Using generalized additive (mixed) models to analyze single case designs

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R.     

Face‐Off: Facial Features and Strategic Choice

I study experimentally a single‐shot trust game where players have the opportunity to choose an avatar—a computer‐generated face—to represent them. These avatars vary on several dimensions—trustworthiness, dominance, and threat—identified by previous work as influencing perceptions of those who view the faces (Todorov, Said, Engell, & Oosterhof, 2008). I take this previous work and ask whether subjects choose faces that are ex ante more trustworthy, whether selected avatars have an influence on strategy choices, and whether individuals who evaluate faces as more trustworthy are also more likely to trust others. Results indicate affirmative answers to all three questions. Additional experimental sessions used randomly assigned avatars. This design allows me to compare behavior when everyone knows avatars are self‐selected versus when everyone knows they are randomly assigned. Random assignment eliminated all three effects observed when subjects chose their avatars.     

计算模型视角下信任形成的心理和神经机制——基于信任博弈中投资者的角度

人际信任渗透在社会交互的各个方面, 是促进和维持合作的重要基石。以往研究者借助信任博弈范式, 主要探讨了人际信任的理论模型、生物基础和影响因素等方面。近年来, 研究者开始将计算模型应用于信任博弈的数据分析中, 深入挖掘人际信任行为背后的心理机制, 将计算模型与神经影像技术结合, 加深对信任行为背后脑机制的理解。目前将计算模型应用于信任博弈范式中的研究主要针对“信任是如何形成的”这一科学问题, 未来要进一步发展计算模型方法, 结合非侵入性脑刺激技术, 应用于精神疾病人群中, 以深入理解正常和异常信任形成的心理和神经机制。     

Individualistic attitudes toward attractive rewards in older people: An experimental study using ultimatum games

This study used an ultimatum game to test the hypothesis that older individuals make more selfish offers when bargaining for highly attractive rewards, regardless of the impacts of others' responses on reward size. Both older and younger individuals separately participated in the experiment and made offers as allocators. The effects of four factors (age, certainty of the allocators' own reward depending on the recipient's response, the recipient's response, and type of rewards to be shared) on participants' offers and expectations of recipients' replies were observed. Older participants demanded higher shares for themselves than younger participants, even under the low certainty condition in the highly attractive reward scenario. Particularly in this scenario, older participants increased their portions immediately after receiving rejections. Moreover, they consistently expected the recipients to accept their offers regardless of the proposed distribution amount or bargaining conditions. It was implied that the high attractiveness of the rewards made it difficult for older individuals to predict the recipient's view of their proposals and thus contributed to them making selfish proposals.     

Within‐subject analysis of a prevention strategy for problem behavior

Although decades of research on functional analysis methodology have identified common contingencies that maintain problem behavior and effective interventions, relatively little research has been conducted on strategies to prevent the initial development of problem behavior. We conducted a 2‐part case study, the purposes of which were to illustrate the use of sensitivity tests as the bases for intervention (Study 1) and subsequently to assess the efficacy of a prevention strategy using a single‐subject design (Study 2). Results showed that the sensitivity tests identified establishing operations that may set the occasion for the development of problem behavior and that interventions based on differential reinforcement prevented increases in the severity of problem behavior relative to untreated and control baselines. Benefits and limitations to this individualized approach to prevention are discussed.     

Psychological and physiological stress in non-competitive and competitive esports settings: A systematic review

ObjectivesCompetition is related to psychophysiological stress responses and is a key aspect of esports. Yet, while competition may indeed have an impact on performance, little is known about competition-related psychophysical stress responses in esports. Accordingly, this review aims to investigate whether playing esports in competitive (and non-competitive) settings is related to psychological and/or physiological stress.DesignA systematic literature review was performed. Study inclusion criteria included: assessment of psychological and/or physiological stress in esports, study of healthy individuals over 12 years of age, and published in peer-reviewed journals in English or German.MethodThe literature search comprised EBSCOhost, PubMed, and Web of Science up until August 2019. An additional search was performed on Science Direct, Google Scholar, and Sponet.ResultsSeventeen studies met inclusion criteria. In publications investigating the non-competitive setting, it can be assumed that merely playing esports games is unrelated to psychophysiological stress reactions. While three out of five studies on stress in competitive settings demonstrated no hormonal reaction, one study reported an increase in anxiety levels in winners as well as an increase cortisol levels from baseline to postgame conditions, and two studies found an activation of the sympathetic nervous system. Overall, the studies revealed theoretical and methodological limitations, which are discussed in the present review.ConclusionsAlthough the existing literature contributes to an initial understanding of psychophysiological stress in esports, further in-depth studies of stress responses in actual competition are needed.     

Rivals without a cause? Relative performance feedback creates destructive competition despite aligned incentives

Whether people compete or cooperate with each other has consequences for their own performance and that of organizations. To explain why people compete or cooperate, previous research has focused on two main factors: situational outcome structures and personality types. Here, we propose that—above and beyond these two factors—situational cues, such as the format in which people receive feedback, strongly affect whether they act competitively, cooperatively, or individualistically. Results of a laboratory experiment support our theorizing: After receiving ranking feedback, both students and experienced managers treated group situations with cooperative outcome structures as competitive and were in consequence willing to forgo guaranteed financial gains to pursue a—financially irrelevant—better rank. Conversely, in dilemma situations, feedback based on the joint group outcome led to more cooperation than ranking feedback. Our study contributes to research on competition, cooperation, interdependence theory, forced ranking, and the design of information environments.     

Effects of perception on public bike-and-ride: A survey under complex,multifactor mode-choice scenarios

Bicycles are an environmentally-friendly, energy-saving mode of transportation. Public bike-and-ride is a term used to describe the use of public bicycles combined with transit as a means of transportation. A new stated preference (SP) design scheme based on the factors respondents felt were most important was proposed to augment the traditional SP design under complex multi-factor and multi-level situations. iPads were used to automatically administer the survey under each design scheme and collect the choice behavior data for public bike-and-ride. The concept of perceived psychological distance was further proposed and used to estimate Logit models. Comparative analysis of these models shows for the traditional SP design scheme, some factors, including perceived psychological distance, have reduced statistically significant effects on public bike-and-ride choice relative to the new SP design scheme. This indicates that in scenarios with complex decisions, the decision-makers would reduce their consideration of some factors and simplify their decision process making the new SP survey design scheme more suitable to analyze their choice behavior in those cases. Model analysis showed that perceived psychological distance for each traveler is the most critical factor during the decision process for travel behavior analysis. Finally, sensitivity analysis was conducted to determine which factors in the new survey design were important to increase the share of public bike-and-ride users. Practical findings for promoting public bike-and-ride use are presented in addition to theoretical references for improving the traditional survey methods.