How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis
Bibliographic record
Abstract
ABSTRACT Model-based cognitive neuroscience elucidates the cognitive processes and neurophysiological oscillations that lead to behavioral performance in cognitive tasks (e.g., response times and accuracy). In this paper we explore the underlying latent process of spatial prioritization in perceptual decision processes, based on one well-known sequential sampling model (SSM), the drift-diffusion model (DDM), and subsequent nested model comparison. Neural components of spatial attention which contributed to the latent process and behavioral performance in a visual face-car perceptual decision were detected based on both time-frequency decomposition and event-related potential (ERP) analysis. For estimating DDM parameters (i.e. the drift rate, the boundary separation, and the non-decision time), a Bayesian hierarchical approach is considered, which allows inferences to be performed simultaneously on the group and individual level. Our cognitive modeling analysis revealed that spatial attention changed the non-decision time parameter across experimental conditions, such that a model with a changing non-decision time parameter provides a better fit to the data than other model parameters, quantified using the deviance information criterion (DIC) score and R-squared. Using multiple regression analysis on the contralateral minus neutral N2 sub-component (N2nc) at central electrodes, it can be concluded that poststimulus N2nc can predict mean response times (RTs) and non-decision time parameters related to spatial prioritization. However the contralateral minus neutral alpha power (Anc) at parieto-occipital electrodes can only predict the mean RTs and not the non-decision time relating to spatial prioritization. It was also found that the difference of contralateral minus neutral neural oscillations were more reflective of the modulation of the top-down spatial attention in comparison to the difference of ipsilateral minus neutral neural oscillations. These results suggest that individual differences in spatial attention are encoded by contralateral (and not ipsilateral) N2 oscillations and non-decision times. This work highlights how model-based Cognitive Neuroscience can further reveal the role of EEG in spatial attention during perceptual decision making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".