After his doctorate, Freeman joined the faculty of Dartmouth College as an Assistant Professor. He moved to New York University as an Assistant Professor in 2014, and was promoted to Associate Professor in 2018. He directs the Social Cognitive and Neural Sciences Lab. His research combines behavioural paradigms with computational modelling and human neuroimaging techniques such as functional magnetic resonance imaging. Broadly, Freeman investigates how we form social judgments and first impressions. In particular, his work has shown that, because facial cues are often complex and ambiguous, multiple “partial” perceptions must initially compete over fractions of a second. This dynamic competition is argued to be central to the ability to form social judgments. His research has proposed flexible interplay between social cognition and visual perception, and has shown that stereotypes and other kinds of social or emotional knowledge can affect visual processing. An example is how stereotypes become expectations that impact visual prototypes and create distortions in how faces are perceived. His research has demonstrated that tacit assumptions about social groups, emotions, or personality can all influence the way we perceive faces. Freeman studies several other topics in social neuroscience related to social cognition, emotion, and decision-making. For instance, he has examined mechanisms of bias learning and change, face perception outside conscious awareness, and the impact of split-second judgments on real-world outcomes. Freeman developed MouseTracker, a software that tracks decision-making in the brain by analysing the trajectory of a human subject’s hand movement via a mouse cursor. It allows researchers to assess real-time processing in cognitive tasks. MouseTracker is used by over 3,000 researchers in several different disciplines. Freeman's work has helped establish and popularise the mouse-tracking technique in cognitive science. Freeman is on the editorial board of Social Cognitive and Affective Neuroscience.
Advocacy and academic service
Freeman wrote a commentary for Nature about how implicit bias hinders the careers of LGBT people in science, technology, engineering, and math fields, and yet this group is often left out of diversity initiatives. He identified that LGBT people in STEM are less represented than expected, reporting negative workplace experiences, and leaving STEM fields at a high rate. Realising the importance of comprehensive data, he led a collaborative effort with the support of 17 scientific organisations asking the National Science Foundation to include questions about sexual orientation and gender identity in their national STEM workforce surveys. LGBT data from these surveys is critical for researchers and policymakers to be able to understand and address potential disparities and disadvantages of LGBT people in U.S. STEM fields. The National Science Foundation is currently piloting these questions for future surveys.
