Biology subjects

Care, D.

Publications and source records attributed to Care, D..

3 recordsLinked to original sources

bioRxiv2026

Time space signatures of hybrid search resolution using EEG and eye movements concurrent recordings

Understanding how the brain supports visual search in naturalistic environments--where attention and memory must work together to find targets among distractors--requires analysing neural signals where responses overlap in time and multiple environmental variables simultaneously interact. Conventional event-related methods cannot disentangle these overlapping signals, creating a fundamental bottleneck for studying cognition in ecologically valid settings. Here, we seek to isolate activation patterns during a hybrid visual and memory search task in naturalistic scenarios. We show that our deconvolution-based approach applied to coregistered EEG and eye-tracking data resolves this problem, capturing fine-grained activation patterns in the temporal response functions (TRFs) for main effects and their interactions. Starting from hypothesis-driven models, we replicated established components for visual processing and target detection in a Hybrid Search task with unrestricted eye movements. Moreover, extending our approach to hierarchically larger data-driven models enabled us to explore interactions between the effects that have otherwise been studied separately. We showed that the TRF estimates remained stable with increasing model complexity, supported by improved model performance (Pearsons correlation coefficient) and controlled by the variance inflation factor (VIF). We identified a late activation consistent with the P300 component for target detection, and revealed that missed detections elicited similar but weaker responses, suggesting a more nuanced role than simple detection. These findings demonstrate how deconvolution methods, complemented with robust measures of model performance that support its expansion in features space, can uncover the dynamic interplay of attention and memory processes underlying free-viewing behavior.

neuroscience↗

bioRxiv2024

Integrating Ideal Bayesian Searcher and Neural Networks Models for Eye Movement Prediction in a Hybrid Search Task

Visual search, where observers search for a specific item, is a crucial aspect of daily human interaction with the visual environment. Hybrid search extends this by requiring observers to search for any item from a given set of objects. While there are models proficient at simulating human eye movement in visual search tasks within natural scenes, none are able to do so in Hybrid search tasks within similar environments. In this work, we present an enhanced version of the neural network Entropy Limit Minimization (nnELM) model, which is based on a Bayesian framework and decision theory. We also present the Hybrid Search Eye Movements (HSEM) Dataset, comprising several thousands of human eye movements during hybrid search tasks in natural scenes. A key challenge in Hybrid search, absent in visual search, is that participants might search for different objects at different time points. To address this, we developed a strategy based on the posterior probability distribution generated after each fixation. By adjusting the models peripheral visibility, we made early search stages more efficient, aligning it closer to human behaviour. Additionally, limiting the models memory capacity reduced its success in longer searches, mirroring human performance. To validate these improvements, we compared our model against participants from the HSEM dataset and against existing models in a visual search benchmark. Altogether, the new nnELM model not only successfully explains Hybrid search tasks, but also closely replicates human behaviour in natural scenes. This work advances our understanding of complex processes underlying visual and Hybrid search while maintaining model interpretability.

neuroscience↗

bioRxiv2022

Active search signatures in a free-viewing task exploiting concurrent EEG and eye movements recordings

Tasks we often perform in our everyday lives, such as reading or looking for a friend in the crowd, are seemingly straightforward but they actually require the orchestrated activity of several cognitive processes. Free-viewing visual search requires a plan to move our gaze on the different items, identifying them, and deciding on whether to continue with the search. Little is known about the electrophysiological signatures of these processes in free-viewing since there are technical challenges associated with eye movement artifacts. Here we aimed to study how category information, as well as ecologically-relevant variables such as the task performed, influence brain activity in a free-viewing paradigm. Participants were asked to observe/search from an array of faces and objects embedded in random noise. We concurrently recorded EEG and eye movements and applied a deconvolution analysis approach to estimate the contribution of the different elements embedded in the task. Consistent with classical fixed-gaze experiments and a handful of free-viewing studies, we found a robust categorical effect around 150 ms in occipital and occipitotemporal electrodes. We also report a task effect, more negative in posterior central electrodes in visual search compared to exploration, starting at around 80 ms. We also found significant effects of trial progression, and an interaction with the task effect. Overall, these results generalise the characterisation of early visual face processing to a wider range of experiments and show how a suitable analysis approach allows to discern among multiple neural contributions to the signal, preserving key attributes of real-world tasks.

neuroscience↗