Dataset of autobiographical and future-related thoughts recorded during a laboratory vigilance task with working memory load manipulation.

The dataset provided in this article comprises frequencies of task-related thoughts, task-unrelated thoughts, involuntary autobiographical memories (IAMs), and involuntary future thoughts (IFTs) reported by adult participants during a laboratory vigilance task. Participants completed a vigilance task that included incidental cue words intended to trigger IAMs and IFTs, whose frequency was measured using random thought probes. The data were collected from two studies (n = 240 per study) in which working memory load and cue-presentation were manipulated. In both studies, participants completed an unexpected cue-recognition task after completing the vigilance task, which allowed for gathering additional data about noticing and remembering specific categories of cues (positive, neutral or negative). The dataset includes not only the frequencies of specific categories of thoughts but also data from numerous follow-up questions related to how participants perceived their performance in the task, such as their concentration level or perceived task difficulty. In conclusion the dataset contains three categories of variables: (1) variables related to participants and the conditions of the experimental sessions (i.e., age, gender, working memory load condition, etc.); (2) variables related to control questions (i.e., perceived task difficulty, emotional states, fatigue, etc.); and (3) variables related to performance in the vigilance task and the occurrence of thoughts (i.e., number of task-unrelated thoughts, number of involuntary memories, percentage of successfully recognized cues, etc.). This dataset could be reused to investigate many interesting relationships between cognitively engaging computer task characteristics and various parameters of task performance. Additionally, it could be used to conduct alternative or replication analyses to gain a deeper understanding of the relationship between working memory load and the experience of involuntary thoughts.

A comprehensive guide to research protocols for collecting and coding involuntary past and future thoughts.

The paper presents a comprehensive guide for researchers investigating mind-wandering and related phenomena such as involuntary past and future thinking. Examining such spontaneous cognitions presents a challenge requiring not only the use of appropriate laboratory-based procedures, but also the coding of complex qualitative data. This guide outlines two main stages of existing research protocols: data acquisition and data coding. For the former, we introduce an easily modifiable computerized version of the vigilance task, designed for broad application in studies focusing on eliciting and measuring involuntary thoughts in controlled laboratory conditions. Regarding data preparation and coding, we provide a detailed step-by-step procedure for categorizing and coding different types of thoughts, involving both participants and competent judges. Additionally, we address some of the difficulties that may arise during this categorization and coding process. The guide is supplemented by a clip demonstrating the main part of the experimental procedure and a step-by-step example of the subsequent data processing stages. We anticipate that this research guide will not only assist a broader group of researchers interested in investigating spontaneous cognition, but will also inspire future studies on spontaneous cognition and related phenomena.•There is a need for standardized approaches to working with qualitative data when investigating spontaneous thoughts.•The paper outlines a comprehensive protocol for collecting and coding involuntary past and future-oriented thoughts.•The paper also presents a detailed step-by-step procedure for data preparation and coding to categorize different types of thoughts, involving both participants and competent judges.

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.