cognitive systems

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 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 working memory in the complex span task implemented in the ARCADIA cognitive framework.

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

The notion of an intelligent agent in AI is too coarse and in need of refinement. We suggest that the space of AI agents can be subdivided into classes, where each class is defined by an associated degree of control.

We present a computational model exploring goal-directed deployment of attention during object tracking. Once selected, objects are tracked in parallel, but serial attention can be directed to an object that is visually crowded and in danger of being lost.

We present a novel computational model of enumeration in which attention unifies distinct processes of numerosity approximation, subitizing, and explicit counting.

We present an account of object tracking in the ARCADIA cognitive system that treats MOT as dependent upon both pre-attentive and attention-bound processes. We show that with minimal addition this model replicates a variety of core phenomena in the MOT literature and provides an algorithmic explanation of human performance limitations.

We describe an attention-centric cognitive system called ARCADIA that demonstrates the orienting, filtering, and resource-skewing functions commonly attributed to attentional mechanisms.