DocumentCode
40375
Title
I Can Already Guess Your Answer: Predicting Respondent Reactions during Dyadic Negotiation
Author
Sunghyun Park ; Scherer, Stefan ; Gratch, Jonathan ; Carnevale, Peter J. ; Morency, Louis-Philippe
Author_Institution
Inst. for Creative Technol., Univ. of Southern California, Los Angeles, CA, USA
Volume
6
Issue
2
fYear
2015
fDate
April-June 1 2015
Firstpage
86
Lastpage
96
Abstract
Negotiation is a component deeply ingrained in our daily lives, and it can be challenging for a person to predict the respondent´s reaction (acceptance or rejection) to a negotiation offer. In this work, we focus on finding acoustic and visual behavioral cues that are predictive of the respondent´s immediate reactions using a face-to-face negotiation dataset, which consists of 42 dyadic interactions in a simulated negotiation setting. We show our results of exploring four different sources of information, namely nonverbal behavior of the proposer, that of the respondent, mutual behavior between the interactants related to behavioral symmetry and asymmetry, and past negotiation history between the interactants. Firstly, we show that considering other sources of information (other than the nonverbal behavior of the respondent) can also have comparable performance in predicting respondent reactions. Secondly, we show that automatically extracted mutual behavioral cues of symmetry and asymmetry are predictive partially due to their capturing information of the nature of the interaction itself, whether it is cooperative or competitive. Lastly, we identify audio-visual behavioral cues that are most predictive of the respondent´s immediate reactions.
Keywords
behavioural sciences computing; acoustic behavioral cues; audio-visual behavioral cues; behavioral symmetry; dyadic negotiation; face-to-face negotiation dataset; nonverbal behavior; Acoustics; Context; History; Proposals; Speech; Time factors; Visualization; Human behavior analysis; human behavior analysis; negotiation; nonverbal behavior; prediction;
fLanguage
English
Journal_Title
Affective Computing, IEEE Transactions on
Publisher
ieee
ISSN
1949-3045
Type
jour
DOI
10.1109/TAFFC.2015.2396079
Filename
7024926
Link To Document