| Paper: | PS-1A.22 | ||
| Session: | Poster Session 1A | ||
| Location: | H Lichthof | ||
| Session Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Measuring behavioural and neural responses to fluctuations in real-world predictability | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1035-0 | ||
| Authors: | Buddhika Bellana, Hongmi Lee, Xiaoye Zuo, Janice Chen, Johns Hopkins University, United States | ||
| Abstract: | A crucial component of brain function is to predict what will happen next. Although prediction is fundamental to brain function, we often study prediction of low-dimensional abstract tasks, rather than real-world events. We developed a novel behavioural approach to measure the dynamics of “real-world” predictability using audiovisual movies and natural language processing. Participants were shown an 11-minute movie, where viewing was occasionally interrupted by requests to generate sentences predicting what would happen next. These written predictions were converted into sentence embeddings using the Universal Sentence Encoder. Using these embedding vectors, we generated a timecourse of “situation-level” predictability during movie watching, revealing periods associated with homogeneous (high-predictability) and heterogeneous predictions (low-predictability) across-participants. We then regressed this timecourse of predictability on the fMRI data of a separate group of participants who watched the same movie, uninterrupted, in the scanner. During periods of high predictability, we observed higher activity in regions of the default mode network, while during periods of low predictability we observed higher activity in sensory cortices, consistent with internal-external models of cortical organization. Overall, we demonstrate the utility of natural language processing in quantifying fluctuations in real-world predictability. | ||