cognitive model

We argue that the insights offered by the literature on agency and intentional action motivate a particular kind of computational cognitive architecture and present an attention-driven cognitive system as first steps toward an architecture to support the type of agency that rich human–machine interaction will undoubtedly demand.

In computational simulations of two psychological experiments, we demonstrate that spatial competition during enhancement and temporal competition for selection can explain a range of findings on multiple-object tracking, and we argue that the interaction between selection and enhancement captured in the model is critical to understanding attention more broadly.

Meeting abstract presented at VSS 2019

We present a cognitive model that is able to exert limited top-down control over attentional capture, increasing the probability that task-relevant objects will be attended to and irrelevant objects will be ignored.

A computational model of task switching developed within the ARCADIA framework.

Meeting abstract presented at VSS 2017