Cognitive science has long established that human decision-making is often flawed by undesirable biases. A fundamental question concerns the underlying basis of such biases and in what types of situations they are most likely to appear. Real-world situations often require humans to perform sequences of decisions based on the same visual information (e.g., deciding whether a fruit is an apple or an orange and then deciding whether to eat that fruit or the one next to it). Very little is known about how different perceptual decisions interact in such visual processing sequences, yet data from a few recent studies suggest that a perceptual decision based on uncertain sensory evidence can substantially bias a person's subsequent percept of this evidence. With support from the National Science Foundation, Dr. Stocker will conduct and oversee research that uses a combined approach of computational modeling and human psychophysical experiments in order to understand how and why perceptual decisions affect subsequent visual percepts. Specifically, the investigator aims to test the hypothesis that the brain applies a decision strategy that ensures self-consistency in the interpretation of sensory information across a sequence of perceptual tasks. The proposed research will constitute a major step forward in understanding perceptual decision making under more natural conditions (in which decisions are not made independently). The results of the proposed research also have the potential to provide a major theoretical advance in linking perception and cognition, leading to a unifying understanding of human decision making strategies.
The research has direct applications for procedures that strongly rely on human experts to perform visual analyses of evidence in their decision-making (e.g. forensic sciences, medical sciences). A key feature of the research is its focus on the computational modeling of brain functions. Dr. Stocker's goal is to promote a rigid quantitative approach to the fields of psychology and behavioral neuroscience. Toward this end, the modeling techniques developed for this project will be directly incorporated in the investigator's graduate teaching. Furthermore, the investigator will organize a yearly modeling workshop for graduate students and postdoctoral fellows in psychology and neuroscience, and will also maintain an online repository of publicly-available learning tools relating to his modeling methods. Together, these efforts will help promote and integrate computational modeling into the mainstream neuroscience and psychology curricula.
