2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-2A.64
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: The Structure of Cognition Across Computational Cognitive Neuroscience
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1426-0
Authors: Richard Gao, UC San Diego, United States; Dylan Christiano, UCLA, United States; Tom Donoghue, Bradley Voytek, UC San Diego, United States
Abstract: Computational Cognitive Neuroscience aims to characterize the neural computations underlying behavior. To do so, we must integrate our understanding of cognition across its different subfields: cognitive science, computational neuroscience, cognitive neuroscience, and machine learning. One key challenge is evaluating whether the structure of cognitive processes – their definitions and interrelations – in each subfield is similar. If not, how different are they and how can we measure and ameliorate those differences? To answer these questions, we mined scientific abstracts from conferences representative of subfields to learn field-specific word embeddings of cognitive concepts using Word2Vec. Vector representations are then used to generate hierarchical and 2D visualizations, forming empirical cognitive ontologies for each subfield. We find that robust ontologies, such as clusters representing language-related concepts, are automatically generated from each corpus. While differences between corpora are evident, exploratory analysis with word vectors can perform similarity queries, as well as more complex algebraic queries, e.g., “working memory” without “memory” retrieves “attention”. These results demonstrate the utility of automated text-mining and natural language processing in serving as a hypothesis-generating procedure to populate manually-maintained ontologies in cognitive science, as well as suggesting potentially overlooked research opportunities across subfields.