Biology subjects

Verguts, T.

Publications and source records attributed to Verguts, T..

3 recordsLinked to original sources

bioRxiv2018

A post-decisional neural marker of confidence predicts information-seeking

Theoretical work predicts that decisions made with low confidence should lead to increased information-seeking. This is an adaptive strategy because it can increase the quality of a decision, and previous behavioral work has shown that decision-makers engage in such confidence-driven information seeking. The present study aimed to characterize the neural markers that mediate the relationship between confidence and information-seeking. A paradigm was used in which human participants made an initial perceptual decision, and then decided whether or not they wanted to sample more evidence before committing to a final decision and confidence judgment. Pre-decisional and post-decisional ERP components were similarly modulated by the level of confidence and by information-seeking choices. Time-resolved multivariate decoding of scalp EEG signals first revealed that information-seeking choices could be decoded from the time of the initial decision to the time of the subsequent information-seeking choice (within-condition decoding). No above-chance decoding was visible in the pre-response time window. Crucially, a classifier trained to decode high versus low confidence predicted information-seeking choices after the initial perceptual decision (across-condition decoding). This time window corresponds to that of a post-decisional neural marker of confidence. Collectively, our findings demonstrate for the first time that neural indices of confidence are functionally involved in information-seeking decisions.

neuroscience↗

bioRxiv2017

Preparation for mental effort recruits Dorsolateral Prefrontal Cortex: an fNIRS investigation

Preparing for a mentally demanding task calls upon cognitive and motivational resources. The underlying neural implementation of these mechanisms is receiving growing attention, given the implications for professional, social, and medical contexts. While several fMRI studies converge in assigning a crucial role to a cortico-subcortical network including Anterior Cigulate Cortex (ACC) and striatum, the involvement of Dorsolateral Prefrontal Cortex (DLPFC) during mental effort anticipation has yet to be replicated. This study was designed to target DLPFC contribution using functional Near Infrared Spectroscopy (fNIRS), as a more cost-effective tool measuring cortical hemodynamics. We adapted a validated mental effort task, where participants performed easy and difficult mental calculation, while measuring DLPFC activity during the anticipation phase. As hypothesized, DLPFC activity increased during preparation for a hard task as compared to an easy task. Besides replicating a previous fMRI study, these results establish fNIRS as an effective tool to investigate cortical contributions to preparation for effortful behavior. This is especially useful if one requires testing large samples (e.g., to target individual differences), populations with contraindication for functional MRI (e.g., infants or patients with metal implants), or subjects in more naturalistic environments (e.g., work or sport).

neuroscience↗

bioRxiv2017

Dorsal Anterior Cingulate-Midbrain Ensemble As A Reinforcement Meta-Learner

The dorsal anterior cingulate cortex (dACC) is central in higher-order cognition and behavioural flexibility. The computational nature of this region, however, has remained elusive. Here we propose a new model - the Reinforcement Meta Learner (RML) - based on the bidirectional anatomical connections of the ACC with midbrain catecholamine nuclei (VTA and LC). In this circuit, dACC learns which actions are valuable and acts accordingly. Crucially, this mechanism is optimized by recurrent connectivity with the midbrain: Midbrain catecholamines provide modulatory signals to dACC, controlling its internal parameters (e.g. learning rate), while these parameter modulations are in turn optimized by dACC afferents to the midbrain. This closed-loop system generates emergent (i.e., homunculus-free) control and supports learning to solve hierarchical decision problems without having an intrinsic hierarchical structure itself. Further, it can be combined with other cortical modules to optimize the processing of these modules. We outline how the RML solves the current theoretical stalemate on dACC by assimilating various previous proposals on ACC functioning, and how it captures critical empirical findings from an unprecedented range of domains (stability/plasticity balance, effort processing, working memory, and higher-order classical and instrumental conditioning).

neuroscience↗