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Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain‐general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain‐general predictive network that encompasses all well‐established cognitive domains exists remains unanswered. The present meta‐analysis aims to address this gap by testing the hypothesis that PP relies on a large‐scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta‐analytic approach was employed, along with Meta‐Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena‐independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross‐domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large‐scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross‐domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta‐Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain‐general, large‐scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context‐ and stimulus‐dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world. [ABSTRACT FROM AUTHOR]

Introduction: Autobiographical memory is the capacity to recollect memories of personally experienced events. The detection of such memories plays a key role in criminal trials. Among behavioral memory-detection methods, the autobiographical Implicit Association Test (aIAT) has gained popularity for its flexibility and suitability for forensic applications. The aIAT is a reaction timebased methodology aiming to assess whether information about an event is encoded in the respondent's mind. Here, we introduced the eye-D index, a measure based on the topography of fixations while performing the aIAT, as an additional measure to detect autobiographical memories covertly. Methods: In this study, participants were involved in a mock-crime experiment in which they could act as Guilty or Innocent. One week later all participants underwent the aIAT combined with eye-tracking to investigate the presence of the crime-related memory. Results: Guilty participants showed a higher number of fixations towards the category labels in the block in which true sentences shared the same response key with crime-related sentences, as compared to the block in which true sentences were paired with sentences describing an alternative version. Innocent participants showed the opposite pattern. This unbalanced allocation of attention to the category labels was quantified by the eye-D index and was found to be highly correlated to the standard aIAT-D index. Discussion: This suggests that more fixations to the category labels could indicate increased cognitive load and monitoring of response conflicts. These preliminary results highlight eye-tracking as a tool to detect autobiographical memories covertly while performing the aIAT. [ABSTRACT FROM AUTHOR]

ObjectiveThis proof-of-concept paper provides evidence to support machine learning (ML) as a valid alternative to traditional psychometric techniques in the development of short forms of longer parent psychological tests. ML comprises a variety of feature selection techniques that can be efficiently applied to identify the set of items that best replicates the characteristics of the original test. MethodsIn the present study, we integrated a dataset of 329 participants from published and unpublished datasets used in previous research on the Structured Inventory of Malingered Symptomatology (SIMS) to develop a short version of the scale. The SIMS is a multi-axial self-report questionnaire and a highly efficient psychometric measure of symptom validity, which is frequently applied in forensic settings. Results State-of-the-art ML item selection techniques achieved a 72% reduction in length while capturing 92% of the variance of the original SIMS. The new SIMS short form now consists of 21 items. ConclusionsThe results suggest that the proposed ML-based item selection technique represents a promising alternative to standard psychometric correlation-based methods (i.e. item selection, item response theory), especially when selection techniques (e.g. wrapper) are employed that evaluate global, rather than local, item value. [ABSTRACT FROM AUTHOR]