Common attentional mechanisms, but distinct computational dynamics, across Statistical and Reward Learning
- 1Ghent University
Abstract
Past experience guides attention through statistical learning (SL) and reward learning (RL). Yet, whether they do so through similar neural and computational mechanisms remains unclear. In a first EEG visual-search experiment, we tested whether RL establishes latent spatial representations, previously demonstrated for SL. Afterwards, we directly compared RL and SL in a double-session experiment. Using multivariate decoding, Experiment 1 showed that high-reward locations could be decoded before target onset following a task-irrelevant, spatially-unspecific visual perturbation. Experiment 2 revealed that SL and RL established comparable neural representations, and similarly facilitated target selection and evidence accumulation (drift-rate). However, combining Rescorla-Wagner and Drift Diffusion Model revealed slower learning-rate for RL than SL. Pupillometry further tracked spatially selective expectation violations during SL, but reflected increased arousal during RL. Together, our results reveal how the brain routes distinct computational algorithms (probability estimation versus value caching) into a unified attentional priority map.
Related
- Trial After Trial: Temporal Dynamics Reveal a Distinct Computational Phenotype of Chronic Cannabis Use
- Identifying attentional control mechanisms related to the performance of high- and low-skilled First-Person Shooter game players: With exploratory data on expert older-adult esports players
- Attentional network processing in children and adults shows comparable behavioural effects but distinct ERP and time–frequency activity
- A Relevance Rate Model of the affective mechanisms linking social media use to later attentional disruption
- Sensing the heart, seeing the world: Neural mechanisms of attentional allocation between interoception and exteroception
BibTeX
@misc{dolci2026common,
title = {{Common attentional mechanisms, but distinct computational dynamics, across Statistical and Reward Learning}},
author = {C. Dolci and T. Verguts and W. J. Louter and E. Santandrea and N. Boehler},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.04.764241},
url = {https://www.biorxiv.org/content/10.64898/2026.11.04.764241v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Common attentional mechanisms, but distinct computational dynamics, across Statistical and Reward Learning
AU - C. Dolci
AU - T. Verguts
AU - W. J. Louter
AU - E. Santandrea
AU - N. Boehler
PY - 2026
DA - 2026/11/04
PB - bioRxiv
DO - 10.64898/2026.11.04.764241
UR - https://www.biorxiv.org/content/10.64898/2026.11.04.764241v1
AB - Past experience guides attention through statistical learning (SL) and reward learning (RL). Yet, whether they do so through similar neural and computational mechanisms remains unclear. In a first EEG visual-search experiment, we tested whether RL establishes latent spatial representations, previously demonstrated for SL. Afterwards, we directly compared RL and SL in a double-session experiment. Using multivariate decoding, Experiment 1 showed that high-reward locations could be decoded before target onset following a task-irrelevant, spatially-unspecific visual perturbation. Experiment 2 revealed that SL and RL established comparable neural representations, and similarly facilitated target selection and evidence accumulation (drift-rate). However, combining Rescorla-Wagner and Drift Diffusion Model revealed slower learning-rate for RL than SL. Pupillometry further tracked spatially selective expectation violations during SL, but reflected increased arousal during RL. Together, our results reveal how the brain routes distinct computational algorithms (probability estimation versus value caching) into a unified attentional priority map.
ER -