Our model jointly learns object embeddings and idiosyncratic weightings of psychological dimensions.
The results show improved accuracy on hold-out data as well as reliably measurable dimensional weights (up to ~8 dimensions) and summary stats thereof
Do people's internal representations of natural objects differ? We (@ericschulz.bsky.social) show so by incorporating idiosyncrasies into neural nets to create better models of human behavior. We also reliably extract idiosyncrasies and map them to demographics. More: osf.io/preprints/psyarxiv/pxt9a