School of Social Sciences, Humanities, and Arts

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When developing ordinal rating scales, we may include potentially unordered response options such as "Neither Agree nor Disagree," "Neutral," "Don't Know," "No Opinion," or "Hard to Say." To handle responses to a mixture of ordered and unordered options, Huggins-Manley et al. (2018) proposed a class of semi-ordered models under the unidimensional item response theory framework. This study extends the concept of semi-ordered models into the area of diagnostic classification models. Specifically, we propose a flexible framework of semi-ordered DCMs that accommodates most earlier DCMs and allows for analyzing the relationship between those potentially unordered responses and the measured traits. Results from an operational study and two simulation studies show that the proposed framework can incorporate both ordered and non-ordered responses into the estimation of the latent traits and thus provide useful information about both the items and the respondents.

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The common sense model of illness self-regulation outlines the dynamic processes by which individuals perceive, interpret, and respond to health threats and illness-related information. An extended version of the model is proposed, which specifies additional constructs and processes to explain how lay perceptions of health threats impact coping responses and health-related outcomes. The extended model provides detail on: (a) the mediating process by which individuals' illness representations relate to illness outcomes through adoption of coping procedures; (b) how illness representations are activated by presentation of health-threatening stimuli; (c) behavioral and treatment beliefs as determinants of coping procedures and illness outcomes alongside illness representations; and (d) effects of moderators of relations between cognitive representations, coping procedures, and illness outcomes. The extended model sets an agenda for future research that addresses knowledge gaps regarding how individuals represent and cope with health threats, and may inform effective illness-management interventions. We identify the kinds of research required to provide robust evidence for the extended model propositions. We call for research that employs incipient illness samples, utilizes designs that capture dynamic processes in the model such as cross-lagged panel and intervention designs, and adopts illness-specific measures of coping procedures rather than relying on generic instruments.

Infants use statistical information in their environment, as well as others' emotional communication, to understand the intentions of social partners. However, rarely do researchers consider these two sources of social information in tandem. This study assessed 2-year-olds' attributions of intentionality from non-random sampling events and subsequent discrete emotion reactions. Infants observed an experimenter remove five objects from either the non-random minority (18%) or random majority (82%) of a sample and express either joy, disgust, or sadness after each selection. Two-year-olds inferred the experimenter's intentionality by giving her the object that she had previously selected when she expressed joy or disgust after non-random sampling events, but not when she expressed sadness or sampled at random. These findings demonstrate that infants use both statistical regularities and discrete emotion communication to infer an agent's intentions. In particular, the present findings show that 2-year-olds infer that an agent can intentionally select a preferred or an undesired object from a sample as a function of the discrete emotion. Implications for the development of inferring intentionality from statistical sampling events and discrete emotion communication are discussed.

Marijuana use among pregnant and breastfeeding women is on the rise and carries risks for infant health and well-being. Decisions to use marijuana while pregnant and breastfeeding are motivated by beliefs that use poses minimal risk to infants and offers benefits to maternal users. Misperceptions and usage trend higher among disadvantaged populations. This study surveyed 401 community residents on beliefs about risks and benefits of marijuana use by pregnant and breastfeeding women. The study utilized techniques to enhance recruitment of Latino and disadvantaged residents of rural communities in California, a state where recreational marijuana use is legal. Analyses revealed substantial endorsement of beliefs about benefits and low risks of marijuana use while pregnant and breastfeeding, many of which run counter to current evidence. Misperceptions were particularly prevalent for cannabis users and male respondents. Trends in valid beliefs, while modest, were higher for Latinos and parents.

Achieving broad immunity through vaccination is a cornerstone strategy for long-term management of COVID-19 infections, particularly the prevention of serious cases and hospitalizations. Evidence that vaccine-induced immunity wanes over time points to the need for COVID-19 booster vaccines, and maximum compliance is required to maintain population-level immunity. Little is known of the correlates of intentions to receive booster vaccines among previously vaccinated individuals. The present study applied an integrated model to examine effects of beliefs from multiple social cognition theories alongside sets of generalized, stable beliefs on individuals' booster vaccine intentions. US residents (N = 479) recruited from an online survey panel completed measures of social cognition constructs (attitude, subjective norms, perceived behavioral control, and risk perceptions), generalized beliefs (vaccine hesitancy, political orientation, and free will beliefs), and COVID-19 vaccine intentions. Social cognition constructs were related to booster vaccine intentions, with attitude and subjective norms exhibiting the largest effects. Effects of vaccine hesitancy, political orientation, and free will beliefs on intentions were mediated by the social cognition constructs, and only vaccine hesitancy had a small residual effect on intentions. Findings provide preliminary evidence that contributes to an evidence base of potential targets for intervention messages aimed at promoting booster vaccine intentions.

The COVID-19 pandemic has had unprecedented health, economic, and social consequences worldwide. Although contact reductions and wearing face coverings have reduced infection rates, and vaccines have reduced illness severity, emergence of new variants of the coronavirus that causes COVID-19, and the shift from pandemic to endemic patterns of infection, highlights the importance of ongoing preventive behavior adherence to manage future outbreaks. Research applying social cognition theories may assist in explaining variance in these behaviors and inform the development of efficacious behavior change interventions to promote adherence. In the present article, we summarize research applying these theories to identify modifiable determinants of COVID-19 preventive behaviors and the mechanisms involved, and their utility in informing interventions. We identify limitations of these applications (e.g., overreliance on correlational data, lack of long-term behavioral follow-up), and suggest how they can be addressed. We demonstrate the virtue of augmenting theories with additional constructs (e.g., moral norms, anticipated regret) and processes (e.g., multiple action phases, automatic processes) to provide comprehensive, parsimonious behavioral explanations. We also outline how the theories contribute to testing mechanisms of action of behavioral interventions. Finally, we recommend future studies applying these theories to inform and test interventions to promote COVID-19 preventive behavior adherence.

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

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