The what, how, and why of naturalistic behavior

Pile driving sound induces short-term behavioral changes in black sea bass (Centropristis striata): A field studya)

Offshore wind represents a renewable energy alternative as countries seek to limit the climate-altering effects of fossil fuels. The global investment in wind energy has generated substantial concern, as turbine construction emits high-intensity sounds and the associated impacts on marine fauna remain largely unknown. This in situ experimental study quantified behavioral changes of commercially important black sea bass (Centropristis striata) to nearshore pile driving using video observation. Behavior of caged black sea bass was characterized at near and far sites before and during repeated bouts of two contrasting pile driving techniques: impact and vibratory hammering (VH), over two consecutive days. At impact hammer onset, animals at both sites reduced swimming in midwater, switching to bottom-associated freezing behaviors consistent with heightened alertness. Yet they returned to pre-exposure states during VH and later impact hammer sequences, even at close sites with the highest peak-to-peak sound pressure levels (203-213 dB re 1μPa). Behavioral changes were more persistent at the near site, but neither distance elicited significant behavioral changes on the second day of exposure. The results indicate that black sea bass behavior may be initially altered by impact hammer sound, but these effects are short-lived and do not continue through multiple exposures or consecutive days.

InteBOMB: Integrating generic object tracking and segmentation with pose estimation for animal behavior analysis

Advancements in animal behavior quantification methods have driven the development of computational ethology, enabling fully automated behavior analysis. Existing multi-animal pose estimation workflows rely on tracking-by-detection frameworks for either bottom-up or top-down approaches, requiring retraining to accommodate diverse animal appearances. This study introduces InteBOMB, an integrated workflow that enhances top-down approaches by incorporating generic object tracking, eliminating the need for prior knowledge of target animals while maintaining broad generalizability. InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings. The "background enhancement" strategy optimizes foreground-background contrastive loss, generating more discriminative correlation maps. The "online proofreading" strategy stores human-in-the-loop long-term memory and dynamic short-term memory, enabling adaptive updates to object visual features. The "automated labeling suggestion" technique reuses the visual features saved during tracking to identify representative frames for training set labeling. Additionally, the "joint behavior analysis" technique integrates these features with multimodal data, expanding the latent space for behavior classification and clustering. To evaluate the framework, six datasets of mice and six datasets of non-human primates were compiled, covering laboratory and natural scenes. Benchmarking results demonstrated a 24% improvement in zero-shot generic tracking and a 21% enhancement in joint latent space performance across datasets, highlighting the effectiveness of this approach in robust, generalizable behavior analysis.

Hierarchical behavioral analysis framework as a platform for standardized quantitative identification of behaviors

Behavior is composed of modules that operate based on inherent logic. Understanding behavior and its neural mechanisms is facilitated by clear structural behavioral analysis. Here, we developed a hierarchical behavioral analysis framework (HBAF) that efficiently reveals the organizational logic of these modules by analyzing high-dimensional behavioral data. By creating a spontaneous behavior atlas for male and female mice, we discovered that spontaneous behavior patterns are hardwired, with sniffing serving as the hub node for movement transitions. The sniffing-to-grooming ratio accurately distinguished the spontaneous behavioral states in a high-throughput manner. These states are influenced by emotional status, circadian rhythms, and lighting conditions, leading to unique behavioral characteristics, spatiotemporal features, and dynamic patterns. By implementing the straightforward and achievable spontaneous behavior paradigm, HBAF enables swift and accurate assessment of animal behavioral states and bridges the gap between a theoretical understanding of the behavioral structure and practical analysis using comprehensive multidimensional behavioral information.

The neurobiology of parenting and infant-evoked aggression

Parenting behavior comprises a variety of adult-infant and adult-adult interactions across multiple timescales. The state transition from nonparent to parent requires an extensive reorganization of individual priorities and physiology and is facilitated by combinatorial hormone action on specific cell types that are integrated throughout interconnected and brainwide neuronal circuits. In this review, we take a comprehensive approach to integrate historical and current literature on each of these topics across multiple species, with a focus on rodents. New and emerging molecular, circuit-based, and computational technologies have recently been used to address outstanding gaps in our current framework of knowledge on infant-directed behavior. This work is raising fundamental questions about the interplay between instinctive and learned components of parenting and the mutual regulation of affiliative versus agonistic infant-directed behaviors in health and disease. Whenever possible, we point to how these technologies have helped gain novel insights and opened new avenues of research into the neurobiology of parenting. We hope this review will serve as an introduction for those new to the field, a comprehensive resource for those already studying parenting, and a guidepost for designing future studies.

SAUSI: an integrative assay for measuring social aversion and motivation

Social aversion is a key feature of numerous mental health disorders such as Social Anxiety and Autism Spectrum Disorders. Nevertheless, the biobehavioral mechanisms underlying social aversion remain poorly understood. Progress in understanding the etiology of social aversion has been hindered by the lack of comprehensive tools to assess social aversion in model systems. Here, we created a new behavioral task - Selective Access to Unrestricted Social Interaction (SAUSI), which integrates elements of social motivation, hesitancy, decision-making, and free interaction to enable the wholistic assessment of social aversion in mice. Using this novel assay, we found that social isolation-induced social aversion in mice is largely driven by increases in social fear and social motivation. Deep learning analyses revealed a unique behavioral footprint underlying the socially aversive state produced by isolation, demonstrating the compatibility of modern computational approaches with SAUSI. Social aversion was further assessed using traditional assays - including the 3-chamber sociability assay and the resident intruder assay - which were sufficient to reveal fragments of a social aversion phenotype, including changes to either social motivation or social interaction, but which failed to provide a wholistic assessment of social aversion. Critically, these assays were not sufficient to reveal key components of social aversion, including social freezing and social hesitancy behaviors. Lastly, we demonstrated that SAUSI is generalizable, as it can be used to assess social aversion induced by non-social stressors, such as foot shock. Our findings debut a novel task for the behavioral toolbox - one which overcomes limitations of previous assays, allowing for both social choice as well as free interaction, and offers a new approach for assessing social aversion in rodents.

Characterizing the structure of mouse behavior using Motion Sequencing

Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called 'syllables'. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior.

Unsupervised decomposition of natural monkey behavior into a sequence of motion motifs

Nonhuman primates (NHPs) exhibit complex and diverse behavior that typifies advanced cognitive function and social communication, but quantitative and systematical measure of this natural nonverbal processing has been a technical challenge. Specifically, a method is required to automatically segment time series of behavior into elemental motion motifs, much like finding meaningful words in character strings. Here, we propose a solution called SyntacticMotionParser (SMP), a general-purpose unsupervised behavior parsing algorithm using a nonparametric Bayesian model. Using three-dimensional posture-tracking data from NHPs, SMP automatically outputs an optimized sequence of latent motion motifs classified into the most likely number of states. When applied to behavioral datasets from common marmosets and rhesus monkeys, SMP outperformed conventional posture-clustering models and detected a set of behavioral ethograms from publicly available data. SMP also quantified and visualized the behavioral effects of chemogenetic neural manipulations. SMP thus has the potential to dramatically improve our understanding of natural NHP behavior in a variety of contexts.

From innate to instructed: A new look at perceptual decision-making

Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.

Exploring transgenerational inheritance in epigenotypes of DAT heterozygous rats: Circadian anomalies and attentional vulnerability

Dopamine (DA) is mainly involved in locomotor activity, reward processes and maternal behaviors. Rats with KO gene for dopamine transporter (DAT), coding for a truncated DAT protein, are in hyperdopaminergic conditions and thus develop stereotyped behaviors and hyperactivity. Our aim was to test the prior transgenerational modulation of wild and truncated alleles as expressed in heterozygous DAT rats: specifically, we addressed the possible sequelae due to genotype and gender of the ancestors, with regard to behavioral differences in F1, F2, F3 rats. We studied non-classical DAT heterozygotes (HETs) based on two specular lines, with putative grand-maternal vs. grand-paternal imprinting. MAT females (F1; offspring of KO male and WT female) mated with a KO male to generate MIX offspring (F2). Specularly, PAT females (F1; offspring of KO female and WT male) mated with a KO male to generate PIX offspring (F2). Similarly to PAT, we obtained MUX (F2; HET offspring of MAT sire and KO dam); we also observed the F3 (MYX: HET offspring of KO male and MUX female, thus with DAT-KO maternal grandmother like also for PIX). We studied their circadian cycle of locomotor activity and their behavior in the elevated-plus-maze (EPM). Locomotor hyper-activity occurs in F1, the opposite occurs in F2, with MYX rats appearing undistinguishable from WT ones. Open-arm preference emerged in PIX and MIX rats. Only MAT and MYX rats showed a significant vulnerability for ADHD-like inattentive symptoms (duration of rearing in the EPM; Viggiano et al., 2002). A risk-taking profile is evident in the F2 phenotype, while inattentiveness from F1 progeny tends to be transferred to F3. We hypothesize that DAT-related phenotypes result from effective inheritance through pedigree of imprints that are dependent on grandparents, suggesting a protective role for gestation within a hyperdopaminergic uterus. For major features, similar odd (F1, F3) generations appear opposed to even (F2) ones; for minor specific features, the phenotype transfer may affect the progenies with a male but not a female DAT-KO ancestor.

Dopamine across timescales and cell types: Relevance for phenotypes in Parkinson's disease progression

Dopamine neurons in the substantia nigra pars compacta (SNc) synthesize and release dopamine, a critical neurotransmitter for movement and learning. SNc dopamine neurons degenerate in Parkinson's Disease (PD), causing a host of motor and non-motor symptoms. Here, we review recent conceptual advances in our basic understanding of the dopamine system - including our rapidly advancing knowledge of dopamine neuron heterogeneity - with special attention to their importance for understanding PD. In PD patients, dopamine neuron degeneration progresses from lateral SNc to medial SNc, suggesting clinically relevant heterogeneity in dopamine neurons. With technical advances in dopamine system interrogation, we can understand the relevance of this heterogeneity for PD progression and harness it to develop new treatments.

Sensory integration of danger and safety cues may explain the fear of a quiet coyote

Sensory integration theory predicts natural selection should favour adaptive responses of animals to multiple forms of information, yet empirical tests of this prediction are rare, particularly in free-living mammals. Studying indirect predator cues offers a salient opportunity to inquire about multimodal risk assessment and its potentially interactive effects on prey responses. Here we exposed California ground squirrels from two study sites (that differ in human and domestic dog activity) to acoustic and/or olfactory predator cues to reveal divergent patterns of signal dominance. Olfactory information most strongly predicted space use within the testing arena. That is, individuals, especially those at the human-impacted site, avoided coyote urine, a danger cue that may communicate the proximity of a coyote. By contrast, subjects allocated less time to risk-sensitive behaviours when exposed to acoustic cues. Specifically, although individuals were consistent in their behavioural responses across trials, 'quiet coyotes' (urine without calls) significantly increased the behavioural reactivity of prey, likely because coyotes rarely vocalize when hunting. More broadly, our findings highlight the need to consider the evolution of integrated fear responses and contribute to an emerging understanding of how animals integrate multiple forms of information to trade off between danger and safety cues in a changing world.

Endocannabinoid system and aggression across animal species

This narrative review article summarizes the current state of knowledge regarding the relationship between the endocannabinoid system (ECS) and aggression across multiple vertebrate species. Experimental evidence indicates that acute administration of phytocannabinoids, synthetic cannabinoids, and the pharmacological enhancement of endocannabinoid signaling decreases aggressive behavior in several animal models. However, research on the chronic effects of cannabinoids on animal aggression has yielded inconsistent findings, indicating a need for further investigation. Cannabinoid receptors, particularly cannabinoid receptor type 1, appear to be an important part of the endogenous mechanism involved in the dampening of aggressive behavior. Overall, this review underscores the importance of the ECS in regulating aggressive behavior and provides a foundation for future research in this area.

Drawing as a versatile cognitive tool

Drawing is a cognitive tool that makes the invisible contents of mental life visible. Humans use this tool to produce a remarkable variety of pictures, from realistic portraits to schematic diagrams. Despite this variety and the prevalence of drawn images, the psychological mechanisms that enable drawings to be so versatile have yet to be fully explored. In this Review, we synthesize contemporary work in multiple areas of psychology, computer science and neuroscience that examines the cognitive processes involved in drawing production and comprehension. This body of findings suggests that the balance of contributions from perception, memory and social inference during drawing production varies depending on the situation, resulting in some drawings that are more realistic and other drawings that are more abstract. We also consider the use of drawings as a research tool for investigating various aspects of cognition, as well as the role that drawing has in facilitating learning and communication. Taken together, information about how drawings are used in different contexts illuminates the central role of visually grounded abstractions in human thought and behaviour.

Seminatural environments for rodent behavioral testing: a representative design improving animal welfare and enhancing replicability

The low replicability of scientific studies has become an important issue. One possible cause is low representativeness of the experimental design employed. Already in the 1950's, Egon Brunswick pointed out that experimental setups ideally should be based on a random sample of stimuli from the subjects' natural environment or at least include basic features of that environment. Only experimental designs satisfying this criterion, representative designs in Brunswikian terminology, can produce results generalizable beyond the procedure used and to situations outside the laboratory. Such external validity is crucial in preclinical drug studies, for example, and should be important for replicability in general. Popular experimental setups in rodent research on non-human animals, like the tail suspension test or the Geller-Seifter procedure, do not correspond to contexts likely to be encountered in the animals' habitat. Consequently, results obtained in this kind of procedures can be generalized neither to other procedures nor to contexts outside the laboratory. Furthermore, many traditional procedures are incompatible with current notions of animal welfare. An approximation to the natural social and physical context can be provided in the laboratory, in the form of a seminatural environment. In addition to satisfy the basic demands for a representative design, such environments offer a far higher level of animal welfare than the typical small cages. This perspective article will briefly discuss the basic principles of the generalizability of experimental results, the virtues of representative designs and the coincidence of enhanced scientific quality and animal welfare provided by this kind of design.

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.

An approximate line attractor in the hypothalamus encodes an aggressive state

The hypothalamus regulates innate social behaviors, including mating and aggression. These behaviors can be evoked by optogenetic stimulation of specific neuronal subpopulations within MPOA and VMHvl, respectively. Here, we perform dynamical systems modeling of population neuronal activity in these nuclei during social behaviors. In VMHvl, unsupervised analysis identified a dominant dimension of neural activity with a large time constant (>50 s), generating an approximate line attractor in neural state space. Progression of the neural trajectory along this attractor was correlated with an escalation of agonistic behavior, suggesting that it may encode a scalable state of aggressiveness. Consistent with this, individual differences in the magnitude of the integration dimension time constant were strongly correlated with differences in aggressiveness. In contrast, approximate line attractors were not observed in MPOA during mating; instead, neurons with fast dynamics were tuned to specific actions. Thus, different hypothalamic nuclei employ distinct neural population codes to represent similar social behaviors.