Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder determined by a combination of both genetic and environmental factors. Despite wide investigations, the role of chronic exposure to environmental pollutants is still rather unknown. Among natural toxins, the mycotoxins have received major attention only in the last few years, due to both technical and scientific achievements that allowed to disentangle many important features of the complex fungal biology. Whereas the effects of acute and high-dose mycotoxin exposure are well known, the potential effects of chronic and low-dose exposure on neurodegeneration have not been broadly elucidated. In this review, we have summarized all the studies concerning environmental exposure to unknown substances that caused ALS outbreaks all over the world, reinterpreting in light of the new scientific acquisitions and highlighting the potential and neglected role of mycotoxins. Then, we focused on recent papers about food exposure to mycotoxin, mycobiome and fungal infections in ALS and other neurodegenerative diseases. We analyzed the gaps of current literature that lead to an undervaluation of mycotoxins as detrimental molecules. By listing all the most important mycotoxins and analyzing all the biological pathways that they can affect, we explained the reasons why they need to be considered in the next epidemiological studies on ALS and other neurodegenerative and neuroinflammatory diseases. In conclusion, after suggesting some possible solutions to mitigate mycotoxin exposure risk, we affirm that future collaborations between scientists and policymakers are important to develop sustainable interventions and promote health through dietary diversity.

The diffusion of Human-Robot Collaborative cells is prevented by several barriers. Classical control approaches seem not yet fully suitable for facing the variability conveyed by the presence of human operators beside robots. The capabilities of representing heterogeneous knowledge representation and performing abstract reasoning are crucial to enhance the flexibility of control solutions. To this aim, the ontology SOHO (Sharework Ontology for Human-Robot Collaboration) has been specifically designed for representing Human-Robot Collaboration scenarios, following a context-based approach. This work brings several contributions. This paper proposes an extension of SOHO to better characterize behavioral constraints of collaborative tasks. Furthermore, this work shows a knowledge extraction procedure designed to automatize the synthesis of Artificial Intelligence plan-based controllers for realizing flexible coordination of human and robot behaviors in collaborative tasks. The generality of the ontological model and the developed representation capabilities as well as the validity of the synthesized planning domains are evaluated on a number of realistic industrial scenarios where collaborative robots are actually deployed.

While the cognitive-behavioral characteristics of amyotrophic lateral sclerosis (ALS) patients carrying C9orf72 pathological repeat expansion have been extensively studied, our understanding of those carrying SOD1 variants is mostly based on case reports. The aim of this paper is to extensively explore the cognitive-behavioral characteristics of a cohort of ALS patients carrying pathogenetic variants of SOD1 gene, comparing them to patients without pathogenetic variants of 46 ALS-related genes (wild-type [WT]-ALS) and healthy controls. All ALS patients seen at the Turin ALS expert center in the 2009-2021 period who underwent both cognitive/behavioral and extensive genetic testing were eligible to be included in the study. Only patients with SOD1 pathogenetic variants (n = 28) (SOD1-ALS) and WT-ALS (n = 829) were enrolled in the study. A series of 129 controls was also included. Among the 28 SOD1-ALS patients, 16 (57.1%) had normal cognitive function, 5 (17.9%) isolated cognitive impairment (ALSci) (17.9%), 6 (21.4%) isolated behavioral impairment (ALSbi), 1 (3.6%) cognitive and behavioral impairment (ALScbi), and no one ALS-FTD. SOD1-ALS performed worse than controls in all explored domains, in particular Social Cognition and Language domains. SOD1-ALS patients had similar scores in all tests compared to WT-ALS, except the Story-based Empathy Task (SET), where they performed worse. Cognitive-behavioral impairment is much more common in SOD1 patients than previously assumed. SOD1-ALS are characterized by a more frequent impairment of Social Cognition and, less markedly, of Language domains. These findings have relevant implication both in the clinical and in the research setting, also considering recently approved treatment for SOD1-ALS. ANN NEUROL 2024;96:150-158.

This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action-perception cycles to those that underwrite planning and imagination; namely, from explicit models that entail variables for predicting concurrent sensations, like objects, faces, or people-to action-oriented models that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities-and the gradual transition from pragmatic to detached neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation.

BackgroundStudies on late talkers (LTs) highlighted their heterogeneity and the relevance of describing different communicative profiles. AimsTo examine lexical skills and gesture use in expressive (E-LTs) vs. receptive-expressive (R/E-LTs) LTs through a structured task. Methods and proceduresForty-six 30-month-old screened LTs were distinguished into E-LTs (n= 35) and R/E-LTs (n= 11) according to their receptive skills. Lexical skills and gesture use were assessed with a Picture Naming Game by coding answer accuracy (correct, incorrect, no response), modality of expression (spoken, spoken-gestural, gestural), type of gestures (deictic, representational), and spoken-gestural answers’ semantic relationship (complementary, equivalent, supplementary). Outcomes and resultsR/E-LTs showed lower scores than E-LTs for noun and predicate comprehension with fewer correct answers, and production with fewer correct and incorrect answers, and more no responses. R/E-LTs also exhibited lower scores in spoken answers, representational gestures, and equivalent spoken-gestural answers for noun production and in all spoken and gestural answers for predicate production. Conclusions and implicationsFindings highlighted more impaired receptive and expressive lexical skills and lower gesture use in R/E-LTs compared to E-LTs, underlying the relevance of assessing both lexical and gestural skills through a structured task, besides parental questionnaires and developmental scales, to describe LTs’ communicative profiles.

Decision-making has been observed to be systematically affected by decoys, i.e., options that should be irrelevant, either because unavailable or because manifestly inferior to other alternatives, and yet shift preferences towards their target. Decoy effects have been extensively studied both in humans and in several other species; however, evidence in non-human primates remains scant and inconclusive. To address this gap, this study investigates how choices in capuchin monkeys (Sapajus spp.) are affected by different types of decoys: asymmetrically dominated decoys, i.e., available and unavailable options that are inferior to only one of the other alternatives, and phantom decoys, i.e., unavailable options that are superior to another available alternative. After controlling for the subjective strength of initial preferences and the distance of each decoy from its target in attribute space, results demonstrate a systematic shift in capuchins’ preference towards the target of both asymmetrically dominated decoys (whether they are available or not) and phantom decoys, regardless of what options is being targeted by such decoys. This provides the most comprehensive evidence to date of decoy effects in non-human primates, with important theoretical and methodological implications for future comparative studies on context effects in decision-making.

Stories, in pictorial format, orally narrated, and later on as narrative texts, have played a key role in human evolution and to this day continue to surreptitiously teach us things and skills. In recent decades, psychologists and cognitive scientists have begun documenting the role of stories, and particularly fiction, in refining our sociocognitive skills. In this essay, I focus specifically on how stories, particularly written fiction, hone our emotion recognition skills. I present a brief overview of existing theorizing and research findings, and propose the less-is-more hypothesis, according to which emotion recognition skills are sharpened by stories that do not tell us, but rather show us the emotional life of fictional characters.

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.

ABSTRACT As state-of-art technology can create artificial images that are indistinguishable from real ones, it is urgent to understand whether believing that a picture is real or not has some import over affective phenomena such as sexual arousal. Thus, in two pre-registered online studies, we tested whether 60 images depicting models in underwear elicited higher self-reported sexual arousal when believed to be (N = 57) or presented as (N = 108) real photographs as opposed to artificially generated. In both cases, Realness correlated with significantly higher scores on self-reported sexual arousal. Consistently with the literature on downregulation of emotional response to fictional works, our result indicates that sexual images that are perceived to be fake are less arousing than those believed to portray real people.