Exploring the Generalization Process from Past Behavior to Predicting Future Behavior

Morality or competence is more important? The effect of evaluation dimensions on ERP responses to neutral faces depends on contextual valence and self-relevance

Recent studies have shown that the processing of neutral facial expressions could be modulated by the valence and self-relevance of preceding verbal evaluations. However, these studies have not distinguished the dimension (i.e., morality and competence) from verbal evaluations. In fact, there is a hot controversy about whether morality or competence receives more weight. Therefore, using the ERP technique, the current study aimed to address this issue by comparing the influence of morality and competence evaluations on behavioral and neural responses to neutral facial expressions when these evaluations varied with contextual valence and self-relevance. Our ERP results revealed that the early EPN amplitudes were larger for neutral faces after receiving evaluations about self relative to evaluations about senders. Moreover, the EPN was more negative after a competence evaluation relative to a morality evaluation when these evaluations were positive, while this effect was absent when these evaluations were negative. The late LPP was larger after a morality evaluation compared to a competence evaluation when these evaluations were negative and directed to self. However, no significant LPP effect between morality and competence evaluations was observed when these evaluations were positive. The present study extended previous studies by showing that early and late processing stages of faces are affected by the evaluation dimension in a top-down manner and further modulated by contextual valence and self-relevance.

Neuropsychological and Symptom Predictors of Diagnostic Persistence in ADHD: A 25-Year Follow-up Study

BACKGROUND

This study extends long-term predictive research on ADHD by including both neuropsychological and symptom measures at baseline in adolescence as predictors of diagnostic persistence 25 years later.

METHODS

Nineteen males with ADHD and 26 healthy controls (HC; M/F = 13/13), were assessed in adolescence and 25 years later. Measurements at baseline included a comprehensive test battery measuring eight neuropsychological domains, an IQ estimate, the Child Behavior Checklist (CBCL), and the Global Assessment Scale of Symptoms. Differences between ADHD Retainers, Remitters, and HC were calculated with ANOVAs, and potential predictions of differences in the ADHD group by linear regression analyses.

RESULTS

Eleven (58%) participants retained their ADHD diagnoses at follow-up. Motor Coordination and Visual perception at baseline predicted diagnosis at follow-up. CBCL Attention problems at baseline in the ADHD group predicted variance in diagnostic status.

CONCLUSION

Lower-order neuropsychological functions related to motor function and perception are important long-time predictors of persistence of ADHD.

Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models

Generally, treatment default of diseases by patients is regarded as the biggest threat to favourable disease treatment outcomes. It is seen as the reason for the resurgence of infectious diseases including tuberculosis in some developing countries. Sadly, its occurrence in chronic disease management is associated with high morbidity and mortality rates. Many reasons have been adduced for this phenomenon. Exploration of treatment default using biographic and behavioral metrics collected from patients and healthcare providers remains a challenge. The focus on contextual nonbiomedical measurements using a supervised machine learning modeling technique is aimed at creating an understanding of the reasons why treatment default occurs, including identifying important contextual parameters that contribute to treatment default. The predicted accuracy scores of four supervised machine learning algorithms, namely, gradient boosting, logistic regression, random forest, and support vector machine were 0.87, 0.90, 0.81, and 0.77, respectively. Additionally, performance indicators such as the positive predicted value score for the four models ranged between 98.72%–98.87%, and the negative predicted values of gradient boosting, logistic regression, random forest, and support vector machine were 50%, 75%, 22.22%, and 50%, respectively. Logistic regression appears to have the highest negative-predicted value score of 75%, with the smallest error margin of 25% and the highest accuracy score of 0.90, and the random forest had the lowest negative predicted value score of 22.22%, registering the highest error margin of 77.78%. By performing a chi-square correlation statistic test of variable independence, this study suggests that age, presence of comorbidities, concern for long queuing/waiting time at treatment facilities, availability of qualified clinicians, and the patient’s nutritional state whether on a controlled diet or not are likely to affect their adherence to disease treatment and could result in an increased risk of default.

The power of online panel paradata to predict unit nonresponse and voluntary attrition in a longitudinal design

The objective of this study is to identify factors affecting participation rates, i.e., nonresponse and voluntary attrition rates, and their predictive power in a probability-based online panel. Participation for this panel had already been investigated in the literature according to the socio-demographic and socio-psychological characteristics of respondents and different types of paradata, such as device type or questionnaire navigation, had also been explored. In this study, the predictive power of online panel participation paradata was instead evaluated, which was expected (at least in theory) to offer even more complex insight into respondents' behavior over time. This kind of paradata would also enable the derivation of longitudinal variables measuring respondents' panel activity, such as survey outcome rates and consecutive waves with a particular survey outcome prior to a wave (e.g., response, noncontact, refusal), and could also be used in models controlling for unobserved heterogeneity. Using the Life in Australia™ participation data for all recruited members for the first 30 waves, multiple linear, binary logistic and panel random-effect logit regression analyses were carried out to assess socio-demographic and online panel paradata predictors of nonresponse and attrition that were available and contributed to the accuracy of prediction and the best statistical modeling. The proposed approach with the derived paradata predictors and random-effect logistic regression proved to be reasonably accurate for predicting nonresponse-with just 15 waves of online panel paradata (even without sociodemographics) and logit random-effect modeling almost four out of five nonrespondents could be correctly identified in the subsequent wave.

Unstandard Deviation: The Untapped Value of Positive Deviance for Reducing Inequalities

Behavioral science is increasingly used in public policy to understand and address various manifestations of inequalities. Yet evidence from effective population-level interventions is limited. One framework, known as positive deviance, emphasizes individuals from disadvantaged circumstances who have significantly better outcomes than are typical for their group. Studying their behaviors and outcomes helps to understand what might explain their overall success. These insights could also be used to help others from these circumstances experience positive outcomes. Because positive deviance has been markedly understudied, we present a framework for doing so specifically within behavioral science for public policies aimed at reducing inequalities. Using examples from real-world and experimental insights on choices and outcomes of positive deviants, we encourage further study of their choices and trajectories over time to produce valuable insights. We propose that leveraging those findings would inform public policy by introducing interventions that are more ecologically sound and population-relevant and thus have a better chance at benefiting those who start off under adverse circumstances.

Exploring how harming and helping behaviors drive prediction and explanation during anthropomorphism

Cacioppo and colleagues advanced the study of anthropomorphism by positing three motives that moderated the occurrence of this phenomenon; belonging, effectance, and explanation. Here, we further this literature by exploring the extent to which the valence of a target's behavior influences its anthropomorphism when perceivers attempt to explain and predict that target's behavior, and the involvement of brain regions associated with explanation and prediction in such anthropomorphism. Participants viewed videos of varying visually complex agents - geometric shapes, computer generated (CG) faces, and greebles - in nonrandom motion performing harming and helping behaviors. Across two studies, participants reported a narrative that explained the observed behavior (both studies) while we recorded brain activity (study one), and participants predicted future behavior of the protagonist shapes (study two). Brain regions implicated in prediction error (striatum), not language generation (inferior frontal gyrus; IFG) engaged more to harming than helping behaviors during the anthropomorphism of such stimuli. Behaviorally, we found greater anthropomorphism in explanations of harming rather than helping behaviors, but the opposite pattern when participants predicted the agents' behavior. Together, these studies build upon the anthropomorphism literature by exploring how the valence of behavior drives explanation and prediction.

Right-wing authoritarianism and stereotype-driven expectations interact in shaping intergroup trust in one-shot vs multiple-round social interactions

Trust towards unrelated individuals is often conditioned by information about previous social interactions that can be derived from either personal or vicarious experience (e.g., reputation). Intergroup stereotypes can be operationalized as expectations about other groups' traits/attitudes/behaviors that heavily influence our behavioral predictions when interacting with them. In this study we investigated the role of perceived social dimensions of the Stereotype Content Model (SCM)-Warmth (W) and Competence (C)-in affecting trusting behavior towards different European national group members during the Trust Game. Given the well-known role of ideological attitudes in regulating stereotypes, we also measured individual differences in right-wing authoritarianism (RWA). In Experiment 1, we designed an online survey to study one-shot intergroup trust decisions by employing putative members of the European Union states which were also rated along SCM dimensions. We found that low-RWA participants' trusting behavior was driven by perceived warmth (i.e., the dimension signaling the benevolence of social intentions) when interacting with low-C groups. In Experiment 2, we investigated the dynamics of trust in a multiple-round version of the European Trust Game. We found that in low-RWA participants trusting behavior decreased over time when interacting with high-W groups (i.e., expected to reciprocate trust), but did not change when interacting with low-W groups (i.e., expected not to reciprocate trust). Moreover, we found that high-RWA participants' trusting behavior decreased when facing low-W groups but not high-W ones. This suggests that low-RWA individuals employ reputational priors but are also permeable to external evidence when learning about others' trustworthiness. In contrast, high-RWA individuals kept relying on stereotypes despite contextual information. These results confirm the pivotal role played by reputational priors triggered by perceived warmth in shaping social interactions.