Latent Semantic Similarity in Unstructured, Initial Dyadic Interactions

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How latent semantic similarity affects dyadic interactions.

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Latent Semantic Similarity in Unstructured, Initial Dyadic Interactions

  1. 1. Latent Semantic Similarity in Unstructured, Initial Dyadic Interactions Vivian Ta, Meghan Babcock, Dr. William Ickes Oral Presentation for ACES March 26, 2014
  2. 2. Introduction • How do people develop a basis of understanding each other? • Developing a “common-ground understanding1” or an “intersubjective meaning context2”  Using the same words in essentially the same way  Involved behaviors:  1) How much the interaction partners talk to each other  2) How much the interaction partners look at each other  3) How much the interaction partners acknowledge each other 1Abbeduto, Short-Meyerson, Benson, Dolish, & Weissman, 1998; Kecskes & Zhang, 2009; Krauss & Fussell, 1991; Schober & Clark, 1989; Wilkes-Gibbs & Clark, 1992 2Gesn & Ickes, 1999; Morganti, 2008
  3. 3. Latent Semantic Analysis • Program that determines the contextual meaning of any text • Exams the relationships among words (Landauer & Dumais, 1997; Landauer et al., 1998)  Word/word pattern usage • LSA Pairwise Comparison program • Analyzes the similarity of 2 blocks of text  Produces an index from -1 to 1 to indicate overall degree of latent semantic similarity (LSS index)  Measures overall semantic similarity
  4. 4. Hypothesis • LSS will be positively correlated with  1. How much dyad members talk to each other  2. How much dyad members look at each other  3. How much dyad members acknowledge each other
  5. 5. Participants • Archival data from Ickes, Tooke, Stinson, Baker, Bissonnette (1988) • 46 dyads (92 students) • Male-Male (20) & Female-Female (26) • Strangers • Undergraduate students
  6. 6. Procedure • Participants came into Social Interaction Lab • Experimenter left to retrieve important items • Covertly audio-videotaped for 6 minutes • Completed a post-interaction questionnaire (e.g., did you like your partner, how smooth was the interaction, how awkward was the interaction, etc.) • Coded various behaviors (e.g., duration of mutual gazes, expressive gestures, head nods, etc.) • Transcripts of conversations were created
  7. 7. Procedure • Transcripts were separated into 2 electronic text files; each file contained only one dyad member’s portion of the conversation • These files were then transferred into the LSA program
  8. 8. Results Significant Correlations of LSS with Dyad-Level Behavioral & Post- Interaction Measures Total number of conversation sequences 0.46** Total number of speaking turns 0.45** Total duration of speaking turns 0.60*** Total number of directed gazes 0.35* Total number of mutual gazes 0.47** Total number of nonverbal acknowledgements (head nods) 0.43** Note: * = p < .025; ** = p < .01; *** = p < .001
  9. 9. Results Unique Significant Correlations of LSS with Dyad-Level Behavioral and Post- Interaction Measures Total word count 0.61* Total number of questions asked 0.43* Number of times dyad members talked about others -0.42* Total percentage of positive entries 0.38* Total frequency of positive affect 0.47* Total frequency of expressive gestures 0.58** Total duration of expressive gestures 0.44* How smooth did you think the interaction was for your partner? 0.36* How awkward did you think the interaction was for your partner? -0.38* How much did you like your partner? 0.35* Note: * = p < .025; ** = p < .001
  10. 10. Mediation Analysis • Factor analysis revealed 4 factors:  1- Looking and acknowledging  2- Gesturing while talking  3- Using questions to advance/sustain conversation  4- Smiling and laughing Factors 1-4 % of repeated words & overall word count LSS + + +
  11. 11. Conclusion • LSS develops from of a highly involving interaction in which a lot of verbal information and nonverbal cues are exchanged by both dyad members • % of repeated words and total word count as mediator between behaviors and LSS

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