Biology subjects

Pena, P.

Publications and source records attributed to Pena, P..

3 recordsLinked to original sources

bioRxiv2026

Multiple-Demand Network encoding geometry balances generalization and dimensionality during novel task assembly.

On the basis of verbal instructions, humans can accomplish novel and diverse demands on the very first try. This complex phenomenon recruits structured brain activity across the frontoparietal Multiple Demand Network (MDN), which is thought to encode upcoming task parameters and guide behavior. However, it is uncertain how novel instructions are translated into efficient neural task representations. To address this, we collected functional magnetic resonance imaging (fMRI) data while participants followed a rich set of novel verbal instructions. These varied along three core dimensions: the overarching task demand (to select or to integrate stimuli information), the relevant target category (animate or inanimate items), and the visual feature that participants responded to (color or shape). We used multivariate pattern analysis (MVPA) to examine whether and how each of these dimensions was reflected in MDN activity. We contrasted two alternative representational geometries that may underpin novel task coding: low-dimensional spaces based on abstract and generalizable representations, and high-dimensional architectures hosting context-unique, conjunctive neural codes. Our results showed that anticipatory MDN activity was sensitive to the content of instructions. While selection vs. integration task demands were broadly encoded across the MDN, coding of the relevant categories and features was restricted to lateral MDN regions, namely, the intraparietal sulcus and the inferior frontal junction. Critically, the representational spaces across the MDN displayed a mixture of geometrical motifs, partially supporting our two alternative hypotheses. On the one hand, Cross-Condition Generalization Performance revealed the presence of abstract and transferable neural codes for task demand information. On the other hand, Shattering Dimensionality showed complex, high dimensional coding spaces across the MDN, structured around both task-informative and non-informative axes. Despite this, no evidence of conjunctive neural codes was observed. Overall, these findings highlight that novel instructed behavior may recruit both abstraction and high dimensionality to promote generalization while maximizing the expressivity of MDN coding spaces. More broadly, they emphasize the importance of considering encoding geometry for a computational understanding of cognitive control processes.

neuroscience↗

bioRxiv2025

Rapid Computation of High-Level Visual Surprise

Predictive processing theories propose that the brain continuously generates expectations about incoming sensory information. Discrepancies between these predictions and actual inputs, sensory prediction errors, guide perceptual inference. A fundamental yet largely unresolved question is which stimulus features the brain predicts, and therefore, what kind of surprise drives neural responses. Here, we investigated this question using EEG and computational modelling based on deep neural networks (DNNs). Participants viewed object images whose identity was probabilistically predicted by preceding cues. We then quantified trial-by-trial surprise at both low-level (early DNN layers) and high-level (late DNN layers) visual feature representations. Results showed that stimulus-evoked responses around 200ms post-stimulus onset over parieto-occipital electrodes were increased by high-level, but not by low-level visual surprise. These findings demonstrate that high-level visual predictions are rapidly integrated into perceptual inference, suggesting that the brains predictive machinery is finely tuned to utilize expectations abstracted away from low-level sensory details to facilitate perception.

neuroscience↗

bioRxiv2024

Novel verbal instructions recruit abstract neural patterns of time-variable information dimensionality

Human performance is endowed by neural representations of information that is relevant for behavior, some of which are also activated in a preparatory fashion to optimize later execution. Most studies to date have focused on highly practiced actions, leaving largely unaddressed the novel re-configuration of information to generate unique whole task-sets. Using electroencephalography (EEG), this study investigated the dynamics of the content and geometry reflected on the neural patterns of control representations during re-configuration of information. We designed a verbal instruction paradigm where each trial involved novel combinations of multi-component task information. By manipulating three task-relevant factors in a sample of 40 participants (26 females, 14 males), we observed complex coding schemes throughout the trial, during both preparation and implementation stages. The temporal profiles were consistent with a hierarchical structure: whereas task information was active in a sustained manner, the coding of more concrete stimulus features was more transient. Data showed both high dimensionality and abstraction, particularly during instruction encoding and target processing. Our results suggest that whenever task content could be recovered from neural patterns of activity, there was evidence of abstract coding, with an underlying geometry that favored generalization. During target processing, where potential interference across stimulus and response factors increased, orthogonal configurations also appeared. Overall, our findings uncover the dynamic manner with which control representations operate during novel recombination unique scenarios, with changes in dimensionality and abstraction adjusting along processing stages. Significance StatementThe neural mechanisms that support task performance in novel contexts have been largely overlooked. Cognitive control is thought to enable complex behavior through the active maintenance of task sets, containing essential information for execution. However, how novel whole combinations of information are organized in neural patterns and their temporal dependencies remain unknown. Here, using a novel complex instruction paradigm, we observed that coding of informational content and its underlying geometry followed a dynamic temporal pattern. Our results reveal varying dimensionality and abstraction throughout the trial, with neural codes generally structured in a geometry favoring generalization of relevant information across task demands. These findings provide a first glimpse into the temporal computations engaged by the brain when encountering novel recombination settings.

neuroscience↗