Human Social Interaction Modeling Using Temporal Deep Networks
This work addresses the problem of understanding and modeling human social interactions for researchers in computational social science, though it is incremental as it builds on existing CRBM methods.
The paper tackles computational modeling of social interactions by detecting essential social interaction predicates (ESIPs) with a novel joint Discriminative Conditional Restricted Boltzmann Machine (DCRBM), achieving detection accuracies of 76% to 49% across various ESIPs and generating lower-level data with mean square errors of 0.01-0.1.
We present a novel approach to computational modeling of social interactions based on modeling of essential social interaction predicates (ESIPs) such as joint attention and entrainment. Based on sound social psychological theory and methodology, we collect a new "Tower Game" dataset consisting of audio-visual capture of dyadic interactions labeled with the ESIPs. We expect this dataset to provide a new avenue for research in computational social interaction modeling. We propose a novel joint Discriminative Conditional Restricted Boltzmann Machine (DCRBM) model that combines a discriminative component with the generative power of CRBMs. Such a combination enables us to uncover actionable constituents of the ESIPs in two steps. First, we train the DCRBM model on the labeled data and get accurate (76\%-49\% across various ESIPs) detection of the predicates. Second, we exploit the generative capability of DCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific ESIP that closely matches the actual training data (with mean square error 0.01-0.1 for generating 100 frames). We are thus able to decompose the ESIPs into their constituent actionable behaviors. Such a purely computational determination of how to establish an ESIP such as engagement is unprecedented.