Automatic Modeling of Dominance Effects Using Granger Causality (original) (raw)
Causal Reasoning for Group Task Performance
Psychological Reports, 1986
Individuals differing in achievement motivation read a case description of a group sales project while assuming the role of the project's manager. Subjects read one of four versions of the case in which the project outcome and the manager's reliance upon the contributions of coworkers were varied. Subjects then evaluated the extent to which the superv~sor's effort, ability, luck, task difficulty, and co-workers contributed to the project outcome. Self-serving attributional biases were not fully evidenced. Differences among achievement groups emerged only on ascriptions to coworkers and only when considering the project outcome and the manager's reliance upon subordinates.
An important focus for technical research related to machine learning has been to address not only generalization across sub<ommunity structures within a hierarchical dataset, but also accommodating changes over time in a longitudinal dataset using evolving behavior models. We have two prototype models built and worning and are extending that worl< to make it more scalable to larger datasets. We completed a highly scalable model, being able to be applied to networks with millions of users. We validated the model on 3 different data sets from Massive Open Online Courses and found that the subcommunity slructure identified by the algorithm was predictive of differences in dropout rate between subsets of students. 15. SUBJECT TERMS 1&. SECURITY CLASSIFICATION OF: 17. LIMITATION OF o. REPORT b. ABSTRACT C. lliiS PAGE ABSTRACT 18. NuMBER OF PAGES 25 19a. NAME OF RESPONSIBLE PERSON Carolyn Penstein Rose 19b. TELEP/iONE NUMBER (lno/uco areo oo<lo) 412.268.7130 Standard Fonn 298 (Rov. 8198) P~sc1ibed by ANSI Sttl. Z3$. 18
Verbal behavior of the more and the less influential meeting participant
Proceedings of the 2007 workshop on Tagging, mining and retrieval of human related activity information - TMR '07, 2007
We test the strength of the relationship between the way that people behave in a discussion and their level of influence on the basis of some empirical grounds. We use the data sources that were collected from the AMI corpus for the experiments in the areas of argumentation, dialogueact and influence research. Statistical dependencies and (cor)relations between the tags are mined for possible relationships.
Automatic dominance detection in dyadic conversations
Escritos de Psicología - Psychological Writings
Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers’ opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences.
Testing different methodologies for Granger causality estimation: A simulation study
2020 28th European Signal Processing Conference (EUSIPCO), 2021
Granger causality (GC) is a method for determining whether and how two time series exert causal influences one over the other. As it is easy to implement through vector autoregressive (VAR) models and can be generalized to the multivariate case, GC has spread in many different areas of research such as neuroscience and network physiology. In its basic formulation, the computation of GC involves two different regressions, taking respectively into account the whole past history of the investigated multivariate time series (full model) and the past of all time series except the putatively causal time series (restricted model). However, the restricted model cannot be represented through a finite order VAR process and, when few data samples are available or the number of time series is very high, the estimation of GC exhibits a strong reduction in accuracy. To mitigate these problems, improved estimation strategies have been recently implemented, including state space (SS) models and partial conditioning (PC) approaches. In this work, we propose a new method to compute GC which combines SS and PC and tests it together with other four commonly used estimation approaches. In simulated networks of linearly interacting time series, we show the possibility to reconstruct the network structure even in challenging conditions of data samples available.
Who had the upper hand? ranking participants of interactions based on their relative power
In this paper, we present an automatic system to rank participants of an interaction in terms of their relative power. We find several linguistic and structural features to be effective in predicting these rankings. We conduct our study in the domain of political debates, specifically the 2012 Republican presidential primary debates. Our dataset includes textual transcripts of 20 debates with 4-9 candidates as participants per debate. We model the power index of each candidate in terms of their relative poll standings in the state and national polls. We find that the candidates' power indices affect the way they interact with others and the way others interact with them. We obtained encouraging results in our experiments and we expect these findings to carry across to other genres of multi-party conversations.
A Multimodal Corpus for Studying Dominance in Small Group Conversations
We present a new multimodal corpus with dominance annotations on small group conversations. We used five-minute non-overlapping slices from a subset of meetings selected from the popular Augmented Multi-party Interaction (AMI) corpus. The total length of the annotated corpus corresponds to 10 hours of meeting data. Each meeting is observed and assessed by three annotators according to their level of perceived dominance. We analyzed the annotations with respect to dominance, status, gender and behavior. The results of the analysis reflect the findings in the social psychology literature on dominance. The described dataset provides an appropriate testbed for automatic dominance analysis.
Identifying triggers of target actions within individual or social processes requires modeling individual (and group) characteristics and sequences of actions. We explicate one such method, statistical discourse analysis (SDA). SDA can model (a) pivotal actions that radically change subsequent processes, (b) effects of previous actions (or their sequences) on target actions, and (c) influences at various levels (turn, time period, individual, group, organization, etc.). SDA addresses difficulties involving data (unit of analysis, coding, interrater reliability, missing data, parallel conversations, breakpoints, time periods, statistical power), dependent variables (discrete variables, infrequency bias, nested data, multiple dependent variables), and explanatory variables (variables at earlier turns, cross-level interactions, indirect multilevel mediation, serial correlation, false positives, odds ratios, robustness). To illustrate the benefits of SDA, we test how social metacognitive actions (e.g., agree, rudely disagree) affect the likelihood of correct, new ideas (microcreativity) and justifications using 3,296 turns of talk by 80 students in 20 groups working on an algebra problem. A rude disagreement often triggered another rude disagreement, which yielded less microcreativity. After a wrong idea or in groups that solved the problem however, a rude disagreement yielded greater microcreativity. After a student with a higher mathematics grade spoke, more justifications followed; this effect differed across time periods. We also discuss limitations of SDA, which include a linear combination of explanatory variables, independent and identically distributed residuals, and a minimum sample size (20 units at the highest level).
Expertise and confidence explain how social influence evolves along intellective tasks
2020
Discovering the antecedents of individuals' influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We observe that along an issue sequence with feedback, individuals with higher expertise and social confidence are accorded higher interpersonal influence. We also observe that low-performing individuals tend to underestimate their high-performing teammate's expertise. Based on these observations, we introduce three hypotheses and present empirical and theoretical support for their validity. We report empirical evidence on longstanding theories of transactive memory systems, social comparison, and confidence heuristics on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time....
Predicting Results from Interaction Patterns During Online Group Work
Lecture Notes in Computer Science, 2015
Group work is an essential activity during both graduate and undergraduate formation. Although there is a vast theoretical literature and numerous case studies about group work, we haven't yet seen much development concerning the assessment of individual group participants. The problem relies on the difficulty to have the perception of each student's contribution towards the whole work. We propose and describe a novel tool to manage and assess individual group. Using the collected interactions from the tool usage we create a model for predicting ill-conditioned interactions which generate alerts. We also describe a functionality to predict the final activity grading, based on the interaction patterns and on an automatic classification of these interactions.