Enter the Wild: Autistic Traits and Their Relationship to Mentalizing and Social Interaction in Everyday Life

Schuwerk, Tobias; Kaltefleiter, Larissa J.; Au, Jiew–Quay; Hoesl, Axel; Stachl, Clemens

doi:10.1007/s10803-019-04134-6

Cited by 13 publications

(10 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Participants were mostly recruited in the university context via flyers, mailings lists, social media, and personal contact in Munich, Germany. For more detailed information about study procedures, see also Stachl et al (2017); Harari et al (2019); Schuwerk et al (2019); Schoedel et al (2018); and Stachl et al (2019).…”

Section: Description Of Data Setmentioning

confidence: 99%

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

Schoedel

Pargent

Au³

et al. 2020

Eur J Pers

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Description Of Data Setmentioning

confidence: 99%

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

Schoedel

Pargent

Au³

et al. 2020

Eur J Pers

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, it remains unclear how well GPS and accelerometer data can capture social interactions. In addition, Schuwerk et al [ 65 ] used the number of contacts as an indicator of social network size [ 65 ], but it is uncertain how accurately the number of contacts can describe the social network size as no research was done to investigate this.…”

Section: Resultsmentioning

confidence: 99%

“…In 20% (3/15) of the studies, different measures were combined to cover a wider part of the social environment [ 65 , 72 , 73 ]. A total of 67% (2/3) of these studies did this to investigate questions regarding different parts of the social environment.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 67% (2/3) of these studies did this to investigate questions regarding different parts of the social environment. Schuwerk et al [ 65 ] investigated offline social interactions measured via ESM and interactions measured via smartphones (eg, number and duration of calls). Abel et al [ 72 ] aimed to capture social interactions objectively via EAR and, in addition, used daily questionnaires to capture subjective emotions during a social interaction [ 72 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review

Langener¹,

Stulp²,

Kas³

et al. 2023

JMIR Ment Health

View full text Add to dashboard Cite

Background Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people’s social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people’s social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. Objective Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. Methods We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. Conclusions We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants’ burden and form a holistic perspective on the social environment.

show abstract

“…Every individual makes a ‘difference’, and one’s mentalising ability, or theory of mind (ToM) (Schaafsma et al, 2015), one of the core functions in human social life (Frith & Frith, 2006), underlies this significance and uniqueness. Such ability is fundamental to social understanding and social learning that its dysfunction is associated with various social disorders, such as autism spectrum disorder (ASD) (Schuwerk et al, 2019; Hyatt et al, 2020) and social anxiety disorder (Washburn et al, 2016). Though most previous work has described mentalising ability as a whole (i.e., the ability to reason about and infer other’s mental states) (Frith & Frith, 2005; Adolphs, 2009), mentalising is a multifaceted concept that refers to multiple constructs involved in treating others and ourselves as social agents (Allen & Fonagy, 2008).…”

Section: Introductionmentioning

confidence: 99%

Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability

Li¹,

Dong

et al. 2022

Preprint

View full text Add to dashboard Cite

Mentalising ability, indexed as the ability to understand others' beliefs, feelings, intentions, thoughts, and traits, is a pivotal and fundamental component of human social cognition. However, considering the multifaceted nature of mentalising ability, little research has focused on characterising individual differences in different mentalising components. And even less research has been devoted to investigating how the variance in the structural and functional patterns of the amygdala and hippocampus, two vital subcortical regions of the 'social brain', are related to inter-individual variability in mentalising ability. Here, as a first step toward filling these gaps, we exploited inter-subject representational similarity analysis (IS-RSA) to assess relationships between amygdala and hippocampal morphometry (surface-based multivariate morphometry statistics, MMS), connectivity (resting-state functional connectivity, rs-FC) and mentalising ability (interactive mentalisation questionnaire (IMQ) scores) across the participants (N = 24). In IS-RSA, we proposed a novel pipeline, i.e., computing patching and pooling operations-based surface distance (CPP-SD), to obtain a decent representation for high-dimensional MMS data. On this basis, We found significant correlations (i.e., second-order isomorphisms) between these three distinct modalities, indicating that a trinity existed in idiosyncratic patterns of brain morphometry, connectivity and mentalising ability. Notably, a region-related mentalising specificity emerged from these associations: self-self and self-other mentalisation are more related to the hippocampus, while other-self mentalisation shows a closer link with the amygdala. Furthermore, by utilising the dyadic regression analysis, we observed significant interactions such that subject pairs with similar morphometry had even greater mentalising similarity if they were also similar in rs-FC. Altogether, we demonstrated the feasibility and illustrated the promise in using IS-RSA to study individual differences, deepening our understanding of how individual brains give rise to their mentalising abilities.

show abstract

Enter the Wild: Autistic Traits and Their Relationship to Mentalizing and Social Interaction in Everyday Life

Cited by 13 publications

References 52 publications

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

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

Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review

Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability

Contact Info

Product

Resources

About