This interdisciplinary research project explores visual perception and attention within a computational system. The work draws inspiration from human psychology to develop software that generates high-level descriptions of visual scenes in a human-like manner. The software is guided by a model of attention that focuses processing on task-relevant objects and ignores irrelevant details.

The ideal candidate will have a strong programming background and an interest in artificial intelligence and cognitive science. Particular topics of interest include perception & attention, spatial reasoning, and qualitative spatial representations. Although the focus will be on computational work, there will be opportunities to conduct empirical research with human participants, in order to explore questions about human perception and to compare human behavior against the computational system’s behavior.

References

* Lovett, A., Bridewell, W., & Bello, P. (2019). Selection enables enhancement: Modeling a key spatial ability. Journal of Vision, 19(14).

* Lovett, A., & Forbus, K. (2017). Modeling visual problem-solving as analogical reasoning. Psychological Review, 124(1), 60-90.

* Lovett, A., & Forbus, K. (2017). Topological relations between objects are categorically coded. Psychological Science, 28(10), 1408-1418.

Additional Benefits

Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.

A group health insurance program is available to awardees and their qualifying dependents in the United States.