Biology subjects

Villacorta-Atienza, J. A.

Publications and source records attributed to Villacorta-Atienza, J. A..

5 recordsLinked to original sources

bioRxiv2026

Dimension lifting in mental space for adaptive behavior in highly dynamic situations

Time compaction theory is a general framework explaining how a brain can efficiently deal with dynamic situations occurring in, e.g., sports games. It involves a geometric representation of the time dimension, which enables effective learning and strategic action planning. The theory has recently received experimental support in humans. However, its current computational model has an important limitation: it does not account for deliberate waiting and speed modulation, behaviors ubiquitous in natural environments. This work substantially extends the original model formulation by a dimensional lifting of an n-D workspace into (n + 1)-D mental space, where time remains geometrically embedded. The proposed biologically inspired computational model can generate adaptive behavior across increasingly complex situations, from navigation in everyday social environments to competitive sports. Furthermore, by actively conditioning the expected responses of other agents and stabilizing future predictions, we introduce the concept of uncertainty points in sequences of generalized cognitive maps to support the generation of adaptive strategies in interactive environments, where future prediction has a limited time horizon. Thus, we provide a mechanism for chaining short-term solutions into long-term strategies, which is illustrated by simulating the behavior of a player in a real football game. Author summaryHumans often anticipate future interactions in dynamic environments. Many behaviors, such as avoiding other pedestrians, letting someone pass through a narrow corridor, or reproducing the kind of dribbling maneuvers performed by elite football players, require deciding not only where to move but also when to move. Existing theories suggest that the brain simplifies such situations by representing future interactions as static spatial maps, making them easier to learn and recall. However, current computational models cannot naturally account for common behaviors such as waiting, slowing down, or modulating speed. Here we show that these behaviors readily emerge if the model space is extended by an additional virtual coordinate that encodes accumulated waiting rather than physical time. The proposed model simultaneously admits a wide variety of behaviors, including speed modulation, multigoal decisions, and compound actions, while preserving the principles of time compaction. We illustrate the model in everyday situations and by reproducing two real football plays, comparing the observed behaviors with model simulations. Our results suggest computational principles through which the human brain may efficiently represent, memorize, and exploit dynamic situations.

biophysics↗

bioRxiv2026

Event-Centered Prediction: How Future Interaction Points Shape Human Anticipation of Motion

Prediction in dynamic situations, in which relevant elements evolve over time, is a fundamental cognitive function. The brain relies on specialized predictive mechanisms, including time compaction, a process that supports dynamic processing by embedding temporal information into space and transforming future interactions into salient spatial representations. Here we investigated how future interactions are salient during dynamic events and how this salience shapes behavior. Participants performed a visuomotor prediction task in which they estimated the future trajectory of a moving object after observing only the initial portion of its motion, while another object was simultaneously present and could generate either interactive (collision) or non-interactive (crossing) dynamics. Although accurate performance required extrapolating motion solely from kinematic information, participants predictions were systematically biased toward locations associated with future interactions. Prediction accuracy was reduced in situations involving potential future interactions compared to non-interactive dynamics. Importantly, participants consistently responded closer to predicted interaction points, even when this strategy did not improve accuracy or trajectory extrapolation. Substantial inter-individual variability was observed, revealing conservative and risk-taking predictive strategies with systematic group differences. When participants were explicitly instructed to improve performance, overall accuracy improved only marginally, while predictive behavior shifted toward greater reliance on interaction-related locations, particularly among those who had not already adopted this strategy. We propose that this interaction-driven bias reflects a core property of time compaction, supporting the idea that predictive cognition relies on future interactions as stable reference points under dynamic uncertainty.

neuroscience↗

bioRxiv2025

Efficient memorization of dynamic stimuli with future interactions

In nature, survival requires coping with complex time-changing situations in real time. In this process, memory plays a major role since the retrieval of critical information is key to rapid and reliable decision making. This work explores modulation of human memory under the hypothesis that in dynamic scenarios such critical information is encoded as a static map of future interactions. Specifically, the reported results show that dynamic visual stimuli that contain future interactions are better recalled than equivalent stimuli that do not. This is in line with the proposed hypothesis since the former type of stimulus would be encoded in a more simplified way than the latter. Moreover, dynamic stimuli with future interactions are better recalled than simpler dynamic stimuli, which reinforces that the former are processed by a static representation - their map of interactions. This cognitive strategy seems to be modulated by the complexity of the stimulus, since in simple situations differences in recall appear only in men, whereas when complexity increases, such differences do not show gender bias. Therefore, this work proposes an answer to how memory can help us reliably cope with dynamic situations, demonstrating that those critical for survival (such as fighting, chasing, fleeing, etc., which involve interactions) are better remembered, allowing more efficient learning and decision making, essential to deal with our complex and changing world.

animal behavior and cognition↗

bioRxiv2023

Internal representation of future interactions in rats

Animals and humans receive the most critical information from parts of the environment that are immediately inaccessible, possibly only visually explored, and highly dynamic. The brain must effectively process potential interactions between elements in such an environment to make appropriate decisions in critical situations. We trained male Long-Evans rats to discriminate static and dynamic visuospatial stimuli displayed on an inaccessible computer screen. We demonstrated that the rats learned to discriminate dynamic visuospatial stimuli faster, with greater accuracy and shorter reaction time than complementary static stimuli. Furthermore, we provide behavioral evidence indicating that rats internally represent dynamic environments as static maps that capture meaningful future interactions. These observations highlight the ecological importance of dynamic stimuli in the outside world and support previous findings in humans that internal static representations can encapsulate relevant spatiotemporal information of dynamic environments. Such a mechanism would allow animals and humans to process complex time-changing situations neatly.

animal behavior and cognition↗

bioRxiv2021

Benchmarking of tools for axon length measurement in individually-labeled projection neurons

Projection neurons are the commonest neuronal type in the mammalian forebrain and their individual characterization is a crucial step to understand how neural circuitry operates. These cells have an axon whose arborizations extend over long distances, branching in complex patterns and/or in multiple brain regions. Axon length is a principal estimate of the functional impact of the neuron, as it directly correlates with the number of synapses formed by the axon in its target regions; however, its measurement by direct 3D axonal tracing is a slow and labor-intensive method. On the contrary, axon length estimations have been recently proposed as an effective and accessible alternative, allowing a fast approach to the functional significance of the single neuron. Here, we analyze the accuracy and efficiency of the most used length estimation tools - design-based stereology by virtual planes or spheres, and mathematical correction of the 2D projected-axon length - in contrast with direct measurement, to quantify individual axon length. To this end, we computationally simulated each tool, applied them over a dataset of 951 3D-reconstructed axons (from NeuroMorpho.org), and compared the generated length values with their 3D reconstruction counterparts. Additionally, the computational results were compared with estimated and direct measurements of individual axon lengths performed on actual brain tissue sections, to analyze the practical difficulties and biases arising in real cases. The evaluated reliability of each axon length estimation method is then balanced with the required human effort, experience and know-how, and economic affordability. This work, therefore, aims to provide a constructive benchmark to help guide the selection of the most efficient method for measuring specific axonal morphologies according to the particular circumstances of the conducted research. AUTHOR SUMMARYCharacterization of single neurons is a crucial step to understand how neural circuitry operates. Visualization of individual neurons is feasible thanks to labelling techniques that allows precise measurements at cellular resolution. This milestone gave access to powerful estimators of the functional impact of a neuron, such as axon length. Although techniques relying on direct 3D reconstruction of individual axons are the gold standard, handiness and accessibility are still an issue. Indirect estimations of axon length have been proposed as agile and effective alternatives, each offering different solutions to the accuracy-cost tradeoff. In this work we report a computational benchmarking between three experimental tools used for axon length estimation on brain tissue sections. Performance of each tool was simulated and tested for 951 3D-reconstructed axons, by comparing estimated axon lengths against direct measurements. Assessment of suitability to different research and funding circumstances is also provided, taking into consideration factors such as training expertise, economic cost and required equipment, alongside methodological results. These findings could be an important reference for research on neuronal wiring, as well as for broader studies involving neuroanatomical and neural circuit modelling.

neuroscience↗