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Large language models (LLMs) are demonstrating impressive performance on many reasoning and problem-solving tasks from cognitive psychology. When tested, their accuracy is often on par with average neurotypical adults, challenging long-standing critiques of associative models. Here we analyse recent findings at the intersection of LLMs and cognitive science. Here we discuss how modern LLMs resurrect associationist principles, with abilities like long-distance associations enabling complex reasoning. While limitations remain in areas like causal cognition and planning, phenomena like emergence suggest room for growth. Providing examples and increasing the dimensions of the network are methods that further improve LLM abilities, mirroring facilitation effects in human cognition. Analysis of LLMs errors provides insight into human cognitive biases. Overall, we argue LLMs represent a promising development for cognitive modelling, enabling new explorations of the mechanisms underlying intelligence and reasoning from an associationist point of view. Carefully evaluating LLMs with the tools of cognitive psychology will further understand the building blocks of the human mind.

IntroductionThe false consensus effect consists of an overestimation of how common a subject opinion is among other people. This research demonstrates that individual endorsement of questions may be predicted by estimating peers’ responses to the same question. Moreover, we aim to demonstrate how this prediction can be used to reconstruct the individual’s response to a single item as well as the overall response to all of the items, making the technique suitable and effective for malingering detection.MethodWe have validated the procedure of reconstructing individual responses from peers’ estimation in two separate studies, one addressing anxiety-related questions and the other to the Dark Triad. The questionnaires, adapted to our scopes, were submitted to the groups of participants for a total of 187 subjects across both studies. Machine learning models were used to estimate the results.ResultsAccording to the results, individual responses to a single question requiring a “yes” or “no” response are predicted with 70–80% accuracy. The overall participant-predicted score on all questions (total test score) is predicted with a correlation of 0.7–0.77 with actual results.DiscussionThe application of the false consensus effect format is a promising procedure for reconstructing truthful responses in forensic settings when the respondent is highly likely to alter his true (genuine) response and true responses to the tests are missing.

Background: Several data suggest that coronavirus disease 2019 (COVID-19) pandemic may exacerbate or trigger eating disorders (EDs). The aim of this paper was to summarize current literature studies on COVID pandemic and EDs.Methods: Literature search, study selection, methods, and quality evaluation were performed according to the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines.Results: A systematic search allowed the initial selection of 172 papers; 21 (12.2%) papers were eligible and included in the review. In selected studies, a total number of 29,108 subjects were enrolled (range: 10–11,391; mean/SD: 1,386 ± 2,800), 6,216 were men (21.4%), 22,703 were women (77.9%), and 189 (0.7%) were gender fluid or not declared. The mean age/SD of subjects was 30.2 ± 7.7. About 12 studies (57.1%) were online surveys, 4 (19.0%) were retrospective studies, 2 (9.5%) were qualitative studies, 2 (9.5%) were longitudinal cohort studies, and 1 was a social media survey (4.8%). Their analysis revealed five main findings: (1) changes in the routines of physical activities were related to the worsening of preoccupation on weight/body shape; (2) food access limitation during pandemic represented the risk factors for both triggering and exacerbating EDs; (3) restriction in healthcare facilities contributed to increase anxiety levels and to modify treatment compliance; (4) social isolation was related to the exacerbation of symptoms in patients with EDs who are home-confined with family members; and (5) conflicts and difficulties in relationships with “no way out” were the maintenance factors for ED symptoms, especially in adolescents and young adults.Conclusion: The COVID-19 pandemic had a negative impact on EDs that might be triggered by the exceptional conditions derived from COVID-19-related stress in predisposed subjects. Patients who were already affected by EDs experienced the worsening of their clinical conditions and related quality of life (QoL).

This contribution places itself within the emergency context of the COVID-19 spread. Until medical research identifies a cure acting at an organic level, it is necessary to manage what the emergency generates among the members of the Community in interactive terms in a scientific and methodologically well-founded way. This is in order to promote, among the members of the Community, the pursuit of the common aim of reducing the spread of infection, with a view to community health as a whole. In addition, being at the level of interactions enables us to move towards a change of these interactions in response to the COVID-19 emergency, in order to manage what will happen in the future, in terms of changes in the interactive arrangements after the emergency itself. This becomes possible by shifting away from the use of deterministic-causal references to the use of the uncertainty of interaction as an epistemological foundation principle. Managing the interactive (and non-organic) fallout of the emergency in the Community is made possible by the formalisation of the interactive modalities (the Discursive Repertories) offered by Dialogical Science. To place oneself within this scientific panorama enables interaction measurements: so, the interaction measurement indexes offers a range of generative possibilities of realities built by the speeches of the Community members. Moreover, the Social Cohesion measurement index, in the area of Dialogical Science, makes available to public policies the shared measure of how and by how much the Community is moving towards the common purpose of reducing the contagion spread, rather than moving towards other personal and not shared goals (for instance, having a walk in spite of the lockdown). In this index, the interaction between the Discursive Repertories and the “cohesion weight” associated with them offers a Cohesion output: the data allow to manage operationally what happens in the Community in a shared way and in anticipation, without leaving the interactions between its members to chance. In this way, they can be directed towards the common purpose through appropriate interventions relevant to the interactive set-up described in the data. The Cohesion measure makes it possible to operate effectively and efficiently, thanks to the possibility of monitoring the progress of the interventions implemented and evaluating their effectiveness. In addition, the use of predictive Machine Learning models, applied to interactive cohesion data, allows for immediate and efficient availability of the measure itself, optimising time and resources.

The coronavirus disease 2019 (COVID-19) outbreak has caused not only significant physical health problems but also mental health disorders. Anxiety and fear appear to be the main psychological symptoms associated with COVID-19. The aim of this study was to investigate whether anxiety and fear related to COVID-19 are influenced by sociodemographics and whether specific conditions, such as positivity for COVID-19 or death among relatives and friends, can further enhance these symptoms. In this cross-sectional study, 697 Italians responded to an online survey assessing sociodemographic information, the presence/absence of positive cases, or deaths due to COVID-19 among relatives or acquaintances. The Coronavirus Anxiety Scale (CAS) and Fear of COVID-19 Scale (FCS-19S) were administered in order to assess the levels of anxiety and fear associated with COVID-19. The data were collected in November 2020. Anxiety and fear scores were positively correlated. Both male and female subjects with higher CAS scores also displayed higher FCS-19S scores. The CAS and FCS-19S scores tended to increase with age, with older subjects exhibiting higher scores than younger subjects. Additionally, respondents with lower educational levels demonstrated higher scores on both the CAS and FCS-19S. Similarly, respondents living with older people and/or experiencing the death of one or more relatives due to COVID-19 exhibited corresponding outcomes. This study demonstrates how the levels of anxiety and fear, measured by CAS and FCS-19S associated with COVID-19, are influenced by gender, age, cohabitation status, educational levels, and the presence of positive cases or deaths due to COVID-19.

ObjectiveMindfulness disposition is associated with various psychological factors and prevents emotional distress in chronic diseases. In the present study, we analyzed the key role of mindfulness dispositions in protecting the individual against psychological distress consequent to COVID-19 social distancing and quarantining.MethodsAn online survey was launched on March 13, 2020, with 6,412 responses by April 6, 2020. Socio-demographic information, exposure to the pandemic, and quarantining were assessed together with psychological distress and mindfulness disposition. Multivariate linear regression analysis was performed to study the influence of predictive factors on psychological distress and quality of life in Italian responders during the early days of lockdown. Pearson correlations were calculated to study the relationship between mindfulness and psychiatric symptoms.ResultsMultivariate linear regression run on socio-demographics, COVID-19-related variables, and mindfulness disposition as moderators of overall psychological distress showed that mindfulness was the best predictor of psychological distress (β = −0.504; p < 0.0001). High negative correlations were found between mindfulness disposition and the overall Global Severity Index (r = −0.637; p < 0.0001), while moderate to high associations were found between mindfulness and all SCL-90 sub-scales.DiscussionFindings showed that high dispositional mindfulness enhances well-being and helps in dealing with stressful situations such as the COVID-19 pandemic. Mindfulness-based mental training could represent an effective intervention to stem post-traumatic psychopathological beginnings and prevent the onset of chronic mental disorders.

Health care professionals (HCPs) are a population at risk for high levels of burnout and compassion fatigue. The aim of the present systematic review was to give an overview on recent literature about mindfulness and compassion characteristics of HCPs, while exploring the effectiveness of techniques, involving the two aspects, such as MBSR or mindfulness intervention and compassion fatigue-related programs. A search of databases, including PubMed and PsycINFO, was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the methodological quality for this systematic review was appraised using AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews-2). The number of articles that met the inclusion criteria was 58 (4 RCTs, 24 studies with pre-post measurements, 12 cross-sectional studies, 11 cohort studies and 7 qualitative studies). MBSR intervention was effective at improving, and maintaining, mindfulness and self-compassion levels and to improve burnout, depression, anxiety, stress. The most frequently employed interventional strategies were mindfulness-related trainings that were effective at improving mindfulness and self-compassion, but not compassion fatigue, levels. Compassion-related interventions have been shown to improve self-compassion, mindfulness and interpersonal conflict levels. Mindfulness was effective at improving negative affect and compassion fatigue, while compassion satisfaction may be related to cultivation of positive affect. This systematic review summarized the evidence regarding mindfulness- and compassion-related qualities of HCPs as well as potential effects of MBSR, mindfulness-related and compassion-related interventions on professionals' psychological variables like mindfulness, self-compassion and quality of life. Combining structured mindfulness and compassion cultivation trainings may enhance the effects of interventions, limit the variability of intervention protocols and improve data comparability of future research.

Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting from adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions.

Objective: Here we report an investigation on the accuracy of the b Test, a measure to identify malingering of cognitive symptoms, in detecting malingerers of mild cognitive impairment.Method: Three groups of participants, patients with Mild Neurocognitive Disorder (n = 21), healthy elders (controls, n = 21), and healthy elders instructed to simulate mild cognitive disorder (malingerers, n = 21) were administered two background neuropsychological tests (MMSE, FAB) as well as the b Test.Results: Malingerers performed significantly worse on all error scores as compared to patients and controls, and performed poorly than controls, but comparably to patients, on the time score. Patients performed significantly worse than controls on all scores, but both groups showed the same pattern of more omission than commission errors. By contrast, malingerers exhibited the opposite pattern with more commission errors than omission errors. Machine learning models achieve an overall accuracy higher than 90% in distinguishing patients from malingerers on the basis of b Test results alone.Conclusions: Our findings suggest that b Test error scores accurately distinguish patients with Mild Neurocognitive Disorder from malingerers and may complement other validated procedures such as the Medical Symptom Validity Test.

BackgroundFibromyalgia syndrome (FMS) is a chronic rheumatologic disease characterized by widespread musculoskeletal pain and other psychopathological symptoms which have a negative impact on patients’ quality of life. FMS is frequently associated with alexithymia, a multidimensional construct characterized by difficulty in identifying feelings (DIF) and verbally communicating them difficulty describing feelings (DDF) and an externally oriented cognitive thinking style (EOT). The aim of the present study was to investigate the relationship between alexithymia, anxious and depressive symptoms and pain perception, in patients with FMS and other rheumatic diseases (RD).MethodsThe sample consisted of 127 participants (M = 25, F = 102; mean age: 51.97; SD: 11.14), of which 48 with FMS, 41 with RD and 38 healthy control group (HC). All groups underwent to a test battery investigating anxiety and depressive symptoms (HADS), pain (VAS; QUID-S/-A) and alexithymia (TAS-20).ResultsA high prevalence of alexithymia (TAS ≥ 61) was found in FMS (47.9%) and RD (41.5%) patients, compared to the HC group (2.6%). FMS patients showed significant higher scores than HC on DIF, DDF, EOT, anxiety and depression. The clinical sample, FMS and RD groups combined (n = 89), alexithymic patients (AL, n = 40) exhibited higher scores in pain and psychological distress compared to non-alexithymic patients (N-AL, n = 34). Regression analysis found no relationship between alexithymia and pain in AL, meanwhile pain intensity was predicted by anxiety in N-AL.ConclusionWhile increasing clinical symptoms (pain intensity and experience, alexithymia, anxiety, and depression) in patients with fibromyalgia or rheumatic diseases, correlations were found on the one side, between alexithymia and psychological distress, on the other side, between pain experience and intensity. Meanwhile, when symptoms of psychological distress and alexithymia were subthreshold, correlations with pain experience and intensity became stronger.