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      Computationally modeling interpersonal trust

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          Abstract

          We present a computational model capable of predicting—above human accuracy—the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind's readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naiveté of this domain knowledge. We then present the construction of hidden Markov models to investigate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust.

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          Most cited references 23

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          10.1162/153244303322753616

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            Fast-Find: A novel computational approach to analyzing combinatorial motifs

            Background Many vital biological processes, including transcription and splicing, require a combination of short, degenerate sequence patterns, or motifs, adjacent to defined sequence features. Although these motifs occur frequently by chance, they only have biological meaning within a specific context. Identifying transcripts that contain meaningful combinations of patterns is thus an important problem, which existing tools address poorly. Results Here we present a new approach, Fast-FIND (Fast-Fully Indexed Nucleotide Database), that uses a relational database to support rapid indexed searches for arbitrary combinations of patterns defined either by sequence or composition. Fast-FIND is easy to implement, takes less than a second to search the entire Drosophila genome sequence for arbitrary patterns adjacent to sites of alternative polyadenylation, and is sufficiently fast to allow sensitivity analysis on the patterns. We have applied this approach to identify transcripts that contain combinations of sequence motifs for RNA-binding proteins that may regulate alternative polyadenylation. Conclusion Fast-FIND provides an efficient way to identify transcripts that are potentially regulated via alternative polyadenylation. We have used it to generate hypotheses about interactions between specific polyadenylation factors, which we will test experimentally.
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              Interactive Robots as Social Partners and Peer Tutors for Children: A Field Trial

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                Author and article information

                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                04 December 2013
                2013
                : 4
                Affiliations
                1Media Lab, Massachusetts Institute of Technology Cambridge, MA, USA
                2Department of Psychology, Northeastern University Boston, MA, USA
                Author notes

                Edited by: Serge Thill, University of Skövde, Sweden

                Reviewed by: Shane Mueller, Michigan Technological University, USA; Geoffrey C.-Y. Tan, National Healthcare Group, Singapore

                *Correspondence: Jin Joo Lee, Media Lab, Massachusetts Institute of Technology, 20 Ames st., E15-468 Cambridge, MA 02142, USA e-mail: jinjoo@ 123456media.mit.edu

                This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology.

                Article
                10.3389/fpsyg.2013.00893
                3850257
                24363649
                Copyright © 2013 Lee, Knox, Wormwood, Breazeal and DeSteno.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 10, Tables: 5, Equations: 5, References: 31, Pages: 14, Words: 10990
                Categories
                Psychology
                Original Research Article

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