Dr Martin Hebart | Towards a comprehensive understanding of mental representations and categorical decisions about real-world objects

Humans constantly interact with an ever-changing visual environment and are experts at rapidly extracting information about the objects in the world. At the same time, capturing the complexity of real-world object recognition and categorical decision-making with classical small-scale laboratory experiments has been surprisingly difficult. Here I propose an approach to overcome these challenges, which promises a comprehensive understanding of (1) what are the dimensions underlying our mental representation of objects, (2) how they are integrated into categorical decisions, and (3) how these representations are affected by task context. The approach encompasses two strategies: a data-driven strategy using large-scale acquisition and analysis of behavioral and neuroimaging data; and a model-driven strategy drawing on recent developments in artificial intelligence that allow us to constrain the space of possible hypotheses and develop new models of visual cognition. In this talk, I will illustrate this approach with recently-conducted research examples and will lay out a roadmap for future projects.

Poster