Jonathan H. Morgan

A preliminary model of participation for small groups

We present a small-group model that moderates agent behavior using several factors to illustrate the influence of social reflexivity on individual behavior. To motivate this work, we review a validated simulation of the Battle of Medenine. Individuals in the battle performed with greater variance than the simulation predicted, suggesting that individual differences are important. Using a light-weight simulation, we implement one means of representing these differences inspired in part by Grossman’s (On Killing: The Psychological Cost of Learning to Kill in War and Society. Little, Brown and Company, New York, 1995) participation formula. This work contributes to a general theory of social reflexivity by offering a theory of participation as a social phenomenon, independent of explicit agent knowledge. We demonstrate that our preliminary version of the participation model generates individual differences that in turn have a meaningful impact on group performance. Specifically, our results suggest that a group member’s location with respect to other group members and observers can be an important exogenous source of individual differences.

Identification of plaque isolates of an avian retrovirus causing rapid and slow onset osteopetrosis

This study examines the basis for the observation that serum stocks of myeloblastosis-associated virus MAV-2(0) obtained from chickens with osteopetrosis induced abnormal bone growth after a latent period of less than 3 weeks, while stocks of plaque-purified MAV-2(0) induced abnormal bone growth only after latent periods of 6 weeks or greater. To establish the basis for this distinction, 85 plaque isolates were derived from a serum stock of MAV-2(0), and each plaque isolate was injected into 10-day-old chick embryos. Infected chicks were examined 3 weeks after hatch for the weight of their body, bursa, and bone. Three MAV-2(0) plaque isolates caused bone growth that was at least fivefold greater in infected than in uninfected animals. The other 82 plaque isolates caused slight osteopetrosis or none at all. The serum stock of MAV-2(0) therefore consisted of two forms of virus: one caused rapid onset osteopetrosis, and the other caused slow onset osteopetrosis. Almost all plaque isolates of MAV-2(0) caused decreased body and bursa weights, but with the rapid onset plaque isolates, these effects were more severe. The ability to cause rapid onset osteopetrosis was stable for three sequential plaque purifications. RNA from rapid and slow MAV-2(0) plaque isolates did not differ in T1 oligonucleotide fingerprints, and all had RNA 8.2 Kb in length. Structural polypeptide compositions of rapid and slow plaque isolates were essentially the same, as defined by migration on SDS-polyacrylamide gels. The growth rate of the isolates on embryonic limb bud cells and chick embryo fibroblasts were similar.

Network sampling coverage II: The effect of non-random missing data on network measurement

Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.

Running behavioral studies with human participants : a practical guide

1. Introduction 2. Preparation For Running Experiments 3. Potential Ethical Problems 4. Risks to Validity to Avoid While Running an Experiment 5. Running a Research Session 6. Concluding a Study Appendix 1: A Checklist for Preparing Studies Appendix 2: Example Scripts for Running Studies Appendix 3: Example Consent Form Appendix 4: Example Debriefing Form Appendix 5: Example Institutional Review Board Application Appendix 6: Considerations When Running a Study Online

A Design, Tests, and Considerations for Improving Keystroke and Mouse Loggers

We start by reviewing several logging tools. We then report improvements to a keystroke logger we have developed for the Mac and PC, Recording User Input (RUI). These improvements include changes to its interface, increased accuracy, and extensions to its logging ability. RUI runs in the background recording user behavior with timestamps and mouse location data across all applications—thus avoiding problems associated with video logs and instrumenting individual applications. We provide a summary and comparison of tests for loggers and and present procedures for validating logger timing that quantifies timing accuracy using an external clock. We demonstrate these tests on RUI and three other applications (Morae, Camtasia, and AppMonitor). We conclude by providing some general specifications and considerations for creating, testing, evaluating, and using keystroke and mouse loggers with respect to different experimental questions and tasks.

Distinguishing Normative Processes From Noise A Comparison of Four Approaches to Modeling Impressions of Social Events

This research evaluates the relative merits of two established and two newly proposed methods for modeling impressions of social events: stepwise regression, ANOVA, Bayesian model averaging, and Bayesian model sampling. Models generated with each method are compared against a ground truth model to assess performance at variable selection and coefficient estimation. We also assess the theoretical impacts of different modeling choices. Results show that the ANOVA procedure has a significantly lower false discovery rate than stepwise regression, whereas Bayesian methods exhibit higher true positive rates and comparable false discovery rates to ANOVA. Bayesian methods also generate coefficient estimates with less bias and variance than either stepwise regression or ANOVA. We recommend the use of Bayesian methods for model specification in affect control theory.

Building social networks out of cognitive blocks: factors of interest in agent-based socio-cognitive simulations

This paper examines how cognitive and environmental factors influence the formation of dyadic ties. We use agent models instantiated in ACT-R that interact in a social simulation, to illustrate the effect of memory constraints on networks. We also show that environmental factors are important including population size, running time, and map configuration. To examine these relationships, we ran simulations of networks using a factorial design. Our analyses suggest three interesting conclusions: first, the tie formation of these networks approximates a logistic growth model; second, that agent memory quality (i.e., perfect or human-like) strongly alters the network’s density and structure; third, that the three environmental factors all influence both network density and some aspects of network structure. These findings suggest that meaningful variance of social network analysis measures occur in a narrow band of memory strength (the cognitive band); the threshold for defining tie criteria is important; and future simulations examining generative social networks should control and carefully report these environmental and cognitive factors.

Practical aspects of running experiments with human participants

There can often be a gap between theory and its implications for practice in human-behavioral studies. This gap can be particularly significant outside of psychology departments. Most students at the undergraduate or early graduate levels are taught how to design experiments and analyze data in courses related to statistics. Unfortunately, there is a dearth of materials providing practical guidance for running experiments. In this paper, we provide a summary of a practical guide for running experiments involving human participants. The full report should improve practical methodology to run a study with diverse topics in the thematic area of universal access in humancomputer interaction.

Grounding Social Interaction with Affective Intelligence

Symbolic interactionist principles of sociology are based on the idea that human action is guided by culturally shared symbolic representations of identities, behaviours, situations and emotions. Shared linguistic, paralinguistic, or kinesic elements allow humans to coordinate action by enacting identities in social situations. Structures of identity-based interactions can lead to the enactment of social orders that solve social dilemmas e.g., by promoting cooperation. Our goal is to build an artificial agent that mimics the identity-based interactions of humans. This paper describes a study in which humans played a repeated prisoners dilemma game against other humans or one of three artificial agents bots. One of the bots has an explicit representation of identity and demonstrates more human-like behaviour than the other bots.

Rapid induction of osteopetrosis by subgroup e recombinant viruses

Avian osteopetrosis is a proliferative bone disorder initiated at high frequency by MAV-2(O), a subgroup B avian myeloblastosis-associated virus. To examine the role of the MAV-2(O) genome in osteopetrosis induction, a series of recombinant viruses between MAV-2(O) and RAV-O was constructed. Recombinant viruses were selected for rapid growth and subgroup E envelopes. The T1 oligonucleotide fingerprint patterns of viruses selected in this manner demonstrated that they were recombinants and were clonally pure because they had oligonucleotides from each parent, and each oligonucleotide was present in single molar yield. When injected into 10-day-old chicken embryos, approximately 50% of the recombinant viruses induced osteopetrosis within 3 weeks after hatch. Therefore, subgroup E envelope did not inhibit osteopetrosis induction. The osteopetrosis that was induced varied from slight to severe, but none of the recombinant viruses induced osteopetrosis as severe as the MAV-2(O) parent.