attention

We present a novel integration between a computational framework for modeling attention-driven perception and cognition (ARCADIA) with a cognitive robotic architecture (DIARC), demonstrating how this integration can be used to drive the gaze behavior of a robotic platform.

This paper presents a theoretical framework for modeling human visual attention.

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.

We claim that intentional action requires some analog of consciousness.

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

A computational model of working memory in the complex span task implemented in the ARCADIA cognitive framework.

Development of the ARCADIA cognitive system and computational model of attention.