Search

The Artificial Intelligence (AI) research community has used ad-hoc benchmarks to measure the “intelligence” level of Large Language Models (LLMs). In humans, intelligence is closely linked to the functional integrity of the prefrontal lobes, which are essential for higher-order cognitive processes. Previous research has found that LLMs struggle with cognitive tasks that rely on these prefrontal functions, highlighting a significant challenge in replicating human-like intelligence. In December 2022, OpenAI released ChatGPT, a new chatbot based on the GPT-3.5 model that quickly gained popularity for its impressive ability to understand and respond to human instructions, suggesting a significant step towards intelligent behaviour in AI. Therefore, to rigorously investigate LLMs' level of “intelligence,” we evaluated the GPT-3.5 and GPT-4 versions through a neuropsychological assessment using tests in the Italian language routinely employed to assess prefrontal functioning in humans. The same tests were also administered to Claude2 and Llama2 to verify whether similar language models perform similarly in prefrontal tests. When using human performance as a reference, GPT-3.5 showed inhomogeneous results on prefrontal tests, with some tests well above average, others in the lower range, and others frankly impaired. Specifically, we have identified poor planning abilities and difficulty in recognising semantic absurdities and understanding others' intentions and mental states. Claude2 exhibited a similar pattern to GPT-3.5, while Llama2 performed poorly in almost all tests. These inconsistent profiles highlight how LLMs' emergent abilities do not yet mimic human cognitive functioning. The sole exception was GPT-4, which performed within the normative range for all the tasks except planning. Furthermore, we showed how standardised neuropsychological batteries developed to assess human cognitive functions may be suitable for challenging LLMs’ performance.

We investigated if interoceptive deficits could be the link between alexithymic traits and eating spectrum manifestations in a non-clinical sample. One-hundred sixty-one young adults (mean age: 23.2 ± 2.4 years) were evaluated with the Toronto Alexithymia Scale-20 (TAS-20), the Interoceptive Accuracy Scale (IAS), the Interoceptive Confusion Questionnaire (ICQ), and the Eating Attitudes Test-26 (EAT-26). Questionnaires were administered with an online procedure (Microsoft Form, Office 365 A1, Pisa, Italy) (Study Protocol #0012005/2023). We compared ICQ, IAS, and TAS-20 scores in subjects who met the threshold for a potential eating spectrum disorder according to EAT-26 scores ≥ 20 (n = 27) vs. subjects who scored n = 134), with an ANCOVA corrected for ‘age’ and ‘gender’. Subjects with EAT-26 ≥ 20, scored significantly higher at ICQ (54.4 ± 13.2 vs. 50.2 ± 6.8; p = 0.011), TAS-20 ‘Total Score’ (60.8 ± 11.9 vs. 58.1 ± 9.2; p = 0.006), and TAS-20 ‘Identifying Feelings’ (21.5 ± 7.6 vs. 17.3 ± 5.8; p = 0.0001). A binary logistic regression analysis, with EAT-26 scores < 20 vs. ≥20 as the dependent variable, and ICQ, IAS, TAS-20 total scores and dimensions, age, and gender (categorical) as covariates, showed that the only variable predicting eating spectrum symptomatology was ‘ICQ Total Score’ (OR = 1.075, 95% CI: 1.016–1.139; p = 0.013). Interoceptive confusion was the dimension linking the occurrence of alexithymic traits and eating spectrum manifestations.

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.

Obesity represents one of the wellness diseases concurring to increase the incidence of diabetes, cardiovascular diseases, and cancer. One of the main perpetuating factors of obesity is food craving, which is characterized by an urgent desire to eat a large and various amount of food, regardless of calories requirement or satiety signals, and it might be addressed to the alteration of the dorsolateral prefrontal cortex (DLPFC) activity. Despite most of the gold-standard therapies focus on symptom treatment only, non-invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) could help treat overeating by modulating specific neural pathways. The current systematic review was conducted to identify whether convergent evidence supporting the usefulness of tDCS to deal with food craving are present in the literature. The review was conducted by searching articles published up to January 1st 2022 on MEDLINE, Scopus and PsycInfo databases. We included studies investigating the effects of tDCS on food craving in subjects affected by overweight and obesity. According to eligibility criteria, 5 articles were included. Results showed that tDCS targeting left DLPFC with unipolar montage induced ameliorating effects on food craving. Controversial results were shown for the other studies, that might be ascribable to the use of bipolar montage, and the choice of other target areas. Further investigations including expectancy effect control, larger sample sizes and follow-up are needed to support more robust conclusions. To conclude, tDCS combined with the use of psychoeducative intervention, diet and physical activity, might represents a potential to manage food craving in individuals with overweight and obesity.

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.

BackgroundsThe field of Artificial Intelligence (AI) has seen a major shift in recent years due to the development of new Machine Learning (ML) models such as Generative Pre-trained Transformer (GPT). GPT has achieved previously unheard-of levels of accuracy in most computerized language processing tasks and their chat-based variations.AimThe aim of this study was to investigate the problem-solving abilities of ChatGPT using two sets of verbal insight problems, with a known performance level established by a sample of human participants.Materials and methodsA total of 30 problems labeled as “practice problems” and “transfer problems” were administered to ChatGPT. ChatGPT's answers received a score of “0” for each incorrectly answered problem and a score of “1” for each correct response. The highest possible score for both the practice and transfer problems was 15 out of 15. The solution rate for each problem (based on a sample of 20 subjects) was used to assess and compare the performance of ChatGPT with that of human subjects.ResultsThe study highlighted that ChatGPT can be trained in out-of-the-box thinking and demonstrated potential in solving verbal insight problems. The global performance of ChatGPT equalled the most probable outcome for the human sample in both practice problems and transfer problems as well as upon their combination. Additionally, ChatGPT answer combinations were among the 5% of most probable outcomes for the human sample both when considering practice problems and pooled problem sets. These findings demonstrate that ChatGPT performance on both set of problems was in line with the mean rate of success of human subjects, indicating that it performed reasonably well.ConclusionsThe use of transformer architecture and self-attention in ChatGPT may have helped to prioritize inputs while predicting, contributing to its potential in verbal insight problem-solving. ChatGPT has shown potential in solving insight problems, thus highlighting the importance of incorporating AI into psychological research. However, it is acknowledged that there are still open challenges. Indeed, further research is required to fully understand AI's capabilities and limitations in verbal problem-solving.

Objective: Prior research has suggested a possible connection between vaccination and manifestations of Sickness Behavior; however, a need remains to first delve deeper into this association and second examine how Interoceptive Awareness and emotional factors may modulate individuals’ perceptions of their health status post vaccination. Method: An online retrospective cross-sectional survey of 647 individuals who received a COVID-19 vaccination was conducted. Together with vaccination side effects, socio-demographic characteristics, health status, level of concern about vaccination, and Interoceptive Awareness were collected at the baseline level. Mood, sleep, and Sickness Behavior were assessed at baseline and after vaccination. Data were analyzed using inferential statistics and machine learning techniques. Results: After vaccination, there was a significant increase in Sickness Behavior levels (mean (±SD) SicknessQ T0 = 1.57 (±2.72), mean (±SD) SicknessQ T1 = 5.54 (±5.51); p-value = 0.001; ES = 0.77). A Machine Learning analysis revealed specific patterns of individual dispositions (sex and age), baseline emotional characteristics (levels of depression, anxiety, stress, and concern about adverse reactions), as well as some components of Interoceptive Awareness (Noticing, Body Listening, and Attention Regulation), as predictors of high levels of Sickness Behavior, both in terms of overall scores (JRIP: 72.65% accuracy, AUC = 0.692, d = 0.709; F1 = 0.726) and individual items (JRIP: 75.77% accuracy, AUC = 0.694; d = 0.717; F1 = 0.754). Conclusions: Our results provide new insight into post-immune reactions by highlighting the contribution of Interoceptive Awareness in modulating the severity of Sickness Behavior. This sheds light on the role of awareness of bodily sensations in modulating perceptions of health status, helping to identify the characteristics that make individuals more prone to feeling sick.

IntroductionObsessive-compulsive disorder (OCD) is characterized by recurrent distressing thoughts and repetitive behaviors, or mental rituals performed to reduce anxiety. Recent neurobiological techniques have been particularly convincing in suggesting that cortico-striatal-thalamic-cortico (CSTC) circuits, including orbitofrontal cortex (OFC) and striatum regions (caudate nucleus and putamen), are responsible for mediation of OCD symptoms. However, it is still unclear how these regions are affected by OCD treatments in adult patients. To address this yet open question, we conducted a systematic review of all studies examining neurobiological changes before and after first-line psychological OCD treatment, i.e., cognitive-behavioral therapy (CBT).MethodsStudies were included if they were conducted in adults with OCD and they assessed the neurobiological effects of CBT before and after treatment. Two databases were searched: PsycINFO and PubMed for the time frame up to May 2022.ResultsWe obtained 26 pre-post CBT treatment studies performed using different neurobiological techniques, namely functional magnetic resonance imaging (fMRI), Positron emission tomography (PET), regional cerebral blood flow (rCBF), 5-HT concentration, magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), Electroencephalography (EEG). Neurobiological data show the following after CBT intervention: (i) reduced activations in OFC across fMRI, EEG, and rCBF; (ii) decreased activity in striatum regions across fMRI, rCBF, PET, and MRI; (iii) increased activations in cerebellum (CER) across fMRI and MRI; (iv) enhanced neurochemical concentrations in MRS studies in OFC, anterior cingulate cortex (ACC) and striatum regions. Most of these neurobiological changes are also accompanied by an improvement in symptom severity as assessed by a reduction in the Y-BOCS scores.ConclusionCognitive-behavioral therapy seems to be able to restructure, modify, and transform the neurobiological component of OCD, in addition to the clinical symptoms. Nevertheless, further studies are necessary to frame the OCD spectrum in a dimensional way.

Psychological inflexibility is related to depressive symptoms through the ‘ruminative response style’ (RR) and ‘cognitive fusion’ (CF). We aimed at exploring whether university students were more exposed to CF, RR and depressive symptoms because of their intellectual performance than non-university students of the same age. We compared university students (US) (n = 105) vs. non-university students (NUS) (n = 76) through online administration of the ‘Cognitive Fusion Questionnaire’ (CFQ-7), the ‘Depression-Zung Self-Assessment Scale’ (ZSDS) and the ‘Perseverative Thinking Questionnaire’ (PTQ) (study protocol #0077818/2022, approved by the Ethical Committee of the University of Pisa, Italy). University students scored significantly higher than non-university students in the CFQ-7 Total Score (27.5 ± 9.4 vs. 24.4 ± 9.5; p = 0.040), ZSDS Total Score (41.1 ± 7.7 vs. 39.0 ± 7.3; p = 0.031), PTQ Total Score (26.1 ± 13.1 vs. 21.8 ± 13.9; p = 0.029), PTQ ‘Repetitiveness’ (5.3 ± 2.8 vs. 4.5 ± 2.9; p = 0.034), ‘Intrusiveness’ (5.8 ± 3.0 vs. 4.8 ± 3.1; p = 0.046) and ‘Repetitive Negative Thinking capturing mental resources’ (5.0 ± 3.1 vs. 4.0 ± 3.0; p = 0.013) (MANOVA analysis). In a binary logistic regression analysis of US (with ZSDS scores < 44 vs. ≥44 as the dependent variable, and PTQ Total Score and dimensions, CFQ-7 Total Score, age and gender as the covariates), PTQ Total Score predicted the more severe depressive symptomatology (OR = 1.44, 95% CI: 1.017–2.039; p = 0.040). We believe that RR and CF should be specifically targeted through psychoeducational/psychotherapeutic interventions in university students.

The temporal-parietal junction (TPJ) is a key structure for the embodiment, term referred to as the sense of being localized within one's physical body and is a fundamental aspect of the self. On the contrary, the sense of disembodiment, an alteration of one's sense of self or the sense of being localized out of one's physical body, is a prominent feature in specific dissociative disorders, namely depersonalization/derealization disorders (DPD). The aims of the study were to provide: 1) a qualitative synthesis of the effect of Transcranial Magnetic Stimulation (TMS), taking into account its use for therapeutic and experimental purposes; 2) a better understanding on whether the use of TMS could support the treatment of DPD and other clinical conditions in which depersonalization and derealization are displayed. To identify suitable publications, an online search of the PubMed, Cochrane Library, Web of science and Scopus databases was performed using relevant search terms. In addition, an in-depth search was performed by screening review articles and the references section of each included articles. Our search yielded a total of 108 records through multiple databases searching and one additional record was identified through other sources. After duplicates removal, b and abstract reading, we retained 16 records for the assessment of eligibility. According to our inclusion criteria, we retained 8 studies. The selected studies showed that TMS targeting the TPJ is a promising technique for treating disembodiment phenomena DPD and for inducing reversible disembodiment states in healthy subjects. These data represent the first step towards a greater understanding of possible treatments to be used in disembodiment disorders. The use of TMS over the TPJ appears to be promising for treating disembodiment phenomena.

Objective The aim of the present study was to investigate whether patients with Parkinson’s Disease (PD) had changes in their level of performance in extra-dimensional shifting by implementing a novel analysis method, utilizing the new alternate phonemic/semantic fluency test. Method We used machine learning (ML) in order to develop high accuracy classification between PD patients with high and low scores in the alternate fluency test. Results The models developed resulted to be accurate in such classification in a range between 80% and 90%. The predictor which demonstrated maximum efficiency in classifying the participants as low or high performers was the semantic fluency test. The optimal cut-off of a decision rule based on this test yielded an accuracy of 86.96%. Following the removal of the semantic fluency test from the system, the parameter which best contributed to the classification was the phonemic fluency test. The best cut-offs were identified and the decision rule yielded an overall accuracy of 80.43%. Lastly, in order to evaluate the classification accuracy based on the shifting index, the best cut-offs based on an optimal single rule yielded an overall accuracy of 83.69%. Conclusion We found that ML analysis of semantic and phonemic verbal fluency may be used to identify simple rules with high accuracy and good out of sample generalization, allowing the detection of executive deficits in patients with PD.

Faking in a psychological test is often observed whenever an examinee may gain an advantage from it. Although techniques are available to identify a faker, they cannot identify the specific questions distorted by faking. This work evaluates the effectiveness of term frequency-inverse document frequency (TF-IDF)-an information retrieval mathematical tool used in search engines and language representations-in identifying single-item faked responses. We validated the technique on three datasets containing responses to the 10-item Big Five questionnaire (total of 694 participants, respectively 221, 243, and 230) in three faking situations. Each participant responded twice, once faking to achieve an objective in one of three contexts (one to obtain child custody and two to land a job) and once honestly. The proposed TF-IDF model has proven very effective in separating honest from dishonest responses-with the honest ones having low TF-IDF values and the dishonest ones having higher values-and in identifying which of the 10 responses to the questionnaire were distorted in the dishonest condition. We also provide examples of the technique in a single-case evaluation.

Considering the growing interest in the possible effects of internet’s addiction on adoles-cent’s mental health, this study aimed at exploring the psychological correlates of social media and internet problematic use during the first year of the covid-19 pandemic. A cross-sectional study was conducted in a sample of secondary school students (N = 258); participants were asked to complete an online survey, investigating social media addiction (BSMAS), self-esteem (RSES), feelings of isolation (CSIQ-A) and anxiety (STAI-Y). Data analysis (descriptive statistics, correlational and regression analyses) was conducted through XLSTAT software ©. An additional ad hoc questionnaire was administrated. Findings showed that the 11% of the participants were significantly addicted to social media, mostly females (59%). Gender represented an exposure factor for the hours spent on social media and the checking activity while performing other daily activities. Significant correlations emerged between the self-report measure of social media addiction and self-esteem and anxiety. Low scores at RSES corresponded to higher checking activity, hours spent on social networks, and playing videogames that were investigated as supplementary indicators of addiction with ad hoc questionnaire. The regression analysis showed just two predictors of social media addiction, gender (female) and trait anxiety. Limitations and implications of the study were argued in order to give some indications for future programs.

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).

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.

Pandemics and government-mandated quarantining measures have a substantial impact on mental health. This study investigated the psychological impact of the coronavirus disease 2019 (COVID-19) crisis on Italian residents during the first week of government-imposed lockdown and the role of defense mechanisms as protective factors against distress. In this cross-sectional study, 5,683 Italians responded to an online survey assessing socio-demographics, overall psychological distress, post-traumatic symptoms, and defense mechanisms using validated measures as the Symptom Checklist-90 (SCL-90), the Impact of Event Scale-Revised (IES-R), and the Defense Mechanisms Rating Scale-Self-Report-30 (DMRS-SR-30). Data were collected from March 13 to March 18, within the first week of lockdown in Italy. Results showed that younger age and female gender were associated with increased psychological distress. Having positive cases nearby, more days on lockdown, and having to relocate were also associated with greater distress. Higher overall defensive functioning (ODF) was associated with lower levels of depression (r = −.44, 95% CI −0.48, −0.40), anxiety (r = −.38, 95% CI −0.42, −0.35), and post-traumatic stress symptoms (PTSS) (r = −.34, 95% CI −0.38, −0.30). Conversely, less adaptive defensive functioning was related to greater affective distress across all domains. Each increased unit of ODF decreased the chances of developing post-traumatic stress symptoms (PTSS) by 71% (odds ratio = 0.29, p < 0.001, 95% CI.026,.032). The psychological impact of COVID-19 among Italians during the early weeks of government lockdown has been significant. The pandemic continues to have extraordinary mental health impact as it moves across the globe. Given the salience of defensive functioning in psychological distress, consideration of interventions that foster the use of more adaptive defenses may be an important component of building resilience amidst a pandemic.

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.

Objectives: Stressful life events (SLEs) are common in patients who developed both physical and psychological syndromes. Research shown the role of psychological defense mechanisms in cancer progression and survival probability. The present study analyzed recent SLEs and defense mechanisms as characteristic of cancer patients and tested their role as potential predisposing factors to cancer development. Methods: This cross-sectional study enrolled 145 participants: 48 recently diagnosed cancer patients (CP), 43 recently diagnosed benign tumor patients (BT), and 54 healthy subjects (HC). Non-blinded raters assessed participants’ defense mechanisms using the Defense Mechanisms Rating Scales Q-sort version (DMRS-Q). Groups were compared on the presence of SLEs and on the maturity of defensive functioning. Significant associations between SLE and defense mechanisms as related to cancer diagnosis were explored. Results: Higher overall defensive functioning was associated with good physical conditions. Recent SLEs, higher use of neurotic defenses and lower use of obsessional defenses characterized cancer patients. CP showed higher use of suppression, repression, dissociation, rationalization and passive aggression and lower use of affiliation, sublimation, undoing, and devaluation of self-image as compared to controls. Hierarchical regression analysis showed that recent SLEs and defense mechanisms of suppression, repression, dissociation, displacement and omnipotence were associated with cancer diagnosis. Discussion: Recent SLEs and repressive defensive functioning characterized the CP’s defensive response to stress. Despite the relevance of present findings, this study shows several limitations. Prospective and longitudinal studies are needed to confirm these results and to investigate the potential role played by SLEs and defense mechanisms in cancer development.

Phantom limb phenomena are often reported by patients after the amputation or deafferentation of limbs. The most debilitating condition is named phantom limb pain and, to present, an efficacious treatment is not been identify, yet. However, alternative non-invasive treatments such as transcranial direct current stimulation could be a valid approach in order to modulate pain. Based on the studies considered, tDCS might represent a potential novel tool able to reduce phantom limb symptoms. Due to the limited number of investigations, the positive outcomes summarized in this letter to editor need to be interpreted with caution.

Interrogative suggestibility (IS) describes the extent to which an individual behavioral response is affected by messages communicated during formal questioning within a closed social interaction. The present study aimed at improving knowledge about IS in the elderly (aged 65 years and older), in particular about its association with both emotive/affective and cognitive variables. The sample (N = 172) was divided into three groups on the basis of age: late adult (aged 55-64, N = 59), young elderly (aged 65-74, N = 63), and elderly (aged 75 and older, N = 50). Cognitive (i.e., Kaufman Brief Intelligence Test-2, Rey Auditory Verbal Learning Test), emotive/affective (i.e., Rosenberg Self-Esteem Scale, Marlowe-Crowne Social Desirability Scale, Penn State Worry Questionnaire) and suggestibility measures (i.e., Gudjonsson Suggestibility Scale-2) were administered. In order to identify differences and associations between groups in IS, cognitive and emotive/affective variables, ANOVAs tests and Pearson's correlations were run. Furthermore, moderation analyses and hierarchical regression were set to determine whether age, cognitive and emotive/affective variables predicted IS components (i.e., Yield and Shift). Finally, machine learning models were developed to highlight the best strategy for classifying elderly subjects with high suggestibility. The results corroborated the significant link between IS and age, showing that elderly participants had the worst performance on all suggestibility indexes. Age was also the most important predictor of both Yield and Shift. Results also confirmed the important role of non-verbal intelligence and memory impairment in explaining IS dimensions, showing that these associations were stronger in young elderly and elderly groups. Implications about interrogative procedures with older adults were discussed.

Here, we report an investigation on the accuracy of the Toronto Alexithymia Scale, a measure to assess alexithymia, a multidimensional construct often associate to fibromyalgia. Two groups of participants, patients with fibromyalgia (n = 38), healthy controls (n = 38) were administered the Toronto Alexithymia Scale and background tests. Machine learning models achieved an overall accuracy higher than 80% in detecting both patients with fibromyalgia and healthy controls. The parameter which alone has demonstrated maximum efficiency in classifying the single subject within the two groups has been the item 3 of the alexithymia scale. The analysis of the most informative features, based on all scales administered, revealed that item 3 and 13 of the alexithymia questionnaire and the visual analog scale scores were the most informative attributes in correctly classifying participants (accuracy above 85%). An additional analyses using only the alexithymia scale subset of items and the visual analog scale scores has shown that the predictors which efficiently classified patients with fibromyalgia and controls were the item 3 and 7 (accuracy = 85.53%). Our findings suggest that machine learning models analysis based on the Toronto Alexithymia Scale subset of items scores accurately distinguish patients with fibromyalgia from healthy controls.

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.

Background and Purpose: Fibromyalgia (FM) is a very complex chronic pain condition, characterized by widespread musculoskeletal pain, fatigue and numerous other physical and psychological symptoms, such as insomnia, morning stiffness, anxiety, depressive disorders and cognitive dysfunctions. Most women with FM are also between the ages of 40 to 55 years old, around the pre‐menopausal and menopausal period. As FM, Menopause (MP) is also associated with physical and psychological symptoms, being the end of ovarian activity, whose decline produces a series of trophic, metabolic, psychological and sexual consequences, altering the homeostatic balance of the woman. The aim of the study was to investigate whether both of the treated syndromes constitute a single nosological entity, or two distinct nosological entities and the most accredited hypothesis. Methods: Through the analysis of the scientific literature, FM and MP have been analyzed using the predicted research objective as a guiding criterion. It was considered useful to analyze the two syndromes separately and then to identify a criterion of interdependence between them. The study was conducted through the use of some keywords included in the main scientific search databases. Results and conclusions: Our research highlight the potential menopausal influence on FM, given that the progressive depletion of ovarian activity constitutes the pathophysiological substratum of profound hormonal changes, with implications not only in terms of somatic symptoms, but also in terms of psychological distress. Furthermore, the lack of scientific evidence about psychological correlations between the two syndromes provides an interesting starting point for future research.

Background: Emerging aspects of the Covid-19 clinical presentation are its long-term effects, which are characteristic of the so-called “long COVID”. The aim of the present study was to investigate the prevalence of physical, psychological, and sleep disturbances and the quality of life in the general population during the ongoing pandemic. Methods: This study, based on an online survey, collected demographic data, information related to COVID-19, sleep disturbances, and quality of life data from 507 individuals. The level of sleep disturbances and quality of life was assessed through the Insomnia Severity Index (ISI) and the EuroQol-5D (EQ-5D), respectively. Results: In total, 507 individuals (M = 91 and F = 416 women) completed the online survey. The main symptoms associated with “long COVID” were headache, fatigue, muscle aches/myalgia, articular pains, cognitive impairment, loss of concentration, and loss of smell. Additionally, the subjects showed significant levels of insomnia (p < 0.05) and an overall reduced quality of life (p < 0.05). Conclusions: The results of the study appear in line with recent publications, but uncertainty regarding the definition and specific features of “long COVID” remains. Further studies are needed in order to better define the clinical presentation of the “long COVID” condition and related targeted treatments.

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.

Background and Purpose. The use of machine learning (ML) models in the detection of malingering has yielded encouraging results, showing promising accuracy levels. We investigated the possible application of this methodology when trained on behavioral features, such as response time (RT) and time pressure, to identify faking behavior in self-report personality questionnaires. To do so, we reintroduced the article of Roma et al. (2018), which highlighted that RTs and time pressure are useful variables in the detection of faking; we then extended the number of participants and applied an ML analysis.Materials and Methods. The sample was composed of 175 subjects, of whom all were graduates (having completed at least 17 years of instruction), male, and Caucasian. Subjects were randomly assigned to four groups: honest speeded, faking-good speeded, honest unspeeded, and faking-good unspeeded. A software version of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) was administered.Results. Results indicated that ML algorithms reached very high accuracies (around 95%) in detecting malingerers when subjects are instructed to respond under time pressure. The classifiers’ performance was lower when the subjects responded with no time restriction to the MMPI-2-RF items, with accuracies ranging from 75% to 85%. Further analysis demonstrated that T-scores of validity scales are ineffective to detect fakers when participants were not under temporal pressure (accuracies 55–65%), whereas temporal features resulted to be more useful (accuracies 70–75%). By contrast, temporal features and T-scores of validity scales are equally effective in detecting fakers when subjects are under time pressure (accuracies higher than 90%).Discussion. To conclude, results demonstrated that ML techniques are extremely valuable and reach high performance in detecting fakers in self-report personality questionnaires over more the traditional psychometric techniques. Validity scales MMPI-2-RF manual criteria are very poor in identifying under-reported profiles. Moreover, temporal measures are useful tools in distinguishing honest from dishonest responders, especially in a no time pressure condition. Indeed, time pressure brings out malingerers in clearer way than does no time pressure condition.

The nature of amnesia in the context of crime has been the subject of a prolonged debate. It is not uncommon that after committing a violent crime, the offender either does not have any memory of the event or recalls it with some gaps in its recollection. A number of studies have been conducted in order to differentiate between simulated and genuine amnesia. The recognition of probable malingering requires several inferential methods. For instance, it typically involves the defendant’s medical records, self-reports, the observed behavior, and the results of a comprehensive neuropsychological examination. In addition, a variety of procedures that may detect very specific malingered amnesia in crime have been developed. In this paper, we investigated the efficacy of three techniques, facial thermography, kinematic analysis, and symptom validity testing in detecting malingering of amnesia in crime. Participants were randomly assigned to two different experimental conditions: a group was instructed to simulate amnesia after a mock homicide, and a second group was simply asked to behave honestly after committing the mock homicide. The outcomes show that kinematic analysis and symptom validity testing achieve significant accuracy in detecting feigned amnesia, while thermal imaging does not provide converging evidence. Results are encouraging and may provide a first step towards the application of these procedures in a multimethod approach on crime-specific cases of amnesia.

In the context of legal damage evaluations, evaluees may exaggerate or simulate symptoms in an attempt to obtain greater economic compensation. To date, practitioners and researchers have focused on detecting malingering behavior as an exclusively unitary construct. However, we argue that there are two types of inconsistent behavior that speak to possible malingering-accentuating (i.e., exaggerating symptoms that are actually experienced) and simulating (i.e., fabricating symptoms entirely)-each with its own unique attributes; thus, it is necessary to distinguish between them. The aim of the present study was to identify objective indicators to differentiate symptom accentuators from symptom producers and consistent participants. We analyzed the Structured Inventory of Malingered Symptomatology scales and the Minnesota Multiphasic Personality Inventory-2 Restructured Form validity scales of 132 individuals with a diagnosed adjustment disorder with mixed anxiety and depressed mood who had undergone assessment for psychiatric/psychological damage. The results indicated that the SIMS Total Score, Neurologic Impairment and Low Intelligence scales and the MMPI-2-RF Infrequent Responses (F-r) and Response Bias (RBS) scales successfully discriminated among symptom accentuators, symptom producers, and consistent participants. Machine learning analysis was used to identify the most efficient parameter for classifying these three groups, recognizing the SIMS Total Score as the best indicator.