2018 Conference on Cognitive Computational Neuroscience

5-8 September 2018    Philadelphia, Pennsylvania

Technical Program

Paper Detail

Paper: PS-1B.21
Session: Poster Session 1B
Location: Symphony/Overture
Session Time: Thursday, September 6, 18:45 - 20:45
Presentation Time:Thursday, September 6, 18:45 - 20:45
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Uncovering mental and neural structure through data-driven ontology discovery
Manuscript:  Click here to view manuscript
DOI: https://doi.org/10.32470/CCN.2018.1179-0
Authors: Ian Eisenberg, Patrick Bissett, A Zeynep Enkavi, Jamie Li, Stanford University, United States; David MacKinnon, Arizona State University, United States; Lisa Marsch, Dartmouth College, United States; Russell Poldrack, Stanford University, United States
Abstract: Despite a wealth of behavioral and neural findings, psychology and cognitive neuroscience lack integrative theories. One difficulty is the apparent multifuctional character of neural function (Anderson, 2016), a perspective ultimately founded on our neural and cognitive ontologies (Shine, Eisenberg, & Poldrack, 2016), and potentially ameliorated by their reconceptualization. While the progressive development of our neural ontology in terms of brain atlases and functional networks is the norm, commiserate refinement of a cognitive ontology has been lacking. We forward a data-driven framework to integrate multiple psychological literatures into a new cognitive ontology. We examine individual-differences across an unprecedented range of behavioral tasks, self-report surveys and real-world outcomes and use factor-analysis to reduce the dimensionality of these measurements, creating a ”cognitive space” to serve as a common coordinate system to describe many cognitive constructs. Within the cognitive space measurements are structured, which is revealed through clustering. This new representation of cognitive measures provides a hypothesis for neural organization, which we pursue in an fMRI experiment where we scan participants completing a subset of the behavioral measures.