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This study introduces a novel methodology for consciousness science. This method is designed to support a quantitative science of consciousness while avoiding metaphysical commitments. We discuss how this methodology applies to current and future research and address questions that others have raised.

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.

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

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 argue that, although belief revision mechanisms surely operate at the level of single agents, we must also consider the need to lift an agent’s understanding of the belief revision process to the knowledge level in order to intentionally guide other agents’ revision processes with whom it socially interacts.

We argue that distinguishing between types of deception is required to generate successful action. We introduce a Framework for Identifying Deceptive Entities and demonstrate that it has the representational power to discriminate categories of deception.

We focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning.

We report on a system for inductive process modeling that uses structural constraints to reduce its search through the space of candidate models and to produce ones that human scientists find plausible.

We pose a novel research problem for machine learning that involves constructing a process model from continuous data.