Attention To Patterns Research Articles

Perceiving Etruscan Art: AI and Visual Perception

This research project is aimed at exploring the cognitive and emotional processes involved in perceiving Etruscan artifacts. The case study is the Sarcophagus of the Spouses at the National Etruscan Museum in Rome, one of the most important masterpieces in pre-Roman art. The study utilized AI and eye-tracking technology to analyze how viewers engaged with the Etruscan Sarcophagus of the Spouses, revealing key patterns of visual attention and engagement. OpenAI, ChatGPT-4 (accessed on 12 October 2024) was used in conjunction with Colab–Python in order to elaborate all the spreadsheets and data arising from the eye-tracking recording. The results showed that viewers primarily focused on the central figures, especially on their faces and hands, indicating a high level of interest in the human elements of the artifact. The longer fixation duration on these features suggest that viewers find them particularly engaging, which is likely due to their detailed craftsmanship and symbolic significance. The eye-tracking data also highlighted specific gaze patterns, such as diagonal scanning across the sarcophagus, which reflects the composition’s ability to guide viewer attention strategically. The results indicate that viewer focus centers on human elements, especially on faces and hands, suggesting that these features hold both esthetic and symbolic significance.

Remote monitoring of social attention in neurogenetic syndromes and idiopathic neurodevelopmental disability.

Social attention is a key aspect of neurodevelopment and is significantly altered in neurodevelopmental genetic syndromes and many individuals with idiopathic autism spectrum disorder (ASD). The primary aim of the present study was to examine the psychometric properties of webcam-collected social attention measurements, including four new specific aspects of social attention, in three genetic syndromes (PTEN Hamartoma Tumor Syndrome-PHTS; Malan Syndrome-NFIX; and SYNGAP1-related disorder-SYNGAP1), a mixed group of other neurodevelopmental genetic syndromes (Other NDGS), and individuals with a range of idiopathic neurodevelopmental disorder (NDD). The secondary aim was to evaluate the construct validity of these social attention measurements, including evaluating known-groups validity across study groups and concurrent validity for separating ASD and non-ASD cases. Participants (N = 467, age 3-45; PHTS n = 102, NFIX n = 23, SYNGAP1 n = 42, other NDGS n = 63, idiopathic NDD n = 53, neurotypical siblings n = 71, and unrelated neurotypical controls n = 113) completed a 4-min online-administered social attention paradigm that includes a variety of distinct stimuli at three timepoints (baseline, 1-month, and 4-month follow-up). Social attention measures had good scale and test-retest reliability, with the exception of measures of non-social preference and face-specific processing. Unique patterns of social attention emerged across study groups, with near neurotypical levels in PHTS and weaker social attention in NFIX and SYNGAP1 relative to controls. Global social attention had good accuracy in detecting ASD within NDGS participants. Remote monitoring social attention, including distinct aspects of social attention, may be useful for characterizing phenotypic profiles and tracking the natural history of distinct NDGS and idiopathic NDD as well as identifying ASD within NDGS. Given their reproducibility and stability, global social attention and several distinct social attention measures may be useful outcomes for future clinical trials.

Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety

Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.

Situational Awareness Errors in Forklift Logistics Operations: A Multiphase Eye-Tracking and Think-Aloud Approach

Background: This study explores forklift operators’ situational awareness (SA) and human errors in logistic operations using a multiphase approach as an innovative methodology. Methods: Ethnography, eye tracking, error taxonomy, and retrospective think-aloud (RTA) were used to study the diverse cognitive, behavioral, and operational aspects affecting SA. After analyzing 566 events across 18 tasks, this research highlighted eye tracking’s potential by offering real-time insights into operator behavior and RTA’s potential as a method for cross-checking the causal factors underlying errors. Results: Critical tasks, like positioning forklifts and lowering pallets, significantly impact incident occurrence, while high-cognitive demand tasks, such as hoisting and identifying pedestrians/obstacles, reduce SA and increase errors. Driving tasks are particularly vulnerable to errors and are the most affected by operator risk generators (ORGs), representing 42% of incident risk events. This study identifies driving, hoisting, and lowering loads as the tasks most influenced by system factors. Limitations include the task difficulty levels, managing physical risk, and training. Future research is suggested in autonomous industrial vehicles and advanced driver assistance systems (ADASs). Conclusions: This study provides valuable insights into how we may improve safety in logistics operations by proposing a multiphase methodology to uncover the patterns of attention, perception, and cognitive errors and their impact on decision-making.

Association between the focus of attention and brain activation pattern during golf putting task in amateur and novice: A fNIRS Study

PurposeExternal focus of attention (FOA) has been shown to improve motor performance. However, recent research has found that the effectiveness of FOA is related to the level of expertise. Therefore, this study examined the effects of FOA on putting performance in golfers of different levels of expertise. The neural mechanisms behind FOA were explored in conjunction with fNIRS. MethodA total of 30 participants, including 15 amateurs (Mage: 23.31(SD = 1.32)years; 15 males) and 15 novices (Mage: 22.69(SD = 1.55) years; 11 males; 4 females) were recruited. Participants completed EF and IF golf putting at a duration of 3s per time wearing fNIRS for 3 blocks of 30 seconds interspersed with 10-second rest blocks. ResultBehavioral results showed a significant difference in the putting performance of the amateur group under the EF condition compared to the IF condition (P = 0.019), and relative to novices, the amateur group performed better under the EF condition (P = 0.003). fNIRS results revealed that the amateur group had higher activation levels in the right somatosensory association cortex (RSAC) and right motor cortex (RMC) under the IF condition. In contrast, for the novice group, higher activation levels were observed in the left prefrontal cortex and RMC under the EF condition. ConclusionsOur results revealed SAC and MC over-activation in the amateur group under IF conditions with poor golf putting performance. Our findings suggest that the impairment of automated motor neural networks could be a possible mechanism by which IF affects motor performance with SAC and MC over-activation. Guiding novices to focus on task-related factors consciously could be a potential mechanism by which EF enhances motor performance.

Effects of Language Proficiency on Selective Attention Patterns at Segmenting Boundaries in English Audio Sentences

Background/Objectives: Normative perceptual segmentation facilitates event perception, comprehension, and memory. Given that native English listeners’ normative perceptual segmentation of English speech streams coexists with a highly selective attention pattern at segmentation boundaries, it is significant to test whether Chinese learners of English have a different attention pattern at boundaries, thereby checking whether they perform a normative segmentation. Methods: Thirty Chinese learners of English with relatively higher language proficiency (CLH) and 26 with relatively lower language proficiency (CLL) listened to a series of English audio sentences. Meanwhile, they were asked to press the key whenever a phonetic probe “ba” occurred. Response time to “ba” reflects the attention where “ba” is located at segmentation boundaries. Results: The results revealed that, (1) relative to native English listeners’ highly selective attention pattern, the CLH group showed a relatively selective attention pattern, while the CLL group displayed a non-selective attention pattern. (2) Both the CLH group and natives had better recognition memory than the CLL group. (3) Both the CLH group and natives’ attention at segmentation boundaries was not correlated with their memory for sentences, while the CLL group’s attention at boundaries was correlated with memory. Conclusions: These findings suggest that (1) Chinese learners of English did not perform a normative segmentation, which shows the effect of English proficiency on perceptual segmentation; (2) English proficiency has a superior effect on memory for sentences, while perceptual segmentation would come next to support memory by providing structure for memory construction if English proficiency is not high; (3) a comparison of attention patterns between Chinese learners and natives can provide a reference for potential intervention to rectify non-natives, thereby improving their perception of English speech streams.

IMFP-LG: Identification of Novel Multi-Functional Peptides by Using Protein Language Models and Graph-Based Deep Learning.

Functional peptides are short amino acid fragments that have a wide range of beneficial functions for living organisms. The majority of previous research focused on mono-functional peptides, but a growing number of multi-functional peptides have been discovered. Although there have been enormous experimental efforts to assay multi-functional peptides, only a small fraction of millions of known peptides have been explored. Effective and precise techniques for identifying multi-functional peptides can facilitate their discovery and mechanistic understanding. In this article, we presented a method iMFP-LG for identifying multi-functional peptides based on protein language models (pLMs) and graph attention networks (GATs). Comparison results showed that iMFP-LG outperforms state-of-the-art methods on both multi-functional bioactive peptides and multi-functional therapeutic peptides datasets. The interpretability of iMFP-LG was also illustrated by visualizing attention patterns in pLMs and GATs. Regarding the outstanding performance of iMFP-LG on the identification of multi-functional peptides, we employed iMFP-LG to screen novel candidate peptides with both ACP and AMP functions from millions of known peptides in the UniRef90. As a result, 8 candidate peptides were identified, and 1 candidate that exhibits both antibacterial and anticancer effects was confirmed through molecular structure alignment and biological experiments. We anticipate that iMFP-LG can assist in the discovery of multi-functional peptides and contribute to the advancement of peptide drug design.

Assessment of early attention in an Italian cohort of preschooler preterm children using the Early Childhood Attention Battery.

This cross-sectional monocentric study aims to utilize the Early Childhood Attention Battery to investigate early attention patterns in young preterm children and ascertain the extent to which their attentional abilities diverge from those of term peers. Inclusion criteria encompassed gestational age < 34 weeks, with assessments conducted between 3 and 5 years 11 months. Exclusion criteria included major brain lesions, significant motor or behavioral disorders, and intellectual functioning with IQ < 70. Preterm raw scores converted to scaled scores and percentiles. Frequency analysis compared preterm scores to norms. Non-parametric tests assessed significance between scaled scores and subgroups (age, gestational age, sex, birth weight). One hundred nineteen preterm patients were enrolled in the study between November 2016 and June 2023. Comparisons with published norms showed lower mean scores across all subtests and domains for preterm children. A notable proportion of assessments (13-40%) fell below the 5th percentile, with the dual task item in sustained attention being the highest (40%). Sex did not predict differences, except for auditory sustained and visual search subtests. Gestational age did not correlate with abnormal scores, consistent with previous studies. The prevalence of assessments below the 5th percentile highlights preterm children's vulnerability to attention issues. The test's sensitivity to attention deficits in preterm populations suggests its potential in identifying at-risk children early for tailored interventions. The battery effectively detects attentional deficits in preterm children. Early detection and targeted insights support tailored educational interventions. By focusing on specific attention skills, the battery guides clinicians in choosing individualized or group activities based on areas most affected. • Attention is often impaired in preterm children but it is not systematically investigated before school age. The ECAB is a tool specifically designed for preschool children. • Using the ECAB we evidenced a different level of attention between pre-term and term-born children at preschool age.

Eye movement characteristics of emotional face recognizing task in patients with mild to moderate depression.

Depression is a complex affective disorder characterized by high prevalence and severe impact, commonly presenting with cognitive impairment. The objective diagnosis of depression lacks precise standards. This study investigates eye movement characteristics during emotional face recognition task (EFRT) in depressive patients to provide empirical support for objective diagnosis. We recruited 43 patients with depression (Depressive patients, DP) from a psychiatric hospital and 44 healthy participants (Healthy Control, HC) online. All participants completed an EFRT comprising 120 trials. Each trial presented a gray screen for 800 ms followed by a stimulus image for judgment. Emotions were categorized as positive, neutral, or negative. Eye movement trajectories were recorded throughout the task. Latency of First Fixation (LFF), Latency of First Fixation for Eye AOI, and Latency of First Fixation for Mouth AOI were used as representative indicators of early attention, Proportion of Eye AOI, and Proportion of Mouth AOI as measures of intermediate attention, Accuracy (ACC) and Reaction Time (RT) as behavioral indicators of late-stage attention. In this study, these metrics were employed to explore the differences between patients with depression and healthy individuals. Compared to healthy participants, individuals with depression exhibit longer first fixation latencies on the eyes and mouth during the early attention stage of emotional face recognition, indicating an avoidance tendency toward key facial recognition cues. In the mid-to-late attention stages, depressive individuals show an increased fixation ratio on the eyes and a decreased fixation ratio on the mouth, along with lower accuracy and longer response times. These findings suggest that, relative to healthy individuals, individuals with depression have deficits in facial recognition. This study identified distinct attention patterns and cognitive deficits in emotional face recognition among individuals with depression compared to healthy individuals, providing an attention-based approach for exploring potential clinical diagnostic markers for depression.

Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task.

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers' cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the "fatigued" spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

Attracting Visual Attention in a Digital Age

The purpose of this study was to determine whether previously established visual attention patterns remained intact during video scenes designed to elicit specific emotions using a novel suite of biosensors. To examine the relationship between visual attention and emotion, data from eye tracking, facial expression recognition (FER), and galvanic skin response (GSR) combined with survey data were used to identify the bottom-up and top-down features of saliency in videos that contributed to their “interestingness.” Using a mixed-methods design and convenience sampling, participants (N = 42) watched 60 video clips designed to evoke different emotional responses (positive, neutral, or negative). The results indicated that using a suite of biosensors to examine the impacts of bottom-up and top-down features of visual attention was effective.

Understanding public opinion and discussion dynamics of digital humans on social media: an analysis of sentiment, themes and user characteristics

PurposeThe emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.Design/methodology/approachThis study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.FindingsThe results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.Originality/valueThrough the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.

MethylGPT: a foundation model for the DNA methylome.

DNA methylation serves as a powerful biomarker for disease diagnosis and biological age assessment. However, current analytical approaches often rely on linear models that cannot capture the complex, context-dependent nature of methylation regulation. Here we present MethylGPT, a transformer-based foundation model trained on 226,555 (154,063 after QC and deduplication) human methylation profiles spanning diverse tissue types from 5,281 datasets, curated 49,156 CpG sites, and 7.6 billion training tokens. MethylGPT learns biologically meaningful representations of CpG sites, capturing both local genomic context and higher-order chromosomal features without external supervision. The model demonstrates robust methylation value prediction (Pearson R=0.929) and maintains stable performance in downstream tasks with up to 70% missing data. Applied to age prediction across multiple tissue types, MethylGPT achieves superior accuracy compared to existing methods. Analysis of the model's attention patterns reveals distinct methylation signatures between young and old samples, with differential enrichment of developmental and aging-associated pathways. When finetuned to mortality and disease prediction across 60 major conditions using 18,859 samples from Generation Scotland, MethylGPT achieves robust predictive performance and enables systematic evaluation of intervention effects on disease risks, demonstrating potential for clinical applications. Our results demonstrate that transformer architectures can effectively model DNA methylation patterns while preserving biological interpretability, suggesting broad utility for epigenetic analysis and clinical applications.

Select & Re-Rank: Effectively and efficiently matching multimodal data with dynamically evolving attention

Extracting semantically consistent representations from multi-modal data helps computers understand the human world more comprehensively. Visual-semantic matching, as one of the fundamental tasks for multi-modal learning, attracts continuous attention. Recent research makes unflagging endeavors to enhance the matching performance, but sometimes at the expense of overlooking the delicate balance between efficiency and effectiveness. In this paper, we aim to address this dilemma through a newly proposed attention-mechanism-based architecture. To ensure optimal effectiveness, we adopt a more advanced Transformer Encoder (TE) as our basic model and introduce two significant ameliorations to tailor it for the visual-semantic matching task. Initially, we incorporate fine-grained supervision into the classic TE, allowing our model to capture sophisticated correspondences between different modalities. Subsequently, we employ a dynamic attention-evolving strategy to selectively pass useful information and strengthen the attention pattern consistency between adjacent TE blocks. To maintain efficiency, we propose a novel Select & Re-rank strategy that enables the model to ignore redundant information. This approach significantly reduces the computational cost and increases the matching speed with relatively minimal performance degradation. The proposed architecture can gradually capture and reorganize useful information from inter-modality as well as intra-modality under the supervision of both fine-grained and global similarity, which leads to more comprehensive and discriminative embeddings. Experiments on two benchmark datasets show that the proposed method achieves competitive results in terms of both efficiency and effectiveness.

I Prefer to Look at an Animal Rather than at a Human: Visual Attention of Neurotypical Children and Children with Autism Spectrum Disorder (ASD) During One-Time First Exposure to an Assistance Dog

Interacting with animals often provides numerous benefits for children with autism spectrum disorder (ASD). One potential explanation for this is that children with ASD exhibit particular visual attention to animals. This study aimed to characterize the visual attention patterns towards humans and animals in children with ASD compared to neurotypical (NT) children during one-time first exposure to an assistance dog. Forty children participated (18 ASD, 22 NT). The interventions were videorecorded. Three categories of visual attention (gaze, glance, and mutual gaze) directed towards assistance dogs and human professionals were coded using ethological methods. Both the children with ASD and the NT children gazed more frequently at the assistance dog than at the professional. However, although infrequently, both groups shared more mutual gazes with the professional than with the assistance dog. Both the NT children and the children with ASD preferred to gaze rather than to glance at, or share mutual gazes with, the assistance dogs. The attention patterns of the NT children towards the professionals were consistent, whereas those of the children with ASD were not. Statistical linear models revealed that a child’s status (ASD/NT) was the only factor influencing the frequency and duration of the visual behaviors observed. These results confirm that individuals with ASD exhibit particularly focused attention towards animals.

Fusion Attention for Action Recognition: Integrating Sparse-Dense and Global Attention for Video Action Recognition.

Conventional approaches to video action recognition perform global attention over the entire video patches, which may be ineffective due to the temporal redundancy of video frames. Recent works on masked video modeling adopt a high-ratio tube masking and reconstruction strategy as a pre-training method to mitigate the problem of focusing on spatial features well but not on temporal features. Inspired by this pre-training method, we propose Fusion Attention for Action Recognition (FAR), which fuses the sparse-dense attention patterns specialized for temporal features with global attention during fine-tuning. FAR has three main components: head-split sparse-dense attention (HSDA), token-group interaction, and group-averaged classifier. First, HSDA splits the head of multi-head self-attention to fuse global and sparse-dense attention. The sparse-dense attention is divided into groups of tube-shaped patches to focus on temporal features. Second, token-group interaction is used to improve information exchange between divided patch groups. Finally, the group-averaged classifier uses spatio-temporal features from different patch groups to improve performance. The proposed method uses the weight parameters that are pre-trained with VideoMAE and MVD, and achieves higher performance (+0.1-0.4%) with less computation than models fine-tuned with global attention on Something-Something V2 and Kinetics-400. Moreover, qualitative comparisons show that FAR captures temporal features quite well in highly redundant video frames. The FAR approach demonstrates improved action recognition with efficient computation, and exploring its adaptability across different pre-training methods presents an interesting direction for future research.

Skill building in freediving as an example of embodied culture.

Skilled activity is a complex mix of automatized action, changed attention patterns, cognitive strategies and physiological adaptations developed within a community of practice. Drawing on physiological and ethnographic research on freediving, this article argues that skill acquisition demonstrates the variety of mechanisms that link biological and cultural processes to produce culturally shaped forms of embodiment. In particular, apneists alter phenotypic expression through patterned practices that canalize development, exaggerating the dive response, developing resistance to elevated carbon dioxide levels (hypercapnia) and accommodating hydrostatic pressure at depth. The community of divers provides technical advice and helps to orient individuals' motivations. Some biological processes are phenomenologically accessible, but others are sub-aware and must be accessed indirectly through behaviour or altered interactions with the environment. The close analysis of embodied skills like freediving illustrates how phenotypic plasticity is inflected by culturally patterned behaviours. Divers do developmental work on bodily traits like the dive response to achieve more dramatic performance, even if they cannot directly control all elements of the neurological and physiological responses. The example of expert freediving illustrates the imbrication of biology and culture in embodiment. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

Attentional bias to social media stimuli ismoderated by fear of missing out among problematic social media users.

Previous evidence has indicated that problematic social media use (PSMU) is characterized by an attentional bias to social media icons (such as Facebook icons), but not to social webpages (such as Facebook webpages). They suggest that there may be other factors influencing attentional bias like fear of missing out (FoMO). But it remains unclear how FoMO moderates attentional bias in PSMU. This study aims to investigate whether PSMU show attentional bias for stimuli associated with social media, and how FoMO moderates on attentional bias among PSMU through experimental methods. Based on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study explored mechanisms of attentional bias to social media icons (such as WeChat) related to PSMU and further examined the role of FoMO in this relationship. Specifically, attentional bias patterns to social media icons of 62 participants (31 PSMU and 31 control group) were explored during a dot-probe paradigm combined with eye-tracking in Experiment 1, and attentional bias patterns to social media icons of another 61 individuals with PSMU with different FoMO levels was explored during a dot-probe paradigm combined with eye-tracking in Experiment 2. Results revealed that individuals with PSMU had an attentional bias toward social media icons, demonstrated by attentional maintenance, and such bias such bias was moderated by FoMO negatively, demonstrated by attentional vigilance and maintenance in PSMU/high FoMO. These results suggest that attentional bias is a common mechanism associated with PSMU, and FoMO is a key factor on the development of PSMU.

Virtual reality interview with feedback framework for situational practice of gaze among autistic adults

BackgroundAutistic individuals commonly seek employment; however, only a small fraction are in the workforce. In part, this might be due to mismatches between their social patterns of attention and gaze, and society’s normative expectations during interviews. MethodTo help mitigate such disadvantages through a solo situational practice tool, we present a framework that consists of a virtual reality (VR) based job interview simulation and a coaching component. Employing data visualization, video modeling, and VR role-play of the targeted behavior, the coaching support could be done in a self-deliverable practice manner. A participatory design session with two autistic design partners was important in the co-creation of the feedback methods, making them easier to understand, and including positive reinforcement. ResultsFourteen autistic individuals used the VR job interview simulation tool. Eleven received the gaze analysis and support stage and participated in a second VR simulation session. Preliminary results were positive, in that participant scores on average eye contact duration, average time without eye contact, and percentage of eye contact while listening and while speaking generally approached the corresponding medians of the non-autistic reference dataset. Participants were surveyed about the utility of the tool after the second feedback session and two months later. All survey respondents perceived the tool to be useful and the provided feedback to be helpful in daily social interactions. ConclusionsThis study provides insights towards the development of a VR job interview simulation and feedback framework that can enable solo situational practice of gaze and common interview questions.

Individual and contextual effects of attention in risky choice

AbstractWe investigate the role of visual attention in risky choice in a rich experimental dataset that includes eye-tracking data. We first show that attention is not reducible to individual and contextual variables, which explain only 20% of attentional variation. We then decompose attentional variation into individual average attention and trial-wise deviations of attention to capture different cognitive processes. Individual average attention varies by individual, and can proxy for individual preferences or goals (as in models of “rational inattention” or goal-directed attention). Trial-wise deviations of attention vary within subjects and depend on contextual factors (as in models of “salience” or stimulus-driven attention). We find that both types of attention predict behavior: average individual attention patterns are correlated with individual levels of loss aversion and capture part of this individual heterogeneity. Adding trial-wise deviations of attention further improves model fit. Our results show that a decomposition of attention into individual average attention and trial-wise deviations of attention can capture separable cognitive components of decision making and provides a useful tool for economists and researchers from related fields interested in decision-making and attention.