Regression II. Development through regression

As shown in our previous paper (‘Regression I. Experimental approaches to regression’, JAP, 65, 2, 345-65), the common mechanism of regression can be described as reversible dedifferentiation, which is understood as a relative increase of the proportion of low-differentiated (older) systems in actualized experience. Experimental data show that regression following disease (chronic tension headache) is followed by adaptation and an increase in system differentiation in that experience domain which contains systems responsible for that adaptation. The results of mathematical modelling support the idea that reversible dedifferentiation can be one of the mechanisms for increasing the effectiveness of adaptation through learning. Reversible dedifferentiation, which is phenomenologically described as regression, is a general mechanism for restructuring the organism-environment interactions in situations where behaviours that were effective in the past become ineffective. Reversible dedifferentiation has evolved as a component of adaptation when new behaviours are formed and large-scale modifications in the existing behaviours are required in the face of changes in the external and/or internal environment. Thus, the authors believe that this article provides evidence for Jung’s view that regression is not only a ‘return’ to past forms of thinking, affects and behaviour, but that regressive processes provide a significant impetus for psychological growth and development.     

Searching for Prosociality in Qualitative Data: Comparing Manual,Closed-Vocabulary,and Open-Vocabulary Methods

Although most people present themselves as possessing prosocial traits, people differ in the extent to which they actually act prosocially in everyday life. Qualitative data that were not ostensibly collected to measure prosociality might contain information about prosocial dispositions that is not distorted by self-presentation concerns. This paper seeks to characterise charitable donors from qualitative data. We compared a manual approach of extracting predictors from participants' self-described personal strivings to two automated approaches: A summation of words predefined as prosocial and a support vector machine classifier. Although variables extracted by the support vector machine predicted donation behaviour well in the training sample (N = 984), virtually, no variables from any method significantly predicted donations in a holdout sample (N = 496). Raters' attempts to predict donations to charity based on reading participants' personal strivings were also unsuccessful. However, raters' predictions were associated with past charitable involvement. In sum, predictors derived from personal strivings did not robustly explain variation in charitable behaviour, but personal strivings may nevertheless contain some information about trait prosociality. The sparseness of personal strivings data, rather than the irrelevance of open-ended text or individual differences in goal pursuit, likely explains their limited value in predicting prosocial behaviour. © 2020 European Association of Personality Psychology     

To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

For decades, day–night patterns in behaviour have been investigated by asking people about their sleep–wake timing, their diurnal activity patterns, and their sleep duration. We demonstrate that the increasing digitalization of lifestyle offers new possibilities for research to investigate day–night patterns and related traits with the help of behavioural data. Using smartphone sensing, we collected in vivo data from 597 participants across several weeks and extracted behavioural day–night pattern indicators. Using this data, we explored three popular research topics. First, we focused on individual differences in day–night patterns by investigating whether ‘morning larks’ and ‘night owls’ manifest in smartphone-sensed behavioural indicators. Second, we examined whether personality traits are related to day–night patterns. Finally, exploring social jetlag, we investigated whether traits and work weekly day–night behaviours influence day–night patterns on weekends. Our findings highlight that behavioural data play an essential role in understanding daily routines and their relations to personality traits. We discuss how psychological research can integrate new behavioural approaches to study personality.     

‘Personality in Its Natural Habitat’ Revisited: A Pooled,Multi-sample Examination of the Relationships Between the Big Five Personality Traits and Daily Behaviour and Language Use

Past research using the Electronically Activated Recorder (EAR), an observational ambulatory assessment method for the real-world measurement of daily behaviour, has identified several behavioural manifestations of the Big Five domains in a small college sample (N = 96). With the use of a larger and more diverse sample of pooled data from N = 462 participants from a total of four community samples who wore the EAR from 2 to 6 days, the primary purpose of the present study was to obtain more precise and generalizable effect estimates of the Big Five–behaviour relationships and to re-examine the degree to which these relationships are gender specific. In an extension of the original article, the secondary purpose of the present study was to examine if the Big Five–behaviour relationships differed across two facets of each Big Five domain. Overall, while several of the behavioural manifestations of the Big Five were generally consistent with the trait definitions (replicating some findings from the original article), we found little evidence of gender differences (not replicating a basic finding from the original article). Unique to the present study, the Big Five–behaviour relationships were not always comparable across the two facets of each Big Five domain. © 2020 European Association of Personality Psychology     

Differential association between food craving,food addiction and eating-related characteristics in persons at risk for eating disorders

IntroductionFood craving is a key feature of eating disorders, but its association with medical and psychological outcome might depend on the type of eating disorder.ObjectivesThis study investigated the factors associated with food craving in individuals at risk for DSM-5 eating disorder.MethodSeventy-six women were recruited from a non-clinical population (n = 372) based on their positive screening (EDDS, QEWP-R) for binge eating disorder (BED; n = 29), anorexia nervosa (AN; n = 28) or bulimia nervosa (BN; n = 19). They completed self-administered questionnaires assessing food craving (FCQ- T-r), Body Mass Index (BMI), eating behaviour (DEBQ), food addiction (YFAS 2.0), and anxiety and depressive symptoms (HADS).ResultsIndividuals at risk for BED or BN had higher food craving than those at risk for AN. Food craving was associated with BMI only in the BED group. Food craving was positively correlated with external eating in all groups, and with emotional eating in the AN and BED groups and correlated with anxiety only in BN. Food addiction prevalence was higher in the BN group than in the AN or BED groups (respectively 94.7%, 46.4% and 65.5%; p < 0.01). In this non-clinical population, we demonstrated that food craving was differentially associated with BMI and eating-related characteristics according to the type of eating disorder.ConclusionThis suggests that food craving is a transdiagnostic dimension that should be differentially targeted depending on the type of eating disorder; future studies should test this hypothesis in clinical populations.     

Traffic safety competencies in Peruvian high school students

This study aims to comprehend traffic safety competencies in high school students from two specific regions of Peru, as well as to analyze the differences between both groups. For this purpose, a qualitative study was carried out which consisted of eight focus groups in high schools, three of them in the metropolitan area of Lima (Lima region) and five in the province of Rodríguez de Mendoza (the Amazonas region). The focus groups were constituted by 73 students enrolled in the last three years of high school, named 3rd, 4th and 5th grade of secondary education, whose ages ranged from 14 to 18 years. The collected data were analyzed employing elements of Grounded Theory and Axiomatic Set Theory. The main findings reveal that most participants have a general understanding of traffic rules and the proper use of the components of traffic safety. Thus, they understand the traffic rules as road signs that regulate the behavior of drivers and pedestrians. In addition, participants know the proper procedure to follow at traffic lights, pedestrian walkways and how to use seat belts. They also identify the driver’s tiredness/drowsiness, and the consumption of alcohol and drugs as potential factors that cause traffic accidents. No causes that are linked to pedestrian behavior were identified, and only participants from the Amazonas region mentioned speeding as a contributing factor. On the other hand, participants report that in practical situations they engage in risky behavior and fail to comply with transit rules. They justify this behavior by citing poor infrastructure, vehicle malfunctions, and the need get around quickly in order to get things done. Finally, the implications of the results are discussed and compared to the content and implementation of the Peruvian Road Safety Educational Program (PENSV, for its acronym in Spanish), providing recommendations that can aid evidence‐based policy making in Peru.     

Prevalence and factors associated with turn signal neglect-related crashes among motorcyclists and car drivers in Vietnam

Road traffic crashes and injuries are a major societal challenge around the world. However, the majority of injuries and fatalities occur disproportionately in low-income and medium-income countries. Previous studies have concluded that risky behaviours were the main contributing factor of traffic crashes. Turn signal neglect (TSN) when making a turn is one of those risky behaviours. Unfortunately, research on TSN has been very limited. This study investigates the prevalence of TSN-related crashes and factors affecting TSN among motorcyclists and car drivers. Data was obtained from self-administered questionnaires conducted in Vietnam. The findings showed that 17.54% of motorcyclists and 14.76% of car drivers have experienced at least one crash caused by their failure to turn signals in the last three years. Additionally, fewer years having a riding/driving license, longer daily travelling time, lower frequency of turn signal use, and having received a fine due to TSN were found to be positively associated with TSN-related crashes for both motorcyclists and car drivers. The findings highlight the importance of TSN for road safety and the need for authorities to develop more effective educational strategies and to increase traffic law enforcement.     

Drivers’ visual attention: A field study at intersections

Crossing a road intersection, a driver must collect visual information from various locations. The allocation of visual attention, which allows this collection, mainly relies on top-down processes. This study focuses on three top-down factors which influence the collection of visual information: the value of visual information for the ongoing task, their bandwidth, and the familiarity with the environment. These factors are studied according to the priority rules at intersections (Give way, Stop or Priority), the expected traffic density (Lower or Higher) and the number of passages (First or Second passage). Fourteen participants were installed in an instrumented vehicle equipped with an eye-tracker. They drove during 1h45 along a 80-km long route, mainly on rural roads, which included 19 intersections. Visual attention was studied by means of the head and gaze horizontal eccentricity. Effects were found for each of the three factors, in agreement with Wickens’ theoretical framework and with previous studies, despite the important variability in the data due to the experimental situation.