Ryan M Stolier

Columbia University

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I am a postdoctoral researcher in the Department of Psychology at Columbia University, where I work with Dr. Kevin Ochsner in the SCAN Lab. My research bridges methods of psychology and neuroscience to study how we think about other people. I ask these questions broadly, exploring how our impressions of others take into account their behaviors, reputation, appearance, and social category memberships. I am particularly interested in how social conceptual knowledge fundamentally structures our interpretations of others. Major themes of my research are person perception, face perception, stereotyping and prejudice, gossip and social networks, computational modeling, and multivariate analysis of functional magnetic resonance imaging.

Read more about my research here.

See my published work and research tools below. As well, visit the SCAN Lab site.


Stolier, R. M., Hehman, E., & Freeman, J. B. (under review). Conceptual structure shapes a common trait space across social cognition. [Preprint] [OSF]

Stolier, R. M. (2019). Conceptual associations guide social inference. [Preprint of doctoral dissertation].

Hehman, E., Stolier, R. M., Freeman, J. B., Flake, J., K., & Xie, S. (2018). Toward a comprehensive model of face impressions: What we know, what we don’t, and paths forward. Social and Personality Psychology Compass, e12431.

Freeman, J. B., Stolier, R. M., Brooks, J. A., & Stillerman, B. A. (2018). The neural representational geometry of social perception. Current Opinion in Psychology, 24, 83-91.

Stolier, R. M., Hehman, E., Keller, M. D., Walker, M., & Freeman, J. B. (2018). The conceptual structure of face impressions. Proceedings of the National Academy of Sciences of the United States of America, 115(37) 9210-9215. [Preprint] [OSF] [Supplementary Materials]

Brooks, J. A., Stolier, R. M., & Freeman, J. B. (2018). Stereotypes bias visual prototypes for sex and emotion categories. Social Cognition, 36(5), 481-493.

Stolier, R. M., Hehman, E., & Freeman, J. B. (2018). A dynamic structure of social trait space. Trends in Cognitive Sciences, 22(3), 197-200.

Stolier, R. M. & Freeman, J. B. (2017). A neural mechanism of social categorization. Journal of Neuroscience, 37(23), 5711-5721.

Lazerus, T., Ingbretsen, Z., Stolier, R. M., Freeman, J. B., & Cikara, M. (2016). Positivity bias in judging in-group members' emotional expressions. Emotion, 16(8), 1117.

Stolier, R. M., & Freeman, J. B. (2016). Functional and temporal considerations for top-down influences in social perception. Psychological Inquiry, 27(4), 352-357.

Stolier, R. M. & Freeman, J. B. (2016). Neural pattern similarity reveals the inherent intersection of social categories. Nature Neuroscience, 19(6), 795-797. [Supplementary Materials] [News & Views: 'Facing up to stereotypes']

Stolier, R. M., & Freeman, J. B. (2016). The neuroscience of social vision. Invited chapter for J. R. Absher & J. Cloutier (Eds.), Neuroimaging Personality, Social Cognition, and Character. Elsevier.

Hehman, E., Stolier, R. M., & Freeman, J. B. (2015). Advanced mouse-tracking analytic techniques for enhancing psychological science. Group Processes and Intergroup Relations, 18(3), 384-401.

Freeman, J. B., Stolier, R. M., Ingbretsen, Z. A., & Hehman, E. A. (2014). Amygdala responsivity to high-level social information from unseen faces. Journal of Neuroscience, 34(32), 10573-10581.

Freeman, J. B., & Stolier, R. M. (2014). The medial prefrontal cortex in constructing personality models. Trends in Cognitive Sciences, 18(11), 571-572.

Research Tools

Face Stimulus & Tool Collection

The Face Stimulus & Tool Collection is a concise, regularly updated, and curated list of face stimulus databases and editing tools (e.g., morphing).


PyMVPAw is a wrapper for PyMVPA, a Python package for multi-variate pattern analyses of neuroimaging data. In PyMVPAw, many of PyMVPA's pattern analysis tools are available in single function commands. As well, PyMVPAw includes many additional tools and analyses (further detailed in its github home, wiki, and Jupyter notebook tutorials).

To learn more and/or install, visit the PyMVPAw github home.


afniGLMprep is a Python package which prepares and runs GLMs on neuroimaging data stored in the BIDS format. In a single line of code, afniGLMprep creates stimulus timeseries and GLM scripts.

To learn more and/or install, visit the afniGLMprep github home.