The COVID-19 pandemic has made the world seem less predictable. Such crises can lead people to feel that others are a threat. Here, we show that the initial phase of the pandemic in 2020 increased individuals' paranoia and made their belief updating more erratic. A proactive lockdown made people's belief updating less capricious. However, state-mandated mask-wearing increased paranoia and induced more erratic behaviour. This was most evident in states where adherence to mask-wearing rules was poor but where rule following is typically more common. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable. People who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines and the QAnon conspiracy theories. These beliefs were associated with erratic task behaviour and changed priors. Taken together, we found that real-world uncertainty increases paranoia and influences laboratory task behaviour. ArticlesNATure HumAN BeHAVIOur (BF 10 = 0.163, strong evidence for the null hypothesis) or reversals achieved (BF 10 = 0.210, strong evidence for the null hypothesis) between social and non-social tasks. Computational modelling. Probabilistic reversal learning involves decision-making under uncertainty. The reasons for decisions may not be manifest in simple counts of choices or errors. By modelling Reward probability (%) 10

BackgroundWith millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.MethodsSchizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses.ResultsSWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function.ConclusionsEEG features derived by SVM are consistent with literature reports of gamma’s role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.

We examined one-month reliability, internal consistency, and validity of ostracism distress (Need Threat Scale) to simulated social exclusion during Cyberball. Thirty adolescents (13-18 yrs.) completed the Cyberball task, ostracism distress ratings, and measures of related clinical symptoms, repeated over one month. Need Threat Scale ratings of ostracism distress showed adequate test-retest reliability and internal consistency at both occasions. Construct validity was demonstrated via relationships with closely related constructs of anxiety, anxiety sensitivity, and emotion dysregulation, and weaker associations with more distal constructs of state paranoia and subclinical psychosis-like experiences. While ratings of ostracism distress and anxiety were significantly attenuated at retest, most participants continued to experience post-Cyberball ostracism distress at one-month follow-up, which indicates that the social exclusion induction of Cyberball persisted despite participants' familiarity with the paradigm. Overall, results suggest that the primary construct of ostracism distress is preserved over repeated administration of Cyberball, with reliability sufficient for usage in longitudinal research. These findings have important implications for translating this laboratory simulation of social distress into developmental and clinical intervention studies.

Objective:Meta-analyses report moderate effects across cognitive remediation (CR) trials in schizophrenia. However, individual responses are variable, with some participants showing no appreciable gain in cognitive performance. Furthermore, reasons for heterogeneous outcome are undetermined. We examine the extent to which CR outcome is attributable to near learning—direct gains in trained cognitive tasks—while also exploring factors influencing far transfer of gains during training to external cognitive measures.Method:Thirty-seven schizophrenia outpatients were classified as CR responders and non-responders according to change in MATRICS Consensus Cognitive Battery composite score following 20 sessions of computer-based training. Metrics of near learning during training, as well as baseline demographic, clinical, cognitive, and electroencephalographic (EEG) measures, were examined as predictors of responder status.Results:Significant post-training improvement in cognitive composite score (Cohen’s d = .41) was observed across the sample, with n = 21 and n = 16 classified as responders and non-responders, respectively. Near learning was evidenced by significant improvement on each training exercise with practice; however, learning did not directly predict responder status. Group-wise comparison of responders and non-responders identified two factors favoring responders: higher EEG individual alpha frequency (IAF) and lower antipsychotic dosing. Tested in moderation analyses, IAF interacted with learning to predict improvement in cognitive outcome.Conclusion:CR outcome in schizophrenia is not directly explained by learning during training and appears to depend on latent factors influencing far transfer of trained abilities. Further understanding of factors influencing transfer of learning is needed to optimize CR efficacy.