The document discusses a study that used computer algorithms to analyze conversational dynamics within the first 5 minutes of simulated employment negotiations. The study aimed to predict negotiation outcomes based on measures of activity, engagement, emphasis, and mirroring. It was found that an individual's activity level and emphasis were positively correlated with their own outcome, while emphasis was also positively correlated with the counterpart's outcome. This provides evidence that conversational dynamics analyzed through computer algorithms can predict behavioral outcomes, offering potential applications for training and evaluation. However, the study also notes limitations, such as the link between speech features and dynamics being tenuous for complex features.

1. Predicting Outcomes From Conversational
Dynamics Within the First 5 minutes
Thin Slices
Curhan & Pentland

2. Power of First Impressions
Thin Slices
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First impression based on 30sec and 6sec of silent video
Interviewer’s impressions are formed in the early stages of the
interview and tend to persist throughout the interaction

4. Power of First Impressions
Thin Slices
Evidence that conversational dynamics might play a critical
role in negotiation
Formal Microanalyses of highly specific speech features
Predictive validity of computers
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Degree to which 4 conventional dynamics occurring within
the 1st
5min of a two-party, simulated employment
negotiation predict the outcomes of that negotiation
Behavioral outcome of a Transactional Negotiation

5.  Have been shown to predict a broad
range of consequences
 Key to success lies in understanding
social signaling, often nonverbal in
nature
Thin Slices of behavioral data
Negotiation context, Pentland
hopes to provide a useful
diagnostic instrument that might
facilitate future research on
negotiation processes as well as
applications for training and
evaluating negotiators.
Computer Algorithms- Thin
Slice
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6. Nonlinguistic social signals, body language,
facial expressions, & tone of voice are as
important as linguistic content in predicting
behavioral outcomes
Which social signals participants might
use to judge outcomes?
Which social signals participants might
use to judge outcomes?
That have already been suggested in
the literature with behavioral
outcomes, to determine which signals
(if any) have predictive power similar
to that of human judges.
Compare candidate signal
features
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How these predictive social signals
relate to existing theories of mental
function and social interaction.
Examine
Social Signaling and
Conversational
Dynamics
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Curhan & Pentland
Research

7. An individual’s activity level
during the first 5 min of the
negotiation will be positively
correlated with his or her own
individual outcome.
Hypothesis
Time a person speak. Studies’
involving competitive settings,
speaking time was positively
correlated with dominance over
the outcome
Activity
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8. An individual’s level of
engagement during the first 5
min of the negotiation will be
positively correlated with his or
her own individual outcome.
Hypothesis
Measured by the influence that
one person has on the other’s
conversational turn taking.
(Markov process).
One sided engagement occurs
when one person is energetically
questioning another and the
other begins speaking only after
the questioner ceases speaking.
Engagement
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9. An individual’s level of emphasis
during the first 5 min of the
negotiation will be positively
correlated with his or her own
individual outcome but positively
correlated with the counterpart’s
individual outcome.
Hypothesis
Measured by variation in speech
prosody, specifically, variations
in pitch and volume.
Pentland & Curhan emphasis
measure may be an indication of
the importance a speaker
attaches to the interaction.
Emphasis
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10. Hypothesis: an individual’s freq.
of mirroring during the first 5 min
of the negotiation will be
positively correlated with his or
her own individual outcome.
Hypothesis
Behavior of one individual is
mimicked or mirrored by another,
this could signal empathy, which
has shown to positively influence
the smoothness of an interaction
as well as mutual liking.
Mirroring
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11. Past studies relied on human
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12. Technology can be used to give real time feedback to diagnose and improve individual
negotiation skills
Microcoding using a computer it may ensure high test-retest reliability. Because computer measures objective,
physical property of the audio signal, not subjective or psychological properties of the measures are 100%
consistent.
Present study uses computer algorithms to identified specific features of thin slices that
correlated with subsequent behavioral outcomes
Pros
Conclusion
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Cons
No real evidence to prove the success of technology based on the algorithms used in this research is a
better predictor of thin slice outcome then human judge predictors
No real evidence to prove the success of technology based on the algorithms used in this research is a
better predictor of thin slice outcome then human judge predictors
Research increase the original concept of Thin Slice 30sec and 6sec to 5min first impression to predict
outcome
Research increase the original concept of Thin Slice 30sec and 6sec to 5min first impression to predict
outcome
For more complicated speech features, the link between specific measures and their respective
conversational dynamics is more tenuous
For more complicated speech features, the link between specific measures and their respective
conversational dynamics is more tenuous
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Notwithstanding this potential shortcoming, one advantage of microcoding using a computer is that it
ensures high test-retest reliability.
Notwithstanding this potential shortcoming, one advantage of microcoding using a computer is that it
ensures high test-retest reliability.
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13. Going beyond
Mining Social Networks
Wearable Networks
The belief that by adding mathematical
understanding of behavior based on the feature
analysis, will enhance distance-separated team work
behavior, collaboration, decision-making and the
sharing of social experiences.
GRS
Group Network
Social
Intelligence

14. THANK YOU!
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