Data-peeking, the practice of stopping data collection once significant results are obtained, poses a significant threat to the credibility of research by increasing the likelihood of observing and publishing false positive results. The effect of this practice on scientific reliability has traditionally been assessed through Monte-Carlo simulations, which suffer from some methodological issues (e.g., Monte–Carlo standard error is often overestimated). This paper presents a computational model that, through a series of convolutions between normal and truncated normal distributions, estimates the increase in false-positive rates due to data-peeking practice, overcoming the issues of the simulation method. The performance of the computational model was compared against Monte-Carlo simulations under different conditions, including alpha levels (0.05, 0.025, 0.01, and 0.001), sample sizes (40, 80, 160, and 320), and numbers of interim analyses (1 to 10). The results confirmed the computational model's accuracy and reliability, showing that computational values fell within two standard deviations of the simulated means. In conclusion, the proposed model not only presents an faster and error-reducing approach to estimating the effects of data-peeking practice but also provides guidelines for the development of similar models to examine other forms of p-hacking, thereby improving the understanding and study of their effects.

The Adult Attachment Trauma Questionnaire (AATQ) is a newly developed measure to assess Attachment Trauma (AT) in adult romantic relationships. This study builds upon the study of validation of the questionanire to further explore the structure and performance of the AATQ in a new general sample of 345 adults through a mixed data analysis strategy. A Confirmatory Factor Analysis (CFA), an invariance analysis based on sex and a Latent Profile Analysis (LPA) of subscale scores were conducted. The CFA revealed that the best model was a six-factor structure nested within a higher-order factor representing general AT. Invariance testing demonstrated that the AATQ is robust across sex, showing configural, metric, and scalar invariance. The LPA identified three latent classes of AT—High, Medium, and Low. Detailed discussions are provided on the performance of the subscales. In summary, the AATQ has proven to be a robust and stable questionnaire.

Despite the wide use of the concept of Attachment Trauma (AT) in clinics and research, there is a scarcity of proper assessment tools. This work aims to fill this gap by outlining the development and validation of the Adult Attachment Trauma Questionnaire (AATQ). The preliminary item pool covered eight conceptual domains: Conflict, Attachment-related Dissociation, Attachment Phobia, Victim, Compulsive Savior, Dangerous Self, Revengeful Perpetrator, and Sadistic Perpetrator. The items were analyzed through two pilot studies. After revising the items according to the results, the final pool underwent factorial, convergent, and incremental validity analyses in a general sample of 407 Italian adults. The EFA supported the eight-factor structure. Validity analyses demonstrated excellent convergent and predictive validity across all questionnaire subscales, except for two of them (Compulsive Savior, Sadistic Perpetrator) that did not exhibit good performance and were subsequently removed. The AATQ final version consisted of six subscales and 28 total items and was confirmed in a further EFA. The AATQ performed better than a popular fearful/disorganized attachment measure (Adult Disorganized Attachment scale) and a standard measure of adult attachment (Emotions in Close Relationships) in predicting traumatic and pathological outcomes.

Dealing with mathematics can induce significant anxiety, affecting academic performance: this phenomenon is known as Math Anxiety (MA). While math anxiety scales were mostly developed in English, some have been translated and validated for Italian populations (e.g., the Abbreviated Math Anxiety Scale). This study translated the 3-factor MAS-UK scale into Italian, producing a new tool, MAS-IT, which was validated in a sample of 324 Italian psychology undergraduates. Confirmatory Factor Analysis (CFA) tested the original MAS-UK 3-factor model and revealed that it did not fit the MAS-IT data. A subsequent Exploratory Graph Analysis (EGA) identified four distinct factors of math anxiety in MAS-IT. The “Passive Observation MA” factor remained stable across the analyses, whereas the “Evaluation MA” and “Everyday/Social MA” items showed poor stability. These quantitative findings suggest potential cultural or contextual differences in the expression of math anxiety among today’s psychology undergraduates, highlighting the need for more appropriate assessment tools tailored to this population.

Aberrant respondents are common but yet extremely detrimental to the quality of social surveys or questionnaires. Recently, factor mixture models have been employed to identify individuals providing deceptive or careless responses. We propose a comprehensive factor mixture model that combines confirmatory and exploratory factor models to represent both the non-aberrant and aberrant components of the responses. The flexibility of the proposed solution allows for the identification of two of the most common aberant response styles, namely faking and careless responding. We validated our approach by means of two simulations and two case studies. The results indicate the effectiveness of the proposed model in handling with aberrant responses in social and behavioral surveys.

The increasing popularity of the Bayesian approach in Psychology has prompted metascientific efforts to quantify its prevalence. However, despite enduring debates between proponents of Frequentist and Bayesian schools of thought, no systematic comparison of their prominence has been conducted in existing literature. This brief report fills this gap, examining Bayesian and Frequentist trends in the period from 1964 to 2023 through a meticulous search in PsycINFO. The findings reveal that the Frequentist approach has consistently been more popular than the Bayesian approach in the realm of Psychometrics and Statistical Psychology. However, Bayesian contributions steadily increased from the 80’s onward and appear to be almost as important or even surpassing the Frequentist counterparts in the latest years investigated (2019–2023). Although this observation applies primarily to specialized literature rather than the entire domain of Psychology, it underscores the growing prevalence of the Bayesian approach, signaling attention among specialists in the field.

The current paper aimed to analyze the trends and prominence of four major schools of thought in scientific Psychology (neuroscience, cognitivism, behaviorism, psychoanalysis) and their intersections, covering the period from 1979 to 2020. We utilized an improved methodology across three distinct sources: mainstream psychology, highly influential journals, and non-English papers. Our findings align with previous research in several aspects: a) psychoanalysis and behaviorism have significantly declined, b) cognitivism remains a prominent trend. However, we deviate from prior studies in the following ways: c) neuroscience may be considered the most influential trend, d) trends exhibit more stability and dispersion than previously assumed, and e) relative stability is observed in psychological studies over the past four decades. We also acknowledge the significance of non-English papers (NEP), which manifest an independent pattern as compared to the other sources and that may reveal what happens in the “periphery” of Psychology. It is noteworthy NEP demonstrated a still lively contribution from psychoanalysis. Overall, our study supports the inference that scientific Psychology is a non-paradigmatic or pre-paradigmatic discipline. It also supports the dominance of applied psychology, confuting the notion of overarching "grand theories." It eventually highlights the insularity and lack of cross-fertilization among psychological subdisciplines, despite the widespread claims to the contrary.

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. Here, we investigate perceptions of math and STEM fields provided by cutting-edge language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach from network science and cognitive psychology. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have an overall negative perception of math and STEM fields, with math being perceived most negatively. We observe significant differences across the three LLMs. We observe that newer versions (i.e. GPT-4) produce richer, more complex perceptions as well as less negative perceptions compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.

Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI’s models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.

A psychometric scale reports experiences in terms of items/sentences rated by individuals. We investigate whether psychometric item ratings reflect semantic/syntactic associations between concepts in items. To this aim, we introduce semantic loadings as a semantic counterpart of psychometric factors, i.e. clusters of items obtained by correlations between item ratings. Semantic loadings quantify how clusters of semantically related concepts, as expressed in the texts of items, are allocated across psychometric factors as identified by ratings. As a case study, we focus on 39775 individual responses to the Depression Anxiety and Stress Scale (DASS) with 42 items on a 4-point Likert scale. To identify communities of semantically related concepts, we exploit the cognitive network framework of Textual Forma Mentis Networks (TFMNs), which reconstruct semantic/syntactic links encoded in the texts of items (e.g. "feel" and "sad" in the item "I usually feel sad"). To identify factors we compare eigenvector-based exploratory analysis with Graph Exploratory Analysis (EGA), which can both cluster items (and their texts) according to user ratings. We find that EGA is better at reconstructing the psychological organisation of DASS along the dimensions of anxiety, stress and depression. Following dual coding theory and the Deep Lexical Hypothesis, we posit that the act of reading items activates interconnected concepts and this influences user ratings and their expressed psychological constructs. Our results show a quantitative match: TFMN-based semantic loadings can identify specific aspects of emotional dysregulation, emotional exhaustion, physical distress and tension states of EGA-based psychometric factors, in non-random ways (up to $p<0.001$). We discuss our results in view of relevant mental distress literature, psychometric scale designing and links with episodic and semantic memories.

In this contribution we provide initial findings to the problem of modeling fuzzy rating responses in a psychometric modeling context. In particular, we study a probabilistic tree model with the aim of representing the stage-wise mechanisms of direct fuzzy rating scales. A Multinomial model coupled with a mixture of Binomial distributions is adopted to model the parameters of LR-type fuzzy responses whereas a binary decision tree is used for the stage-wise rating mechanism. Parameter estimation is performed via marginal maximum likelihood approach whereas the characteristics of the proposed model are evaluated by means of an application to a real dataset.

Modeling fuzziness and imprecision in human rating data is a crucial problem in many research areas, including applied statistics, behavioral, social, and health sciences. Because of the interplay between cognitive, affective, and contextual factors, the process of answering survey questions is a complex task, which can barely be captured by standard (crisp) rating responses. Fuzzy rating scales have progressively been adopted to overcome some of the limitations of standard rating scales, including their inability to disentangle decision uncertainty from individual responses. The aim of this article is to provide a novel fuzzy scaling procedure which uses Item Response Theory trees (IRTrees) as a psychometric model for the stage-wise latent response process. In so doing, fuzziness of rating data is modeled using the overall rater's pattern of responses instead of being computed using a single-item based approach. This offers a consistent system for interpreting fuzziness in terms of individual-based decision uncertainty. A simulation study and two empirical applications are adopted to assess the characteristics of the proposed model and provide converging results about its effectiveness in modeling fuzziness and imprecision in rating data.

Modeling human ratings data subject to raters' decision uncertainty is an attractive problem in applied statistics. In view of the complex interplay between emotion and decision making in rating processes, final raters' choices seldom reflect the true underlying raters' responses. Rather, they are imprecisely observed in the sense that they are subject to a non-random component of uncertainty, namely the decision uncertainty. The purpose of this article is to illustrate a statistical approach to analyse ratings data which integrates both random and non-random components of the rating process. In particular, beta fuzzy numbers are used to model raters' non-random decision uncertainty and a variable dispersion beta linear model is instead adopted to model the random counterpart of rating responses. The main idea is to quantify characteristics of latent and non-fuzzy rating responses by means of random observations subject to fuzziness. To do so, a fuzzy version of the Expectation-Maximization algorithm is adopted to both estimate model's parameters and compute their standard errors. Finally, the characteristics of the proposed fuzzy beta model are investigated by means of a simulation study as well as two case studies from behavioral and social contexts.

Background Recent research postulated that organizational identification plays an important role in employees' health and well-being. Building on the Social Identity Approach as a framework, we test the so-called social cure hypothesis, according to which group-based processes of social support should reduce employees' psychological distress. Design and Methods While there is a considerable amount of cross-sectional evidence concerning the positive role played by organizational identification in this dynamic, there is a lack of full panel studies. This study tries to fill this gap by using data from a sample of technical and administrative staff of a University in Italy at three time points (N = 96). Data were analyzed using Autoregressive Cross-Lagged Panel models. Results We found support for the hypothesized longitudinal mediational model. Specifically, strongly identified employees tend to receive more social support, and this in turn reduces psychological distress over time. Conclusions This study is the first test of the social cure hypothesis in an organizational context that uses a panel study design. We discuss the theoretical and practical implications for management.

When localising sounds in space the brain relies on internal models that specify the correspondence between the auditory input reaching the ears, initial head-position and coordinates in external space. These models can be updated throughout life, setting the basis for re-learning spatial hearing abilities in adulthood. In addition, strategic behavioural adjustments allow people to quickly adapt to atypical listening situations. Until recently, the potential role of dynamic listening, involving head-movements or reaching to sounds, have remained largely overlooked. Here, we exploited visual virtual reality (VR) and real-time kinematic tracking, to study the role of active multisensory-motor interactions when hearing individuals adapt to altered binaural cues (one ear plugged and muffed). Participants were immersed in a VR scenario showing 17 virtual speakers at ear-level. In each trial, they heard a sound delivered from a real speaker aligned with one of the virtual ones and were instructed to either reach-to-touch the perceived sound source (Reaching group), or read the label associated with the speaker (Naming group). Participants were free to move their heads during the task and received audio-visual feedback on their performance. Most importantly, they performed the task under binaural or monaural listening. Results show that both groups adapted rapidly to monaural listening, improving sound localisation performance across trials and changing their head-movement behaviour. Reaching the sounds induced faster and larger sound localisation improvements, compared to just naming its position. This benefit was linked to progressively wider head-movements to explore auditory space, selectively in the Reaching group. In conclusion, reaching to sounds in an immersive visual VR context proved most effective for adapting to altered binaural listening. Head-movements played an important role in adaptation, pointing to the importance of dynamic listening when implementing training protocols for improving spatial hearing.

Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information. In the present work we proposed an architecture for jointly modelling the reciprocal relation between behavioural and neural information in the context of risky decision-making. More precisely, we offered a way to relate Diffusion Tensor Imaging data to cognitive parameters of a computational model accounting for behavioural outcomes in the popular Balloon Analogue Risk Task (BART). Results show that the proposed architecture has the potential to account for individual differences in task performances and brain structural features by letting individual-level parameters to be modelled by a joint distribution connecting both sources of information. Such a joint modelling framework can offer interesting insights in the development of computational models able to investigate correspondence between decision-making and brain structural connectivity.

Research on enumeration with isolated objects has indicated that young and older adults can report up to three elements with similar efficiency (subitizing effect). Recent studies on subitizing in young adults have shown that individuation occurs over parts of an object as efficiently as over physically disconnected objects, suggesting that spatial separation is a sufficient requirement for efficient individuation. Do young and older adults share this sufficient requirement? In two experiments, we tested for the presence of subitizing in an enumeration task with a varying number of distinct objects and object parts. In Experiment 1, results indicated the presence of a bilinear function (with an inflection point between 3 and 4 elements, a proxy for subitizing) in the response speed of young and older adults, and in both stimulus conditions. In addition, the enumeration slope in older participants was steeper for object parts than for objects in the subitizing range, possibly due to perceptual degradation (e.g., in contour detection). The pattern found generalizes to other stimuli (Experiment 2), thus highlighting the robustness of the present findings. Overall, the results indicate that while some perceptual factors (such as contour detection or curvature polarity) may hamper subitizing speed of older individuals relative to young adults, the subitizing span remains at approximately three to four elements for multiple objects and object parts in both young and older adults. Thus, individuation of multiple objects and object parts is a mechanism relatively resistant to aging.

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task’s structure, the received feedback, and the agent’s behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.