Determining the independence of face dimensions through an extended multidimensional signal detection theory Grant

Determining the independence of face dimensions through an extended multidimensional signal detection theory .


  • People can easily perceive several separate properties of a face, such as its identity, emotional expression, age, and movement. An important question is whether the brain processes each of those properties independently, so that, for example, it is easy to identify a friend regardless of whether she is happy, angry, moving her eyes, shaking her head, or even if we have not seen her in years. Although influential theories of face recognition propose that some face properties are indeed processed independently from others, a review of the literature shows that it is full of contradictory results, probably due to lack of critical experimental controls. The present project seeks to determine whether face identity, expression, and motion are processed independently, using both behavioral and brain measures of independence. A better understanding of how face properties are perceived in healthy individuals could in turn lead to a better understanding of the reason behind impairments in face perception in some disorders, such as depression, anxiety, schizophrenia, and autism.To resolve the contradictory findings from previous research, the investigator proposes to use a novel methodology in a series of experiments that use both behavioral and brain measures of face perception. First, tight control over the properties of faces presented to participants is achieved using computer graphics: three-dimensional models of faces and expressions are created, and images and videos are obtained from those models. Because of advances in computer graphics, such images and videos look quite naturalistic, but their shape and movements can be controlled with high precision. Second, mathematical models of perception are used to analyze the data. These models provide rigorous definitions of features of perceptual processing such as independence, and ways to measure those features while separating them from decision-making strategies used by participants in behavioral tasks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

date/time interval

  • September 1, 2020 - August 31, 2023

sponsor award ID

  • 2020982