Biology subjects

Deraeve, J.

Publications and source records attributed to Deraeve, J..

3 recordsLinked to original sources

bioRxiv2019

Conjunction or co-activation? A multi-level MVPA approach to task set representations

While representing and maintaining rules in order to govern behavior is a critical function of the brain, it remains an open question as to how collections of rules - task sets - are represented in cortex. One possibility is that task sets are represented as the co-activation of representations of the simple rules from which a task set is composed. Alternatively, task sets could be encoded in a conjunctive manner as the unique combination of rules that belong to a task set. Using a novel multi-level MVPA approach in combination with fMRI, we attempted to answer both \"where\" and \"how\" task sets are represented in the brain. Subjects performed a delayed match-to-sample task using task sets composed of multiple, partially overlapping rules that governed which feature dimensions subjects should attend to, and MVPA was used to identify regions that encoded task set information. We identified voxels most relevant for classifying task sets, and, using these voxels as input to a second MVPA analysis, were able to identify regions in prefrontal cortex with activity consistent with co-active representation, while activity in visual cortex was consistent with conjunctive representation. These results highlight the utility of feature selection methods in neuroimaging analyses.

neuroscience↗

bioRxiv2018

Testing competing models of dorsal anterior cingulate

Recent theories have attempted to provide unifying accounts of dorsal anterior cingulate cortex (dACC), a region routinely observed in studies of cognitive control and decision-making. Despite the proliferation of frameworks, rigorous empirical testing has lagged behind theory. Here we test competing predictions of three accounts of dACC using a simple value-based decision-making task. We find that the Predicted Response-Outcome model provides an integrative and parsimonious account of our results. Our results highlight the need for increased emphasis on empirical tests of theoretical frameworks.

neuroscience↗

bioRxiv2017

Predicting motivation: computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted in order to obtain potential benefits. Medial prefrontal cortex (MPFC) and dorsolateral prefrontal cortex (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward, and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (PRO model, Alexander and Brown, 2011, HER Model, Alexander and Brown, 2015). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations, and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions which describe how effort and reward information is coded in PFC, and how changing the configuration of such environmental information might affect decision-making and task-performance involving motivation.

neuroscience↗