Bear Moon Goldstein

Excited to share new work from @bear_goldstein, @shannon47burns & my lab! We’re introducing a dynamic analytical approach for Hyperscanning called Cross-Recurrence Quantification Analysis (CRQA) that captures the complex coupling traditional methods often miss.

Major thank you's to my co-authors, Shannon Burns (@shannon47burns.bsky.social), Ashley Binnquist, Macrina Dieffenbach, Csaba Konkoly, Shira Abromowitz, and Matt Lieberman (@socialbrain.bsky.social) for all their hard work and contributions. Let us know what you think! 8/8

Our findings suggest that brain-based measures can offer insights into people’s psychological lenses in real-world contexts, highlighting their potential value for interventions aimed at fostering supportive and engaged environments. 7/8

Since MTPA selects a subset of timepoints, we can map those back onto the stimulus to reverse-engineer which elements evoked predictive neural patterns. Features predicting overwhelm aligned with emotional moments, while predicting need for a new/different challenge aligned with social moments. 6/8

One of my fav parts of the study was developing Multi-Timepoint Pattern Analysis (MTPA). MTPA can be viewed as a temporal analog to MVPA, but instead of a spatial searchlight it uses a “temporal searchlight” to select a subset of features. Check out another recent paper for more: bit.ly/4qeUEBq. 5/8

This isn’t “mind-reading” in the sense that we are predicting the objective content a person is presented with, but rather the subjective experience of that content that differs from person to person – the psychological “lenses” through which people see, interpret, and experience everyday life. 4/8

We scanned business executives’ brains as they watched a video of others discussing their attitudes toward work. ML models were able to predict who felt overwhelmed using the TPJ and who was in need of a new/different challenge in their career using the dmPFC with ~73-79% accuracy. 3/8

Negative experiences at work often remain hidden as people struggle to share their feelings or fear being judged if they do. What if there was a way to unobtrusively predict hidden and persistent states (eg feeling overwhelmed) so that we can better identify issues and target support? 2/8

Excited to share our new paper out in PNAS: “Neural predictors of hidden, persistent psychological states at work.” Full paper at bit.ly/47oaGRR

We brought portable fNIRS into the field to predict the subjective work-related “lenses” of business leaders. Check out the thread below for more! 1/8

Negative experiences at work often remain hidden as people struggle to share their feelings or fear being judged if they do. What if there was a way to unobtrusively predict hidden and persistent states (eg feeling overwhelmed) so that we can better identify issues and target support? 2/8

Hello world (ie my 3 followers). Re-entering online life for science, and the occasional meme. Excited to connect!