A calculus of probability or belief? Neural underpinnings of social decision-making in a card game.

For decades, the prefrontal cortex (PFC) has been the focus of social neuroscience research, specifically regarding its role in competitive social decision-making. However, the distinct contributions of PFC subregions when making strategic decisions involving multiple types of information (social, non-social, and mixed information) remain unclear. This study investigates decision-making strategies (pure probability calculation vs. mentalizing) and their neural representations using functional near-infrared spectroscopy (fNIRS) data collected during a two-person card game. We observed individual differences in information processing strategy, indicating that some participants relied more on probability than others. Overall, the use of pure probability decreased over time in favor of other types of information (e.g., mixed information), with this effect being more pronounced within-round trials than across rounds. In the brain, (1) the lateral PFC activates when decisions are driven by probability calculations; (2) the right lateral PFC responds to trial difficulty; and (3) the anterior medial PFC is engaged when decision-making involves mentalizing. Furthermore, neural synchrony, which reflects the real-time interplay between individuals' cognitive processes, did not consistently contribute to correct decisions and fluctuated throughout the experiment, suggesting a hierarchical mentalizing mechanism at work.

Why scapegoating can ruin an apology: The mediated-moderation model of appropriate crisis response messages in the context of South Korea.

Introduction: As South Korean companies frequently use apologies for various crisis situations and pair them with other types of crisis response strategies (i.e., scapegoating), theory-driven recommendations for crisis response messages may fall short in practice. This study empirically examines the effectiveness of two crisis response messages (i.e., apology + compensation vs. apology + scapegoating) by integrating the theory of communicative responsibility and situational crisis communication theory. Methods: South Korean participants (n = 392) read one of two vignettes: the vignettes described an automobile company's apology for malfunctioning seat belts which included either compensation or scapegoating. The participant's perceived communicative responsibility, appropriateness of the apology, and reputation of the company were measured. Process analysis was conducted to examine the mediated-moderation effect of the crisis response messages. Results and Discussion: The findings indicate that an apology that is provided with compensation is more appropriate than those with scapegoating. The appropriateness is moderated by the perceived symmetry in communicative responsibility, and fully mediates the relationship between apology type and reputation. This study integrates two theoretical models to examine the mechanism behind the crisis response strategies from the perspective of the message receivers, while considering the cultural and normative context of South Korea.

Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions.

The "replication crisis" in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform.