Marek Wypych

Objective: To determine whether machine-learning models using baseline self-report data couldpersonalize selection between two behavioral strategies embedded within cognitive-behavioraltherapy (CBT) for procrastination.Methods: Participants were 256 Polish-speaking university students aged 18–26 years (M=22.4;73.0% women) who completed active treatment in two preregistered randomized trials(development sample, N=173; external validation sample, N=83). Participants received fiveweekly group CBT sessions with identical psychoeducation and cognitive restructuring butdifferent behavioral modules: Realistic Planning and Timely Beginning (RPT) or Working TimeRestriction (WTR). Separate partial least-squares regression models used 483 baselinedemographic and self-report items to predict posttreatment Aitken ProcrastinationInventory–Revised (API-R) scores under each strategy. Predicted between-strategy differencesand Youden-index cutoffs generated recommendations: RPT, WTR, Either, or Neither. Becausetreatment assignment was randomized, outcomes could be compared between participants whoseassigned treatment matched versus mismatched their model-derived recommendation.Results: In the development trial, matched participants had lower posttreatment API-R scoresthan mismatched participants (p=5.6×10−24; d=1.90) and higher likely-response rates(p=1.8×10−15; RR=4.6). In the validation trial, each treatment-specific model explained 9% ofoutcome variance. Matched participants again had lower API-R scores (p=.009; d=.53) andhigher response rates (p=.04; RR=1.3). Differences remained at six months (p=.033; d=.40).Conclusion: Together, these findings indicate that personalization of procrastination treatmentcan be achieved by modeling differential response to specific behavioral components rather thanentire treatment packages, supporting precision intervention in procrastination and beyond.Further, explorative examination of predictors suggests that individuals may benefit from amechanism-informed, but not strictly mechanism-deterministic, precision framework.

Introduction. Procrastination is highly prevalent among university students, often becoming chronic and undermining academic performance and well-being. Cognitive behavioral therapy (CBT) is the most studied treatment, but evidence remains limited to a few rigorous trials, broad multi-component packages, and scarce long-term follow-up. We evaluated two CBT protocols differing in behavioral modules against an active comparator and waitlist control.Methods. In this four-arm randomized controlled trial, 255 help-seeking university students with problematic procrastination, verified through semi-structured clinical interviews, were randomized to Realistic Planning and Timely Beginning (RPT), Working Time Restriction (WTR), the Pomodoro Technique (PT), or waitlist (WL). Active conditions comprised five weekly online group sessions with shared psychoeducational and cognitive modules. Outcomes included academic procrastination (API-R), three Pure Procrastination Scale subscales, academic project progress, depression (PHQ-9), anxiety (GAD-7), and treatment-attributed negative effects (NEQ-20), analyzed using linear mixed models, with a six-month follow-up in active conditions.Results. All active interventions produced large reductions in procrastination compared with WL (API-R: d = −1.24 to −1.54), with no significant differences between them. Reliable or likely improvement occurred in 44.8–55.4% of active-condition participants versus 15.5% in WL. RPT and WTR additionally improved project progress. Depression decreased in all active conditions and anxiety in WTR and PT. Procrastination reductions were maintained at six months; early gains in depression and anxiety were not. Negative effects, reported by 39.7% of completers, were mostly mild or moderate.Conclusion. Brief group-based CBT protocols produced large and durable reductions in problematic procrastination among university students, with comparable efficacy across protocols.

Objective. Procrastination is a common self-regulatory difficulty leading to impaired well-being. Although cognitive-behavioral therapy (CBT) is considered the most promising intervention for procrastination, it remains unclear whether outcomes vary across individuals and which mechanisms drive therapeutic change. To address these gaps, we synthesized key mechanistic accounts of procrastination in an integrative cognitive-behavioral (ICB-P) model outlining processes involved in development and persistence of procrastination. We then comprehensively examined the processes of change in CBT for procrastination.Methods. Candidate moderators and mechanisms of therapeutic change were examined in secondary analyses of pooled data (N = 459) from two randomized controlled trials comparing CBT for academic procrastination with a wait-list control. Procrastination severity and a wide set of candidate mechanisms were assessed at pre-, mid-, and post-intervention. Moderation analyses and longitudinal structural equation models with contemporaneous (within-wave) and lagged (cross-wave) mediation paths were applied.Results. Participants with greater baseline emotion regulation difficulties benefited more from the intervention, whereas dysfunctional beliefs associated with some cluster B personality disorders were linked to lower improvements. Baseline depression, anxiety, or ADHD symptoms did not moderate outcomes. Regarding mechanisms of change, increased task value and proactive control mediated both contemporaneous and lagged reductions in procrastination. Contemporaneous effects were mediated through increased positive and decreased negative task-related affect, improved perseverance, emotional clarity, and internal attributions of success.Conclusions. Findings suggest broad applicability of CBT for procrastination. Optimized interventions could enhance the implicated mechanisms, i.e. target task-related affect and self-regulation, or incorporate additional components. Directions for future research are discussed.

Climate change threatens human populations and ecosystems worldwide. Neuroscience research on this topic is emerging, but validated questionnaires, stimuli, and fMRI tasks remain scarce. Here, we present the CLIMATE BRAIN dataset, a multimodal collection of questionnaire, behavioral, and neuroimaging data from 160 young, healthy Polish individuals. Designed to advance research on climate emotions and pro-environmental behavior, the dataset includes individuals with moderate climate change concern. Participants read anger and hope-evoking stories about climate change and made pro-environmental decisions. The dataset includes data from (1) various questionnaire measures, including the Inventory of Climate Emotions (ICE); (2) a neuroimaging task for measuring emotional reactions to standardized Emotional Climate Change Stories (ECCS); and (3) a neuroimaging task based on Carbon Emission Task (CET) to measure climate action-taking. For technical validation, we provide image quality metrics and show the evidence for the effectiveness of the tasks consistent with prior studies. To our knowledge, the proposed multimodal dataset is currently the only publicly available resource specifically designed to investigate human brain responses to climate change.

Patients with major depressive disorder (MDD) and borderline personality disorder (BPD) are reported to have disrupted autobiographical memory (AM). Using functional magnetic resonance imaging we investigated behavioral and neural processing of the recall of emotional (sad and happy) memories in 30 MDD, 18 BPD, and 34 healthy control (HC) unmedicated women. The behavioral results showed that the MDD group experienced more sadness than the HC after the sad recall, while BPD participants experienced less happiness than HC after the happy recall. The fMRI results for sad AMs, compared to happy AMs, elicited greater activation in multiple brain regions (i.e., medial prefrontal cortex, posterior cingulate cortex, insula) linked to self-related information, emotional processing, and semantic recollection across all groups. Functional connectivity analysis revealed a significant main effect of group between the occipital cortex and precuneus and between occipital cortex and posterior cingulate cortex. The effect was driven by stronger connectivity between the occipital cortex and precuneus in the clinical groups taken together than in the HC. Our results suggest a need for stronger coordination between visual imagery and contextual recall for vivid memory retrieval in these clinical groups.

Climate change presents a fundamental threat to human populations and ecosystems across the globe. Neuroscience researchers have recently started developing ways to advance research on this topic. However, validated questionnaires, experimental stimuli, and fMRI tasks are still needed. Here we describe the CLIMATE BRAIN dataset, a multimodal collection of questionnaire, behavioural, and neuroimaging data related to climate change, acquired from 160 healthy individuals. In particular, it includes data from (1) various questionnaire measures, including the Inventory of Climate Emotions (ICE); (2) a neuroimaging task for measuring emotional reactions to standardized Emotional Climate Change Stories (ECCS); and (3) a neuroimaging task based on Carbon Emission Task (CET) to measure climate action-taking. For technical validation, we provide image quality metrics and show the evidence for the effectiveness of tasks consistent with prior studies. To our knowledge, the proposed dataset is currently the only publicly available resource specifically designed to investigate human brain responses to climate change.

Background: Alexithymia, difficulty in recognising and naming emotions, is common among people who use alcohol. There is also emerging evidence that people with alexithymia are unable to distinguish emotions from non‐emotional physiological states. The project aimed to test if alcohol use is related to the way student drinkers experience emotions and physiological states in the body.Methods: We employed a novel method to study bodily sensations related to emotions and physiological states in the context of alcohol use: the emBODY tool, which allowed participants to mark areas of the body in which they experience various emotions and physiological states.Results: Students who showed a hazardous pattern of alcohol use (alcohol use disorders identification test [AUDIT] score ≥ 7, N = 91), overall, presented higher alexithymia levels and coloured larger areas for emotions and physiological states (showed less specificity) than those who show low‐risk alcohol consumption (AUDIT ≤ 4, N = 90). Moreover, statistical classifiers distinguished feeling‐specific maps less accurately for hazardous drinkers than low‐risk drinkers [F(1,1998) = 441.16; p < 0.001], confirming that higher alcohol use is related to higher confusion of emotional and non‐emotional bodily feelings.Conclusions: Plausibly, this increased bodily confusion drives alcohol consumption: alcohol may serve as a means of dealing with undifferentiated changes in psychophysiological arousal accompanying emotional states.

Reading danger signals may save an animal’s life, and learning about threats from others allows avoiding first-hand aversive and often fatal experiences. Fear expressed by other individuals, including those belonging to other species, may indicate the presence of a threat in the environment and is an important social cue. Humans and other animals respond to conspecifics’ fear with increased activity of the amygdala, the brain structure crucial for detecting threats and mounting an appropriate response to them. It is unclear, however, whether the cross-species transmission of threat information involves similar mechanisms, e.g., whether animals respond to the aversively induced emotional arousal of humans with activation of fear-processing circuits in the brain. Here, we report that when rats interact with a human caregiver who had recently undergone fear conditioning, they show risk assessment behavior and enhanced amygdala activation. The amygdala response involves its two major parts, the basolateral and central, which detect a threat and orchestrate defensive responses. Further, we show that humans who learn about a threat by observing another aversively aroused human, similar to rats, activate the basolateral and centromedial parts of the amygdala. Our results demonstrate that rats detect the emotional arousal of recently aversively stimulated caregivers and suggest that cross-species social transmission of threat information may involve similar neural circuits in the amygdala as the within-species transmission.