When faced with many options to choose from, humans typically need to explore the utility of new choice options. People with an autism diagnosis or elevated autism traits are thought to avoid exploring such unknown options, but it remains unclear how autism affects exploration in decision spaces with many options. In a large online sample (N = 588), we investigated the impact of autism diagnosis or elevated autism traits on exploration behavior during value-based decision-making in vast decision spaces. We used a 121-armed bandit with spatially correlated choice options, and a dedicated computational model to disentangle generalization, uncertainty-guided exploration, and random exploration strategies. Our findings show that participants with a self-reported autism diagnosis were less likely to explore novel choice options and more likely to exploit known high-value options. Computational modeling suggests they engaged in less uncertainty-driven exploration but exhibited equal random exploration and generalization strategies. Interestingly, among non-diagnosed participants, people with elevated autism traits did not explore less. This study relies on self-reported autism diagnoses and trait measures collected online. This may limit the generalizability of the findings to clinically verified or more diverse autism populations. Our findings highlight important differences in exploration strategies between clinical and subclinical populations and emphasize the importance of cognitive modeling and using vast decision spaces to better understand autism.

npj Complexity - The complexity of misinformation extends beyond virus and warfare analogies

Two EEG studies in healthy human adults suggest that choice history and stimulus probability-induced biases in somatosensory perception are reflected in distinct prestimulus beta power modulations across distinct cortical regions.

npj Science of Learning - Do it yourself: discerning the effects of self-directed activity on conceptual learning

A growing number of self-report measures aim to define interactions with social media in a pathological behavior framework, often using terminology focused on identifying those who are ‘addicted’ to engaging with others online. Specifically, measures of ‘social media addiction’ focus on motivations for online social information seeking, which could relate to motivations for offline social information seeking. However, it could be the case that these same measures could reveal a pattern of friend addiction in general. This study develops the Offline-Friend Addiction Questionnaire (O-FAQ) by re-wording items from highly cited pathological social media use scales to reflect “spending time with friends”. Our methodology for validation follows the current literature precedent in the development of social media ‘addiction’ scales. The O-FAQ had a three-factor solution in an exploratory sample of N = 807 and these factors were stable in a 4-week retest (r = .72 to .86) and was validated against personality traits, and risk-taking behavior, in conceptually plausible directions. Using the same polythetic classification techniques as pathological social media use studies, we were able to classify 69% of our sample as addicted to spending time with their friends. The discussion of our satirical research is a critical reflection on the role of measurement and human sociality in social media research. We question the extent to which connecting with others can be considered an ‘addiction’ and discuss issues concerning the validation of new ‘addiction’ measures without relevant medical constructs. Readers should approach our measure with a level of skepticism that should be afforded to current social media addiction measures.

Using machine learning, this study revealed that predictors from 51 theoretical models of delusions explain only 31% of the variance in persecutory beliefs, raising questions about current theories and pointing to gaps in understanding the specific aetiology of delusions.