{"id":4876,"date":"2025-02-21T16:13:36","date_gmt":"2025-02-21T07:13:36","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/?post_type=event&p=4876"},"modified":"2025-02-21T16:19:36","modified_gmt":"2025-02-21T07:19:36","slug":"20250321_1848","status":"publish","type":"event","link":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/event\/20250321_1848\/","title":{"rendered":"\uff1cCiNet \u30e1\u30f3\u30d0\u30fc\u3092\u5bfe\u8c61\u306b\u30aa\u30f3\u30e9\u30a4\u30f3\u958b\u50ac\uff1e Friday Lunch Seminar \u77f3\u4e95 \u4e3b\u7a0e \uff1a\u201cEvent-related brain activity in natural conversation\u201d"},"content":{"rendered":"\n

2025\u5e743\u670821\u65e5\u3000\u3000Friday Lunch Seminar \uff08\u82f1\u8a9e\u3067\u958b\u50ac\u3057\u307e\u3059\uff09
12:15 \u301c 13:00
On-line\u958b\u50ac<\/p>\n\n\n\n

\u6f14\u984c\uff1aEvent-related brain activity in natural conversation<\/p>\n\n\n\n

\u60c5\u5831\u901a\u4fe1\u7814\u7a76\u6a5f\u69cb\uff08NICT\uff09
\u672a\u6765ICT\u7814\u7a76\u6240
\u8133\u60c5\u5831\u901a\u4fe1\u878d\u5408\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\uff08CiNet\uff09
\u8133\u6a5f\u80fd\u89e3\u6790\u7814\u7a76\u5ba4
\u7814\u7a76\u54e1\u3000\u77f3\u4e95 \u4e3b\u7a0e<\/a><\/p>\n\n\n\n

\u62c5\u5f53PI :  \u4e95\u539f \u7dbe<\/a><\/p>\n\n\n\n

<\/p>\n\n\n\n

Abstract:
Event-related potentials (ERPs) are powerful tools that capture cognitive processing of specific stimuli with high temporal resolution. However, traditional ERP approaches required sufficient intervals between events. While recent studies using multivariate temporal response functions (mTRF) have successfully estimated ERP-like responses during passive listening to naturalistic speech, this approach had not yet been applied to natural conversation. In this study, we extended this approach to natural conversations, estimating ERP-like responses to word events. We will present findings on associations between early and late attentional processes and both listeners’ and speakers’ subjective mental states and personality traits. Furthermore, we used machine learning with brain responses and turn-taking patterns as features to classify conversations into mutually satisfying and not mutually satisfying interactions.<\/p>\n\n\n\n

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