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.Author Summary: When making decisions in our everyday life (e.g. where to eat) we first have to identify a set of environmental features that are relevant for the decision (e.g. the distance to the place, current time or the price). Although we are able to make such inferences almost effortlessly, this type of problems is computationally challenging, as we live in a complex environment that constantly changes and contains an immense number of features. Here we investigated the question of how the human brain solves this computational challenge. In particular, we designed a new experimental paradigm and derived novel behavioral models to test the hypothesis that attention modulates the formation of beliefs about the relevance of several environmental features. As each behavioral model accounted for a different hypothesis about the underlying computational mechanism we compared them in their ability to explain the measured behavior of human subjects performing the experimental task. The model comparison indicates that an attentional-focus mechanism is a key feature of behavioral models that accurately replicate subjects’ behavior. These findings suggest that the evolution of beliefs is modulated by a competitive attractor dynamics that forms prior expectation about future outcomes. Hence, the findings provide interesting and novel insights into the computational mechanisms underlying human behavior when making decisions in complex environments.