Center for Neuroscience and Cognitive Systems

• PA12 fatigue life characterization with Average Strain Energy Density (ASED) method. • An algorithm is developed for lattice structure fatigue predictions. • Fatigue predictions are conducted on as-designed lattice geometries. • The critical radius for the ASED method is identified for MJF PA12 material. The industrial sector continues to explore innovative strategies to exploit the full potential of Additive Manufacturing (AM). Among its many advantages, AM enables the fabrication of lattice structures; these are lightweight metamaterials with tunable mechanical properties and excellent energy absorption capabilities. Despite their promise, the widespread industrial use of such structures is limited by the difficulty in accurately assessing their fatigue behavior. This study presents a methodology aimed at predicting the fatigue life of polymer-based lattice components, with a specific focus on PA12 manufactured using the Multi Jet Fusion (MJF) process. This is an industrially relevant technology offering large production volumes, high printing quality and low production costs. The approach begins with fatigue testing of bulk PA12 specimens to establish baseline material behavior. Based on these results, a predictive algorithm is developed to estimate the fatigue performance of lattice structures. The model adopts an energy-based framework inspired by the Average Strain Energy Density (ASED) method, previously used for metallic materials, and adapts it to the characteristics of polymer lattices. The proposed methodology contributes to the development of efficient fatigue assessment tools, supporting the broader adoption of lattice structures in cost-sensitive industrial applications where polymer-based materials are effective.

Background/Objectives: Autism research has often interpreted autistic sociality through neurotypical norms, limiting ecological accounts of autistic meaning-making and context-sensitive support needs. Social virtual environments (SVEs), such as VRChat, allow modulation of sensory exposure, social distance, and participation pace, potentially enabling autistic-led interaction with greater autonomy and predictability. This study examined how autistic young adults co-construct meanings around social interaction, identity, and self-regulation in peer-led discussions within an SVE; identified context-sensitive processes relevant to well-being; and evaluated the feasibility and acceptability of SVEs as a participatory research setting. Methods: Sixteen autistic young adults (18-38 years; DSM-5-TR, Level 1) participated in nine remote sessions conducted in VRChat, coordinated via a co-designed Discord server. The peer-led discussions were audio-video recorded, transcribed, and anonymized. Data were analyzed using reflexive thematic analysis, combining inductive session-level coding, cross-session thematic clustering, and participatory refinement with community partners. Results: Autistic experience was framed as a context-dependent negotiation of interpretive risk, interactional workload, masking-related energy costs, and epistemic injustice, alongside future-oriented accounts emphasizing access, dignity, and systemic redesign. Observational memos documented multimodal participation, distributed peer facilitation, and accessibility-relevant sensitivities to environmental stability. Community partners reported positive experiences and supported the acceptability of private-world VRChat sessions. Conclusions: Peer-led discussions in an SVE can support ecologically grounded, participant-centered qualitative research, offering methodological opportunities to study autistic meaning-making under conditions that reduce demands and risks.

In this paper, we introduce the reader to the field of cognitive network science, that is, the application of network science methods to study human cognition and knowledge structures. Cognitive networks are representations of associative knowledge between concepts in a cognitive system apt at acquiring, storing, processing and producing language, that is, the mental lexicon. In a cognitive network, nodes represent concepts with links expressing relations, such as semantic, syntactic, phonological and visual connections, for example, "canine" and "dog" (nodes) linked by "being synonyms" (link). Hence, cognitive networks represent associative knowledge in mathematical, measurable and quantifiable ways. Can such structure be used to gain insights over cognitive phenomena? We explore this research question by reviewing recent, pioneering key applications and limitations of cognitive networks across visual, auditory, and semantic language processing tasks, either in healthy or clinical populations. We also review applications of cognitive networks modeling language acquisition, reconstructing text content and assessing creativity or personality traits in individuals. Our paper also gently introduces the reader to mathematical notations, definitions and measures about single-layer and multiplex networks as well as hypergraphs. Last but not least, across phonological, semantic and syntactic networks, we guide the reader through relevant psychological frameworks, datasets and software packages that might all aid current and future cognitive network scientists. This article is categorized under: Psychology > Memory Psychology > Theory and Methods Linguistics > Cognitive.

Humans, as social beings, may choose to be either truthful or deceptive to achieve personal benefits. Although deception and truth-telling are distinct, both rely on an individual's certainty about the world, shaping responses associated with honesty or lying. However, our surrounding environment is often characterized by a lack of certainty, and little is known about the psychophysiological responses insituations of uncertainty, where individuals lack confidence in whether their statements are truthful or deceptive. In this study, we implemented a within-subject design where thirty-two participants (mean age: 23.94; age range: 19-32; 17 females) were asked to persuade another person about the goodness of their performance in cognitively demanding tasks, while their self-confidence, electrodermal activity (EDA), and facial muscle activity were measured under three conditions: Deception, Uncertainty, and Truth. Participants reported lower self-confidence in convincing an interlocutor of their great results during Deception compared to Truth. Deceptive behavior was also associated with increased physiological arousal (trough-to-peak; TTP) compared to truth-telling. Crucially, when faced with uncertainty, participants exhibited behavioral and physiological responses that fell in between those of Deception and Truth, self-enhancing their performance while retaining residual characteristics of deception at the implicit level. We propose that these results could be framed within a continuum between truth and deception, where uncertainty can give rise to a form of partial deception, allowing individuals to enhance their self-confidence while reducing the social and physiological drawbacks of lying.

Laboratory medicine is a cornerstone of modern clinical practice, providing essential contributions to screening, diagnosis, prognosis and therapeutic management of human disease [...].

ABSTRACTBackgroundBD is a chronic, cyclical condition marked by severe mood instability and significant impairment. Although pharmacological and psychotherapeutic interventions have demonstrated efficacy, challenges remain in preventing relapse and sustaining long‐term euthymia. This study evaluates a CBT‐based intervention designed to reduce symptom severity and recurrence by targeting three core maintenance factors: intolerance of anergic states, addiction to mania, and meta‐emotional problems.MethodFour individuals diagnosed with BD participated in a one‐year CBT intervention tailored to address these key vulnerabilities. Assessments were conducted at baseline, post‐treatment, and at 18‐ and 24‐month follow‐ups, measuring depression, mania, quality of life, and personality functioning. Changes were evaluated with RCI and repeated measures ANOVA.ResultsAll four patients showed a marked reduction in depressive symptoms at the end of treatment and at the 24‐month follow‐up, with a large effect size (η2 = 0.83). Improvements were also observed in the quality of life and maladaptive personality traits over time. Notably, euthymia was maintained throughout the follow‐up period, with no recurrence of manic episodes or new affective cycles.ConclusionDespite limitations, these preliminary findings suggest that this three‐goal CBT intervention targeting core maintenance factors in bipolar disorder may promote sustained clinical improvements, increase long‐term stability, and reduce reliance on medication.