Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects’ behavior and found that attention-like features in the behavioral model are essential for explaining subjects’ responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects

Neurons and the synaptic basis of the fMRI signal associated with cognitive flexibility

The Wisconsin Card Sorting Test (WCST) is well known to test cognitive flexibility in terms of set-shifting capabilities. Many fMRI studies with behaving monkeys as well as human subjects have shown transient neural activity in the Prefrontal Cortex (PFC), as indicated by an increase in the fMRI signal, following a rule change in the WCST or when using a WCST-like paradigm. We present a computational model, covering a limited number of PFC neurons and using precise biophysical descriptions, which is able to simulate WCS-like tests. Further, the detailed neuronal representation of the model allows us to calculate the resulting fMRI signal. Thus, we are able to analyze the adequacy of the model and its structure by comparing the calculated fMRI signal with the experimental data which in turn provides promising insights into the neural base of the increase in the fMRI signal