Music-reading expertise modulates the visual span for English letters but not Chinese characters.

Recent research has suggested that the visual span in stimulus identification can be enlarged through perceptual learning. Since both English and music reading involve left-to-right sequential symbol processing, music-reading experience may enhance symbol identification through perceptual learning particularly in the right visual field (RVF). In contrast, as Chinese can be read in all directions, and components of Chinese characters do not consistently form a left-right structure, this hypothesized RVF enhancement effect may be limited in Chinese character identification. To test these hypotheses, here we recruited musicians and nonmusicians who read Chinese as their first language (L1) and English as their second language (L2) to identify music notes, English letters, Chinese characters, and novel symbols (Tibetan letters) presented at different eccentricities and visual field locations on the screen while maintaining central fixation. We found that in English letter identification, significantly more musicians achieved above-chance performance in the center-RVF locations than nonmusicians. This effect was not observed in Chinese character or novel symbol identification. We also found that in music note identification, musicians outperformed nonmusicians in accuracy in the center-RVF condition, consistent with the RVF enhancement effect in the visual span observed in English-letter identification. These results suggest that the modulation of music-reading experience on the visual span for stimulus identification depends on the similarities in the perceptual processes involved

Music Reading Expertise Modulates Visual Spans in both Music Note and EnglishLetter Reading

Here we investigated how music reading experience modu-lates visual spans in language reading. Participants wereasked to identify music notes, English letters, Chinese charac-ters, and novel symbols (Tibetan letters) presented at randomlocations on the screen while maintaining central fixation. Wefound that for music note reading, musicians outperformednon-musicians at some peripheral positions in both visualfields, and for English letter reading, musicians outperformednon-musicians at some peripheral positions in the RVF butnot in the LVF. In contrast, in both Chinese character andnovel symbol reading, musicians and non-musicians did notdiffer in their performance at peripheral positions. Since bothmusic and English reading involve a left-to-right reading di-rection and a RVF/LH advantage, these results suggest thatthe modulation of music reading experience on visual spans inlanguage reading depends on the similarities in the cognitiveprocesses involved

Explanation Strategies for Image Classification in Humans vs. Current Explainable AI

Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. In image classification, we found that humans adopted more explorative attention strategies for explanation than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations diagnostic for inferring class labels, whereas the other involved explorative scanning with more visual explanations rated higher for effectiveness. Interestingly, XAI saliency-map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans differ in information and strategy use for explanations, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users

Predicting an observer's task using multi-fixation pattern analysis

Since Yarbus's seminal work in 1965, vision scientists have argued that people's eye movement patterns differ depending upon their task. This suggests that we may be able to infer a person's task (or mental state) from their eye movements alone. Recently, this was attempted by Greene et al. [2012] in a Yarbus-like replication study; however, they were unable to successfully predict the task given to their observer. We reanalyze their data, and show that by using more powerful algorithms it is possible to predict the observer's task. We also used our algorithms to infer the image being viewed by an observer and their identity. More generally, we show how off-the-shelf algorithms from machine learning can be used to make inferences from an observer's eye movements, using an approach we call Multi-Fixation Pattern Analysis (MFPA)

Cultural orientation of self-bias in perceptual matching

This work was supported by grants from the Economic and Social Research Council (ES/K013424/1), the National Natural Science Foundation of China (31371017), and the Research Grants Council of Hong Kong (HKU758412H)Peer reviewedPublisher PD

Objective Measures of IS Usage Behavior Under Conditions of Experience and Pressure Using Eye Fixation Data

The core objective of this study is to understand individuals IS usage by going beyond the traditional subjective self-reported and objective system-log measures to unveil the delicate process through which users interact with IS. In this study, we conducted a laboratory experiment to capture users’ eye movement and, more importantly, applied a novel methodology that uses the Gaussian mixture model (GMM) to analyze the gathered physiological data. We also examine how performance pressure and prior usage experience of the investigative system affect IS usage patterns. Our results suggest that experienced and pressured users demonstrate more efficient and focused usage patterns than inexperienced and non-pressured ones, respectively. Our findings constitute an important advancement in the IS use literature. The proposed statistical approach for analyzing eye-movement data is a critical methodological contribution to the emerging research that uses eye-tracking technology for investigation

Modeling an autism risk factor in mice leads to permanent immune dysregulation

Increasing evidence highlights a role for the immune system in the pathogenesis of autism spectrum disorder (ASD), as immune dysregulation is observed in the brain, periphery, and gastrointestinal tract of ASD individuals. Furthermore, maternal infection (maternal immune activation, MIA) is a risk factor for ASD. Modeling this risk factor in mice yields offspring with the cardinal behavioral and neuropathological symptoms of human ASD. In this study, we find that offspring of immune-activated mothers display altered immune profiles and function, characterized by a systemic deficit in CD4^+ TCRβ^+ Foxp3^+ CD25^+ T regulatory cells, increased IL-6 and IL-17 production by CD4^+ T cells, and elevated levels of peripheral Gr-1^+ cells. In addition, hematopoietic stem cells from MIA offspring exhibit altered myeloid lineage potential and differentiation. Interestingly, repopulating irradiated control mice with bone marrow derived from MIA offspring does not confer MIA-related immunological deficits, implicating the peripheral environmental context in long-term programming of immune dysfunction. Furthermore, behaviorally abnormal MIA offspring that have been irradiated and transplanted with immunologically normal bone marrow from either MIA or control offspring no longer exhibit deficits in stereotyped/repetitive and anxiety-like behaviors, suggesting that immune abnormalities in MIA offspring can contribute to ASD-related behaviors. These studies support a link between cellular immune dysregulation and ASD-related behavioral deficits in a mouse model of an autism risk factor

Predicting an observer's task using multi-fixation pattern analysis

Since Yarbus's seminal work in 1965, vision scientists have argued that people's eye movement patterns differ depending upon their task. This suggests that we may be able to infer a person's task (or mental state) from their eye movements alone. Recently, this was attempted by Greene et al. [2012] in a Yarbus-like replication study; however, they were unable to successfully predict the task given to their observer. We reanalyze their data, and show that by using more powerful algorithms it is possible to predict the observer's task. We also used our algorithms to infer the image being viewed by an observer and their identity. More generally, we show how off-the-shelf algorithms from machine learning can be used to make inferences from an observer's eye movements, using an approach we call Multi-Fixation Pattern Analysis (MFPA)

Microbiota Modulate Behavioral and Physiological Abnormalities Associated with Neurodevelopmental Disorders

Neurodevelopmental disorders, including autism spectrum disorder (ASD), are defined by core behavioral impairments; however, subsets of individuals display a spectrum of gastrointestinal (GI) abnormalities. We demonstrate GI barrier defects and microbiota alterations in the maternal immune activation (MIA) mouse model that is known to display features of ASD. Oral treatment of MIA offspring with the human commensal Bacteroides fragilis corrects gut permeability, alters microbial composition, and ameliorates defects in communicative, stereotypic, anxiety-like and sensorimotor behaviors. MIA offspring display an altered serum metabolomic profile, and B. fragilis modulates levels of several metabolites. Treating naive mice with a metabolite that is increased by MIA and restored by B. fragilis causes certain behavioral abnormalities, suggesting that gut bacterial effects on the host metabolome impact behavior. Taken together, these findings support a gut-microbiome-brain connection in a mouse model of ASD and identify a potential probiotic therapy for GI and particular behavioral symptoms in human neurodevelopmental disorders