategies to the expression of behavioral traits. Neurophysiological markers of adaptability within the dopamine system were apparent upon nicotine challenge and were associated with specific profiles. Our findings suggest that environmental adaptations influence behavioral traits and sensitivity to nicotine by acting on dopaminergic reactivity in the face of nicotine exposure, potentially contributing to addiction susceptibility. These results further emphasize the importance of understanding interindividual variability in behavior to gain insight into the mechanisms of decision-making and addiction.

The central role of the individual in the history of brains

Every species' brain, body and behavior is shaped by the contingencies of their evolutionary history; these exert pressures that change their developmental trajectories. There is, however, another set of contingencies that shape us and other animals: those that occur during a lifetime. In this perspective piece, we show how these two histories are intertwined by focusing on the individual. We suggest that organisms--their brains and behaviors--are not solely the developmental products of genes and neural circuitry but individual centers of action unfolding in time. To unpack this idea, we first emphasize the importance of variation and the central role of the individual in biology. We then go over "errors in time" that we often make when comparing development across species. Next, we reveal how an individual's development is a process rather than a product by presenting a set of case studies. These show developmental trajectories as emerging in the contexts of the "the actual now" and "the presence of the past". Our consideration reveals that individuals are slippery-they are never static; they are a set of on-going, creative activities. In light of this, it seems that taking individual development seriously is essential if we aspire to make meaningful comparisons of neural circuits and behavior within and across species.

Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages

Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual's behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Global Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11263-024-02118-3.

Measuring the replicability of our own research

In the study of transgenic mouse models of neurodevelopmental and neurodegenerative disorders, we use batteries of tests to measure deficits in behaviour and from the results of these tests, we make inferences about the mental states of the mice that we interpret as deficits in "learning", "memory", "anxiety", "depression", etc. This paper discusses the problems of determining whether a particular transgenic mouse is a valid mouse model of disease X, the problem of background strains, and the question of whether our behavioural tests are measuring what we say they are. The problem of the reliability of results is then discussed: are they replicable between labs and can we replicate our results in our own lab? This involves the study of intra- and inter- experimenter reliability. The variables that influence replicability and the importance of conducting a complete behavioural phenotype: sensory, motor, cognitive and social emotional behaviour are discussed. Then the thorny question of failure to replicate is examined: Is it a curse or a blessing? Finally, the role of failure in research and what it tells us about our research paradigms is examined.

Toward a neuroscience of natural behavior

One of the most exciting new developments in systems neuroscience is the progress being made toward neurophysiological experiments that move beyond simplified laboratory settings and address the richness of natural behavior. This is enabled by technological advances such as wireless recording in freely moving animals, automated quantification of behavior, and new methods for analyzing large data sets. Beyond new empirical methods and data, however, there is also a need for new theories and concepts to interpret that data. Such theories need to address the particular challenges of natural behavior, which often differ significantly from the scenarios studied in traditional laboratory settings. Here, we discuss some strategies for developing such novel theories and concepts and some example hypotheses being proposed.

Natural behavior in everyday settings

Infant behaviors-walking, vocalizing, playing, interacting with others, and so on-offer an unparalleled window into learning and development. The study of infants requires strategic choices about what to observe, where, when, and how. We argue that loosening study constraints-by allowing infants and caregivers to do whatever they choose, wherever they choose, and with whatever materials they choose-promises to reveal a deep understanding of the everyday data on which learning builds. We show that observations of infants' natural behavior yield unique insights into the nature of visual exploration, object play, posture and locomotion, proximity to caregiver, and communication. Furthermore, we show that by situating the study of behavior in ecologically-valid settings, researchers can gain purchase on the contextual regularities that frame learning. We close by underscoring the value of studies at every point on the research continuum-from cleverly controlled lab-based tasks to fully natural observations in everyday environments. Acceleration in the science of behavior rests on leveraging expertise across disciplines, theoretical positions, and methodological approaches.

The importance of choice and agency in animal models of addiction

The aim of this short commentary is twofold. First, it uncovers and outlines in broad strokes a historical, albeit oft-overlooked, trend toward a growing place for choice and agency in the design of animal models of addiction. Next, it tries to draw from this historical trend some perspectives and implications for future research.

The choice-wide behavioral association study: data-driven identification of interpretable behavioral components

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with ubiquitous movements responsible for our posture, facial expressions, ability to actively sample our sensory environments, and other critical processes. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes, making it challenging to dissociate the neural dynamics that support cognitive processes from those supporting related movements. In such cases, a critical issue is whether cognitive processes are separable from related movements, or if they are driven by common neural mechanisms. Here, we demonstrate how the separability of cognitive and motor processes can be assessed, and, when separable, how the neural dynamics associated with each component can be isolated. We establish a novel two-context behavioral task in mice that involves multiple cognitive processes and show that commonly observed dynamics taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories. Further, properly accounting for movement revealed that largely separate populations of cells encode cognitive and motor variables, in contrast to the 'mixed selectivity' often reported. Accurately isolating the dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function and evaluating the function of the cell types of which neural circuits are composed.

Fantastic beasts and how to study them: rethinking experimental animal behavior

Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.

Learning of object-in-context sequences in freely-moving macaques

Flexible learning is a hallmark of primate cognition, which arises through interactions with changing environments. Studies of the neural basis for this flexibility are typically limited by laboratory settings that use minimal environmental cues and restrict interactions with the environment, including active sensing and exploration. To address this, we constructed a 3-D enclosure containing touchscreens on its walls, for studying cognition in freely moving macaques. To test flexible learning, two monkeys completed trials consisting of a regular sequence of object selections across four touchscreens. On each screen, the monkeys had to select by touching the sole correct object item ('target') among a set of four items, irrespective of their positions on the screen. Each item was the target on exactly one screen of the sequence, making correct performance conditioned on the spatiotemporal sequence rule across screens. Both monkeys successfully learned multiple 4-item sets (N=14 and 22 sets), totaling over 50 and 80 unique, conditional item-context memoranda, with no indication of capacity limits. The enclosure allowed freedom of movements leading up to and following the touchscreen interactions. To determine whether movement economy changed with learning, we reconstructed 3D position and movement dynamics using markerless tracking software and gyroscopic inertial measurements. Whereas general body positions remained consistent across repeated sequences, fine head movements varied as monkeys learned, within and across sequence sets, demonstrating learning set or "learning to learn". These results demonstrate monkeys' rapid, capacious, and flexible learning within an integrated, multisensory 3-D space. Furthermore, this approach enables the measurement of continuous behavior while ensuring precise experimental control and behavioral repetition of sequences over time. Overall, this approach harmonizes the design features that are needed for electrophysiological studies with tasks that showcase fully situated, flexible cognition.

Within arms reach: Physical proximity shapes mother-infant language exchanges in real-time

During everyday interactions, mothers and infants achieve behavioral synchrony at multiple levels. The ebb-and-flow of mother-infant physical proximity may be a central type of synchrony that establishes a common ground for infant-mother interaction. However, the role of proximity in language exchanges is relatively unstudied, perhaps because structured tasks-the common setup for observing infant-caregiver interactions-establish proximity by design. We videorecorded 100 mothers (U.S. Hispanic N = 50, U.S. Non-Hispanic N = 50) and their 13- to 23-month-old infants during natural activity at home (1-to-2 h per dyad), transcribed mother and infant speech, and coded proximity continuously (i.e., infants and mother within arms reach). In both samples, dyads entered proximity in a bursty temporal pattern, with bouts of proximity interspersed with bouts of physical distance. As hypothesized, Non-Hispanic and Hispanic mothers produced more words and a greater variety of words when within arms reach than out of arms reach. Similarly, infants produced more utterances that contained words when close to mother than when not. However, infants babbled equally often regardless of proximity, generating abundant opportunities to play with sounds. Physical proximity expands opportunities for language exchanges and infants' communicative word use, although babies accumulate massive practice babbling even when caregivers are not proximal.

Everyone knows what behavior is but they just don't agree on it

Studying "behavior" lies at the heart of many disciplines. Nevertheless, academics rarely provide an explicit definition of what "behavior" actually is. What range of definitions do people use, and how does that vary across disciplines? To answer these questions, we have developed a survey to probe what constitutes "behavior." We find that academics adopt different definitions of behavior according to their academic discipline, animal model that they work with, and level of academic seniority. Using hierarchical clustering, we identify at least six distinct types of "behavior" which are used in seven distinct operational archetypes of "behavior." Individual respondents have clear consistent definitions of behavior, but these definitions are not consistent across the population. Our study is a call for academics to clarify what they mean by "behavior" wherever they study it, with the hope that this will foster interdisciplinary studies that will improve our understanding of behavioral phenomena.

Sex differences in patch-leaving foraging decisions in rats

The ubiquity, importance, and sophistication of foraging behavior makes it an ideal platform for studying naturalistic decision making in animals. We developed a spatial patch-foraging task for rats, in which subjects chose how long to remain in one foraging patch as the rate of food earnings steadily decreased. The cost of seeking out a new location was varied across sessions. The behavioral task was designed to mimic the structure of natural foraging problems, where distinct spatial locations are associated with different reward statistics, and decisions require navigation and movement through space. Male and female Long-Evans rats generally followed the predictions of theoretical models of foraging, albeit with a consistent tendency to persist with patches for too long compared to behavioral strategies that maximize food intake rate. The tendency to choose overly-long patch residence times was stronger in male rats. We also observed sex differences in locomotion as rats performed the task, but these differences in movement only partially accounted for the differences in patch residence durations observed between male and female rats. Together, these results suggest a nuanced relationship between movement, sex, and foraging decisions.

Sex differences in patch-leaving foraging decisions in rats

The ubiquity, importance, and sophistication of foraging behavior makes it an ideal platform for studying naturalistic decision making in animals. We developed a spatial patch-foraging task for rats, in which subjects chose how long to remain in one foraging patch as the rate of food earnings steadily decreased. The cost of seeking out a new location was varied across sessions. The behavioral task was designed to mimic the structure of natural foraging problems, where distinct spatial locations are associated with different reward statistics, and decisions require navigation and movement through space. Male and female Long-Evans rats generally followed the predictions of theoretical models of foraging, albeit with a consistent tendency to persist with patches for too long compared to behavioral strategies that maximize food intake rate. The tendency to choose overly-long patch residence times was stronger in male rats. We also observed sex differences in locomotion as rats performed the task, but these differences in movement only partially accounted for the differences in patch residence durations observed between male and female rats. Together, these results suggest a nuanced relationship between movement, sex, and foraging decisions.

Lost in time: Relocating the perception of duration outside the brain

It is well-accepted in neuroscience that animals process time internally to estimate the duration of intervals lasting between one and several seconds. More than 100 years ago, Henri Bergson nevertheless remarked that, because animals have memory, their inner experience of time is ever-changing, making duration impossible to measure internally and time a source of change. Bergson proposed that quantifying the inner experience of time requires its externalization in movements (observed or self-generated), as their unfolding leaves measurable traces in space. Here, studies across species are reviewed and collectively suggest that, in line with Bergson's ideas, animals spontaneously solve time estimation tasks through a movement-based spatialization of time. Moreover, the well-known scalable anticipatory responses of animals to regularly spaced rewards can be explained by the variable pressure of time on reward-oriented actions. Finally, the brain regions linked with time perception overlap with those implicated in motor control, spatial navigation and motivation. Thus, instead of considering time as static information processed by the brain, it might be fruitful to conceptualize it as a kind of force to which animals are more or less sensitive depending on their internal state and environment.

Hierarchical action encoding in prefrontal cortex of freely moving macaques

Our natural behavioral repertoires include coordinated actions of characteristic types. To better understand how neural activity relates to the expression of actions and action switches, we studied macaques performing a freely moving foraging task in an open environment. We developed a novel analysis pipeline that can identify meaningful units of behavior, corresponding to recognizable actions such as sitting, walking, jumping, and climbing. On the basis of transition probabilities between these actions, we found that behavior is organized in a modular and hierarchical fashion. We found that, after regressing out many potential confounders, actions are associated with specific patterns of firing in each of six prefrontal brain regions and that, overall, encoding of action category is progressively stronger in more dorsal and more caudal prefrontal regions. Together, these results establish a link between selection of units of primate behavior on one hand and neuronal activity in prefrontal regions on the other.

Beyond Nature Versus Nurture: the Emergence of Emotion

Affective science is stuck in a version of the nature-versus-nurture debate, with theorists arguing whether emotions are evolved adaptations or psychological constructions. We do not see these as mutually exclusive options. Many adaptive behaviors that humans have evolved to be good at, such as walking, emerge during development - not according to a genetically dictated program, but through interactions between the affordances of the body, brain, and environment. We suggest emotions are the same. As developing humans acquire increasingly complex goals and learn optimal strategies for pursuing those goals, they are inevitably pulled to particular brain-body-behavior states that maximize outcomes and self-reinforce via positive feedback loops. We call these recurring, self-organized states emotions. Emotions display many of the hallmark features of self-organized attractor states, such as hysteresis (prior events influence the current state), degeneracy (many configurations of the underlying variables can produce the same global state), and stability. Because most bodily, neural, and environmental affordances are shared by all humans - we all have cardiovascular systems, cerebral cortices, and caregivers who raised us - similar emotion states emerge in all of us. This perspective helps reconcile ideas that, at first glance, seem contradictory, such as emotion universality and neural degeneracy.

Beyond simple laboratory studies: Developing sophisticated models to study rich behavior

Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage.

Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion

The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.

On the Proper Treatment of Dynamics in Cognitive Science

This essay examines the relevance of dynamical ideas for cognitive science. On its own, the mere mathematical idea of a dynamical system is too weak to serve as a scientific theory of anything, and dynamical approaches within cognitive science are too rich and varied to be subsumed under a single "dynamical hypothesis." Instead, after first attempting to dissect the different notions of "dynamics" and "cognition" at play, a more specific theoretical framework for cognitive science broadly construed is sketched. This framework draws upon not only dynamical ideas, but also such contemporaneous perspectives as situatedness, embodiment, ecological psychology, enaction, neuroethology/neuroscience, artificial life, and biogenic approaches. The paper ends with some methodological suggestions for pursuing this theoretical framework.

Cognition from the Body-Brain Partnership: Exaptation of Memory

Examination of cognition has historically been approached from language and introspection. However, human language-dependent definitions ignore the evolutionary roots of brain mechanisms and constrain their study in experimental animals. We promote an alternative view, namely that cognition, including memory, can be explained by exaptation and expansion of the circuits and algorithms serving bodily functions. Regulation and protection of metabolic and energetic processes require time-evolving brain computations enabling the organism to prepare for altered future states. Exaptation of such circuits was likely exploited for exploration of the organism's niche. We illustrate that exploration gives rise to a cognitive map, and in turn, environment-disengaged computation allows for mental travel into the past (memory) and the future (planning). Such brain-body interactions not only occur during waking but also persist during sleep. These exaptation steps are illustrated by the dual, endocrine-homeostatic and memory, contributions of the hippocampal system, particularly during hippocampal sharp-wave ripples.

Tasks and their role in visual neuroscience

Vision is widely used as a model system to gain insights into how sensory inputs are processed and interpreted by the brain. Historically, careful quantification and control of visual stimuli have served as the backbone of visual neuroscience. There has been less emphasis, however, on how an observer's task influences the processing of sensory inputs. Motivated by diverse observations of task-dependent activity in the visual system, we propose a framework for thinking about tasks, their role in sensory processing, and how we might formally incorporate tasks into our models of vision.

Arc regulates a second-guessing cognitive bias during naturalistic foraging through effects on discrete behavior modules

Foraging in animals relies on innate decision-making heuristics that can result in suboptimal cognitive biases in some contexts. The mechanisms underlying these biases are not well understood, but likely involve strong genetic effects. To explore this, we studied fasted mice using a naturalistic foraging paradigm and discovered an innate cognitive bias called "second-guessing." This involves repeatedly investigating an empty former food patch instead of consuming available food, which hinders the mice from maximizing feeding benefits. The synaptic plasticity gene Arc is revealed to play a role in this bias, as Arc-deficient mice did not exhibit second-guessing and consumed more food. In addition, unsupervised machine learning decompositions of foraging identified specific behavior sequences, or "modules", that are affected by Arc. These findings highlight the genetic basis of cognitive biases in decision making, show links between behavior modules and cognitive bias, and provide insight into the ethological roles of Arc in naturalistic foraging.

Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development

Mapping the eco-evolutionary factors shaping the development of animals' behavioural phenotypes remains a great challenge. Recent advances in 'big behavioural data' research-the high-resolution tracking of individuals and the harnessing of that data with powerful analytical tools-have vastly improved our ability to measure and model developing behavioural phenotypes. Applied to the study of behavioural ontogeny, the unfolding of whole behavioural repertoires can be mapped in unprecedented detail with relative ease. This overcomes long-standing experimental bottlenecks and heralds a surge of studies that more finely define and explore behavioural-experiential trajectories across development. In this review, we first provide a brief guide to state-of-the-art approaches that allow the collection and analysis of high-resolution behavioural data across development. We then outline how such approaches can be used to address key issues regarding the ecological and evolutionary factors shaping behavioural development: developmental feedbacks between behaviour and underlying states, early life effects and behavioural transitions, and information integration across development.

Neurodynamical Computing at the Information Boundaries of Intelligent Systems

Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies-properly conceived as reentrant dynamical flows and not merely as identified groups of neurons-may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience.

Sounding the Alarm: Sex Differences in Rat Ultrasonic Vocalizations during Pavlovian Fear Conditioning and Extinction

Pavlovian fear conditioning is a prevalent tool in the study of aversive learning, which is a key component of stress-related psychiatric disorders. Adult rats can exhibit various threat-related behaviors, including freezing, motor responses, and ultrasonic vocalizations (USVs). While these responses can all signal aversion, we know little about how they relate to one another. Here we characterize USVs emitted by male and female rats during cued fear acquisition and extinction, and assess the relationship between different threat-related behaviors. We found that males consistently emitted >22 kHz calls (referred to here as "alarm calls") than females, and that alarm call frequency in males, but not females, related to the intensity of the shock stimulus. Interestingly, 25% of males and 45% of females did not emit any alarm calls at all. Males that did make alarm calls had significantly higher levels of freezing than males who did not, while no differences in freezing were observed between female Alarm callers and Non-alarm callers. Alarm call emission was also affected by the predictability of the shock; when unpaired from a tone cue, both males and females started emitting alarm calls significantly later. During extinction learning and retrieval sessions, males were again more likely than females to emit alarm calls, which followed an extinction-like reduction in frequency. Collectively these data suggest sex dependence in how behavioral readouts relate to innate and conditioned threat responses. Importantly, we suggest that the same behaviors can signal sex-dependent features of aversion.

Integrated neural dynamics of sensorimotor decisions and actions

Recent theoretical models suggest that deciding about actions and executing them are not implemented by completely distinct neural mechanisms but are instead two modes of an integrated dynamical system. Here, we investigate this proposal by examining how neural activity unfolds during a dynamic decision-making task within the high-dimensional space defined by the activity of cells in monkey dorsal premotor (PMd), primary motor (M1), and dorsolateral prefrontal cortex (dlPFC) as well as the external and internal segments of the globus pallidus (GPe, GPi). Dimensionality reduction shows that the four strongest components of neural activity are functionally interpretable, reflecting a state transition between deliberation and commitment, the transformation of sensory evidence into a choice, and the baseline and slope of the rising urgency to decide. Analysis of the contribution of each population to these components shows meaningful differences between regions but no distinct clusters within each region, consistent with an integrated dynamical system. During deliberation, cortical activity unfolds on a two-dimensional "decision manifold" defined by sensory evidence and urgency and falls off this manifold at the moment of commitment into a choice-dependent trajectory leading to movement initiation. The structure of the manifold varies between regions: In PMd, it is curved; in M1, it is nearly perfectly flat; and in dlPFC, it is almost entirely confined to the sensory evidence dimension. In contrast, pallidal activity during deliberation is primarily defined by urgency. We suggest that these findings reveal the distinct functional contributions of different brain regions to an integrated dynamical system governing action selection and execution.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

A neuromechanical model for Drosophila larval crawling based on physical measurements

BACKGROUND

Animal locomotion requires dynamic interactions between neural circuits, the body (typically muscles), and surrounding environments. While the neural circuitry of movement has been intensively studied, how these outputs are integrated with body mechanics (neuromechanics) is less clear, in part due to the lack of understanding of the biomechanical properties of animal bodies. Here, we propose an integrated neuromechanical model of movement based on physical measurements by taking Drosophila larvae as a model of soft-bodied animals.

RESULTS

We first characterized the kinematics of forward crawling in Drosophila larvae at a segmental and whole-body level. We then characterized the biomechanical parameters of fly larvae, namely the contraction forces generated by neural activity, and passive elastic and viscosity of the larval body using a stress-relaxation test. We established a mathematical neuromechanical model based on the physical measurements described above, obtaining seven kinematic values characterizing crawling locomotion. By optimizing the parameters in the neural circuit, our neuromechanical model succeeded in quantitatively reproducing the kinematics of larval locomotion that were obtained experimentally. This model could reproduce the observation of optogenetic studies reported previously. The model predicted that peristaltic locomotion could be exhibited in a low-friction condition. Analysis of floating larvae provided results consistent with this prediction. Furthermore, the model predicted a significant contribution of intersegmental connections in the central nervous system, which contrasts with a previous study. This hypothesis allowed us to make a testable prediction for the variability in intersegmental connection in sister species of the genus Drosophila.

CONCLUSIONS

We generated a neurochemical model based on physical measurement to provide a new foundation to study locomotion in soft-bodied animals and soft robot engineering.

A mechanism for punctuating equilibria during mammalian vocal development

Evolution and development are typically characterized as the outcomes of gradual changes, but sometimes (states of equilibrium can be punctuated by sudden change. Here, we studied the early vocal development of three different mammals: common marmoset monkeys, Egyptian fruit bats, and humans. Consistent with the notion of punctuated equilibria, we found that all three species undergo at least one sudden transition in the acoustics of their developing vocalizations. To understand the mechanism, we modeled different developmental landscapes. We found that the transition was best described as a shift in the balance of two vocalization landscapes. We show that the natural dynamics of these two landscapes are consistent with the dynamics of energy expenditure and information transmission. By using them as constraints for each species, we predicted the differences in transition timing from immature to mature vocalizations. Using marmoset monkeys, we were able to manipulate both infant energy expenditure (vocalizing in an environment with lighter air) and information transmission (closed-loop contingent parental vocal playback). These experiments support the importance of energy and information in leading to punctuated equilibrium states of vocal development.

Species can sometimes evolve suddenly; their appearance is preceded and followed by long periods of stability. This process is known as “punctuated equilibrium”. Our data show that for three mammalian species—marmoset monkeys, fruit bats, and humans—early vocal development trajectories can also be characterized as different equilibrium states punctuated by sharp transitions; transitions indicate the advent of a new vocal behavior. To better understand the putative mechanism behind such transitions, we show that a balance model, in which variables trade-off in their importance over time, captured this change by accurately simulating the shape of the developmental trajectory and predicting the timing of the transition between immature and mature vocal states for all three species. Two variables—energy and information—were hypothesized to trade-off during development. We tested and found support for this hypothesis in analyses of two marmoset monkey experiments, one which manipulated energy metabolic costs and another which manipulated information transmission.

Natural behavior is the language of the brain

The breadth and complexity of natural behaviors inspires awe. Understanding how our perceptions, actions, and internal thoughts arise from evolved circuits in the brain has motivated neuroscientists for generations. Researchers have traditionally approached this question by focusing on stereotyped behaviors, either natural or trained, in a limited number of model species. This approach has allowed for the isolation and systematic study of specific brain operations, which has greatly advanced our understanding of the circuits involved. At the same time, the emphasis on experimental reductionism has left most aspects of the natural behaviors that have shaped the evolution of the brain largely unexplored. However, emerging technologies and analytical tools make it possible to comprehensively link natural behaviors to neural activity across a broad range of ethological contexts and timescales, heralding new modes of neuroscience focused on natural behaviors. Here we describe a three-part roadmap that aims to leverage the wealth of behaviors in their naturally occurring distributions, linking their variance with that of underlying neural processes to understand how the brain is able to successfully navigate the everyday challenges of animals' social and ecological landscapes. To achieve this aim, experimenters must harness one challenge faced by all neurobiological systems, namely variability, in order to gain new insights into the language of the brain.

An Active Sensing Paradigm for Studying Human Auditory Perception

Our perception is based on active sensing, i.e., the relationship between self-motion and resulting changes to sensory inputs. Yet, traditional experimental paradigms are characterized by delayed reactions to a predetermined stimulus sequence. To increase the engagement of subjects and potentially provide richer behavioral responses, we developed Sensory Island Task for humans (SITh), a freely-moving search paradigm to study auditory perception. In SITh, subjects navigate an arena in search of an auditory target, relying solely on changes in the presented stimulus frequency, which is controlled by closed-loop position tracking. A “target frequency” was played when subjects entered a circular sub-area of the arena, the “island”, while different frequencies were presented outside the island. Island locations were randomized across trials, making stimulus frequency the only informative cue for task completion. Two versions of SITh were studied: binary discrimination, and gradual change of the stimulus frequency. The latter version allowed determining frequency discrimination thresholds based on the subjects’ report of the perceived island location (i.e., target frequency). Surprisingly, subjects exhibited similar thresholds as reported in traditional “stationary” forced-choice experiments after performing only 30 trials, highlighting the intuitive nature of SITh. Notably, subjects spontaneously employed a small variety of stereotypical search patterns, and their usage proportions varied between task versions. Moreover, frequency discrimination performance depended on the search pattern used. Overall, we demonstrate that the use of an ecologically driven paradigm is able to reproduce established findings while simultaneously providing rich behavioral data for the description of sensory ethology.

Low-Power Lossless Data Compression for Wireless Brain Electrophysiology

Wireless electrophysiology opens important possibilities for neuroscience, especially for recording brain activity in more natural contexts, where exploration and interaction are not restricted by the usual tethered devices. The limiting factor is transmission power and, by extension, battery life required for acquiring large amounts of neural electrophysiological data. We present a digital compression algorithm capable of reducing electrophysiological data to less than 65.5% of its original size without distorting the signals, which we tested in vivo in experimental animals. The algorithm is based on a combination of delta compression and Huffman codes with optimizations for neural signals, which allow it to run in small, low-power Field-Programmable Gate Arrays (FPGAs), requiring few hardware resources. With this algorithm, a hardware prototype was created for wireless data transmission using commercially available devices. The power required by the algorithm itself was less than 3 mW, negligible compared to the power saved by reducing the transmission bandwidth requirements. The compression algorithm and its implementation were designed to be device-agnostic. These developments can be used to create a variety of wired and wireless neural electrophysiology acquisition systems with low power and space requirements without the need for complex or expensive specialized hardware.

Information normally considered task-irrelevant drives decision-making and affects premotor circuit recruitment

Decision-making is a continuous and dynamic process with prior experience reflected in and used by the brain to guide adaptive behavior. However, most neurobiological studies constrain behavior and/or analyses to task-related variables, not accounting for the continuous internal and temporal space in which they occur. We show mice rely on information learned through recent and longer-term experience beyond just prior actions and reward - including checking behavior and the passage of time - to guide self-initiated, self-paced, and self-generated actions. These experiences are represented in secondary motor cortex (M2) activity and its projections into dorsal medial striatum (DMS). M2 integrates this information to bias strategy-level decision-making, and DMS projections reflect specific aspects of this recent experience to guide actions. This suggests diverse aspects of experience drive decision-making and its neural representation, and shows premotor corticostriatal circuits are crucial for using selective aspects of experiential information to guide adaptive behavior.

Show your work: Tools for open developmental science

Since grade school, students of many subjects have learned to "show their work" in order to receive full credit for assignments. Many of the reasons for students to show their work extend to the conduct of scientific research. And yet multiple barriers make it challenging to share and show the products of scientific work beyond published findings. This chapter discusses some of these barriers and how web-based data repositories help overcome them. The focus is on Databrary.org, a data library specialized for storing and sharing video data with a restricted community of institutionally approved investigators. Databrary was designed by and for developmental researchers, and so its features and policies reflect many of the specific challenges faced by this community, especially those associated with sharing video and related identifiable data. The chapter argues that developmental science poses some of the most interesting, challenging, and important questions in all of science, and that by openly sharing much more of the products and processes of our work, developmental scientists can accelerate discovery while making our scholarship much more robust and reproducible.

Noncanonical genomic imprinting in the monoamine system determines naturalistic foraging and brain-adrenal axis functions

Noncanonical genomic imprinting can cause biased expression of one parental allele in a tissue; however, the functional relevance of such biases is unclear. To investigate ethological roles for noncanonical imprinting in dopa decarboxylase (Ddc) and tyrosine hydroxylase (Th), we use machine learning to decompose naturalistic foraging in maternal and paternal allele mutant heterozygous mice. We uncover distinct roles for the maternal versus paternal alleles on foraging, where maternal alleles affect sons while daughters are under paternal allelic control. Each parental allele controls specific action sequences reflecting decisions in naive or familiar contexts. The maternal Ddc allele is preferentially expressed in subsets of hypothalamic GABAergic neurons, while the paternal allele predominates in subsets of adrenal cells. Each Ddc allele affects distinct molecular and endocrine components of the brain-adrenal axis. Thus, monoaminergic noncanonical imprinting has ethological roles in foraging and endocrine functions and operates by affecting discrete subsets of cells.