Biology subjects

Fischer, A. G.

Publications and source records attributed to Fischer, A. G..

2 recordsLinked to original sources

bioRxiv2023

Cortical beta power reflects a neural implementation of decision boundary collapse in a speeded flanker task

A prominent account of decision-making assumes that information is accumulated until a fixed response threshold is crossed. However, many decisions require weighting of information appropriately against time. Collapsing response thresholds are a mathematically optimal solution to this decision problem. However, our understanding of the neurocomputational mechanisms that underly dynamic response thresholds remains very incomplete. To investigate this issue, we used a multistage drift diffusion model (DDM) and also analysed EEG beta power lateralization (BPL). The latter served as a neural proxy for decision signals. We analysed a large dataset (n=863) from a speeded flanker task and data from an independent confirmation sample (n=119). We show that a DDM with collapsing decision thresholds, a process where the decision boundary reduces over time, captured participants time-dependent decision policy better than a model with fixed thresholds. Previous research suggests that BPL over motor cortices reflects features of a decision signal and that its peak may serve as a neural proxy for the decision threshold. Our findings offer compelling evidence for the existence of collapsing decision thresholds in decision-making processes.

neuroscience↗

bioRxiv2021

Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices

Optimal decision making in complex environments requires dynamic learning from unexpected events. To speed up learning, we should heavily weight information that indicates state-action-outcome contingency changes and ignore uninformative fluctuations in the environment. Often, however, unrelated information is hard to ignore and can potentially bias our learning. Here we used computational modelling and EEG to investigate learning behaviour in a modified probabilistic choice task that introduced two types of unexpected events that were irrelevant for optimal task performance, but nevertheless could potentially bias learning: pay-out magnitudes were varied randomly and, occasionally, feedback presentation was enhanced by visual surprise. We found that participants overall good learning performance was biased by distinct effects of these non-normative factors. On the neural level, these parameters are represented in a dynamic and spatiotemporally dissociable sequence of EEG activity. Later in feedback processing the different streams converged on a central to centroparietal positivity reflecting a final pathway of adaptation that governs future behaviour.

neuroscience↗