Li, A.-S. | Search bioRxiv

bioRxiv2025

Dissociable neural substrates of integration and segregation in exogenous attention

Impact StatementThis study offers the first direct neuroimaging evidence for the integration-segregation theory of exogenous attention by identifying dissociable neural signatures of the competing integration and segregation processes, strengthening the understanding of attentional orienting mechanisms. The integration-segregation theory proposes that early facilitation and later inhibition (i.e., inhibition of return, IOR) in exogenous attention arise from the competition between cue-target event integration and segregation. Although widely supported behaviorally, the theory lacked direct neural evidence. Here, we used event-related fMRI with an optimized cue-target paradigm to test this account. Cued targets elicited stronger activation in the frontoparietal attention networks, including the bilateral frontal eye field (FEF) and intraparietal sulcus (IPS), right temporoparietal junction (TPJ), and left dorsal anterior cingulate cortex (dACC), consistent with the notion of attentional demand of reactivating the cue-initiated representations for integration. In contrast, uncued targets engaged the medial temporal cortex, particularly the bilateral parahippocampal gyrus (PHG) and superior temporal gyrus (STG), reflecting the segregation processes associated with new object-file creation and novelty encoding. These dissociable activations provide the first direct neuroimaging evidence for the integration-segregation theory. Moreover, we observed neural interactions between IOR and cognitive conflict, suggesting a potential modulation of conflict processing by attentional orienting. Taken together, these findings provide new insights into exogenous attention by clarifying the neural underpinnings of integration and segregation and uncovering the interaction between spatial orienting and conflict processing.

neuroscience↗

bioRxiv2024

Neural representations of predicted events: Evidence from time-resolved EEG decoding

Through statistical learning, humans are able to extract temporal regularities, using the past to predict the future. Evidence suggests that learning relational structures makes it possible to anticipate the imminent future; yet, the neural dynamics of predicting the future and its time-course remain elusive. To examine whether future representations are denoted in a temporally discounted fashion, we used the high-temporal-resolution of electroencephalography (EEG). Observers were exposed to a fixed sequence of events at four unique spatial positions within the display. Using multivariate pattern analyses trained on independent pattern estimators, we were able to decode the spatial position of dots within full sequences, and within randomly intermixed partial sequences wherein only a single dot was presented. Crucially, within these partial sequences, subsequent spatial positions could be reliably decoded at their expected moment in time. These findings highlight the dynamic weight changes within the assumed spatial priority map and mark the first implementation of EEG to decode predicted, yet critically omitted events. Impact statementUtilizing high-temporal-resolution EEG, the dynamic weight changes of assumed spatial priority map were visualized by decoding the spatial position of expected, yet omitted, events at their expected moment in time.

neuroscience↗