Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.06.23.733896

Self-Supervised Behavioral Representations Across the Life Course: A Killifish Case Study

Chang, J.-C.·Komatsu, T. S.·Onami, S.
View original source ↗Export metadata ↓

Abstract

Self-supervised foundation models of aging are increasingly built from longitudinal data (biobanks, electronic health records, wearables) that is inherently incomplete: no individual is followed across a whole lifetime, and how much of each life is captured varies widely. This raises two linked questions: is it worth modeling an individuals whole life course rather than its current state, and can such a model be built from brief, fragmentary records? No human cohort can settle them, because none offers a complete life to compare against. We turn to the African turquoise killifish (Nothobranchius furzeri), tracked from youth to natural death in publicly released recordings, as a controlled testbed: its complete lifespans provide the full-life reference that human data lacks. On these data we build LifeMAE, a two-stage self-supervised model: a day encoder that summarizes each day of behavior, then a life-course encoder over the trajectory of those daily summaries. We find that the day encoder alone is already strong: from a single day of behavior it predicts chronological age, separates long-from short-lived individuals (coarsely), and flags nearness to death. Adding the life-course encoder improves on none of the three; each is matched by trivially aggregating the day-level predictions (a smoother for age, an early-life average for lifespan). Near-term mortality seems the exception, where the whole-life model looks far better (AUROC 0.81 to 0.91), but the gain is not behavioral: it reflects where each day falls within the observation window (a cue supplied by the models encoding of time), and a single-day model given that cue closes the gap at any observation length. For these traits, an individuals place in its life course is legible from a single day: the trajectory stage is unnecessary, and the record it needs is as short as one day, the finest grain our day-level setup resolves. For characterizing a cohort, this favors observing many individuals briefly over tracking a few for long. The result joins a growing body of work in which deep and foundation models, fairly benchmarked, fail to beat deliberately simple baselines. We add a concrete mechanism for the over-optimism: a models encoding of time can leak the very quantity it predicts, which backward-looking evaluation mistakes for learned biology, so only evaluation fixed to the moment of prediction is trustworthy.

Explore related subjects

animal behavior and cognitionPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chang, J.-C., Komatsu, T. S., Onami, S.. 2026-06-29. Self-Supervised Behavioral Representations Across the Life Course: A Killifish Case Study. https://doi.org/10.64898/2026.06.23.733896

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

roostR: An R package to examine diel activity patterns from Motus radio telemetry data

1. Monitoring the diel activity patterns of free-living animals is a methodological challenge. Signal strength fluctuations from Very High Frequency (VHF) radio transmitters deployed within the Motus Wildlife Tracking System can be used as a proxy for activity. However, analytical tools to extract behavioral metrics that quantify activity patterns from these data are needed. 2. We developed roostR, an open-source R package that converts Motus detection data into quantitative behavioral metrics, including roost initiation and departure, roost duration, observation time, and restlessness. The package uses a sequential pipeline built around signal volatility and rolling medians to detect transitions between active and inactive states. Default parameter values were tuned using data from 55 dark-eyed juncos (Junco hyemalis) overwintering in southeastern Ohio. 3. We provide an example from a dark-eyed junco over a 58-day period. roostR estimated roost onset on 48 nights and departure on 56 mornings, with higher rolling median signal differences during the day than at night, consistent with a diurnal animal, and variable restlessness periods each night. We also used the package to estimate roost behavior of an American tree sparrow (Spizelloides arborea) over 43-nights. 4. roostR enables researchers to extract individual activity data from Motus datasets. Because all thresholds are user-adjustable, the pipeline is adaptable across species, tag specifications, and ecological contexts, enabling researchers to test hypotheses about how environmental factors influence diel activity patterns.

animal behavior and cognition↗
bioRxiv2026

Characterizing rhythmic wheel-turning behavioral patterns in cockroaches Rhyparobia maderae using machine learning

Organisms must adapt to environmental changes occurring across multiple time scales, with endogenous multiscale clocks coordinating physiology and behavior with recurring environmental rhythms, including the dominant 24-hour cycle and faster ultradian rhythms. The Madeira cockroach (Rhyparobia maderae) provides a suitable model for investigating such multiscale temporal organization. Here, locomotor activity was recorded in running-wheel experiments under constant darkness. While the endogenous circadian clock produces a clearly visible 24-hour rhythm, it remains unknown whether locomotor behavior also exhibits temporal patterns at additional time scales. These temporal patterns cannot be found by classical frequency analysis, as they are veiled by higher harmonics of the circadian rhythm which are in the same frequency range. Unsupervised machine learning methods such as K-Means clustering, self-organizing maps and Gaussian mixture are used in search for fast ultradian rhythms possibly linked to circadian cycles in locomotor activity. Prior to applying these methods, data metrics are defined which characterize bouts of activity (called activity impulses) compared to periods of reduced activity. A stochastic pattern was found in these activity metrics which characterizes the time distance between activity impulses. Across all approaches, a consistent ultradian rhythm of approximately one hour was identified in the timing of the activity maxima. This rhythm was mainly detected during the subjective night, suggesting circadian control, and appears to consist of two components with periods of approximately 40 minutes and 1.5 hours. The method proposed in this paper is applied to two cockroach groups with different levels of activity, and is generalizable to diverse datasets occurring in the form of a time series with a dominant rhythm.

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↗