Project description:Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI's model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.
Project description:BackgroundIndividuals with autism spectrum disorder (ASD) are characterized by social communication challenges and repetitive behaviors that may be quickly detected by experts (Autism Res 10:653-62, 2017; American Psychiatric Association, Diagnostic and statistical manual of mental disorders, 2013). Recent research suggests that even naïve non-experts judge a variety of human dimensions using narrow windows of experience called "first impressions." Growing recognition of sex differences in a variety of observable behaviors in ASD, combined with research showing that some autistic girls and women may "camouflage" outward symptoms, suggests it may be more difficult for naïve conversation partners to detect ASD symptoms in girls. Here, we explore the first impressions made by boys and girls with ASD and typically developing (TD) peers.MethodsNinety-three school-aged children with ASD or TD were matched on IQ; autistic girls and boys were additionally matched on autism symptom severity using the ADOS-2. Participants completed a 5-minute "get-to-know-you" conversation with a new young adult acquaintance. Immediately after the conversation, confederates rated participants on a variety of dimensions. Our primary analysis compared conversation ratings between groups (ASD boys, ASD girls, TD boys, TD girls).ResultsAutistic girls were rated more positively than autistic boys by novel conversation partners (better perceived social communication ability), despite comparable autism symptom severity as rated by expert clinicians (equivalent true social communication ability). Boys with ASD were rated more negatively than typical boys and typical girls by novel conversation partners as well as expert clinicians. There was no significant difference in the first impressions made by autistic girls compared to typical girls during conversations with a novel conversation partner, but autistic girls were rated lower than typical girls by expert clinicians.LimitationsThis study cannot speak to the ways in which first impressions may differ for younger children, adults, or individuals who are not verbally fluent; in addition, there were more autistic boys than girls in our sample, making it difficult to detect small effects.ConclusionsFirst impressions made during naturalistic conversations with non-expert conversation partners could-in combination with clinical ratings and parent report-shed light on the nature and effects of behavioral differences between girls and boys on the autism spectrum.
Project description:Safety and efficiency of human-AI collaboration often depend on how humans could appropriately calibrate their trust towards the AI agents. Over-trusting the autonomous system sometimes causes serious safety issues. Although many studies focused on the importance of system transparency in keeping proper trust calibration, the research in detecting and mitigating improper trust calibration remains very limited. To fill these research gaps, we propose a method of adaptive trust calibration that consists of a framework for detecting the inappropriate calibration status by monitoring the user's reliance behavior and cognitive cues called "trust calibration cues" to prompt the user to reinitiate trust calibration. We evaluated our framework and four types of trust calibration cues in an online experiment using a drone simulator. A total of 116 participants performed pothole inspection tasks by using the drone's automatic inspection, the reliability of which could fluctuate depending upon the weather conditions. The participants needed to decide whether to rely on automatic inspection or to do the inspection manually. The results showed that adaptively presenting simple cues could significantly promote trust calibration during over-trust.
Project description:First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable "ambient" face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58% of the variance in raters' impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features.
Project description:A common goal in psychological research is the measurement of subjective impressions, such as first impressions of faces. These impressions are commonly measured using Likert ratings. Although these ratings are simple to administer, they are associated with response issues that can limit reliability. Here we examine best-worst scaling (BWS), a forced-choice method, as a potential alternative to Likert ratings for measuring participants' facial first impressions. We find that at the group level, BWS scores correlated almost perfectly with Likert scores, indicating that the two methods measure the same impressions. However, at the individual participant level BWS outperforms Likert ratings, both in terms of ability to predict preferences in a third task, and in terms of test-retest reliability. These benefits highlight the power of BWS, particularly for use in individual differences research.
Project description:Americans' increasing levels of ideological polarization contribute to pervasive intergroup tensions based on political partisanship. Cues to partisanship may affect even the most basic aspects of perception. First impressions of faces constitute a widely-studied basic aspect of person perception relating to intergroup tensions. To understand the relation between face impressions and political polarization, two experiments were designed to test whether disclosing political partisanship affected face impressions based on perceivers' political ideology. Disclosed partisanship more strongly affected people's face impressions than actual, undisclosed, categories (Experiment 1). In a replication and extension, disclosed shared and opposing partisanship also engendered, respectively, positive and negative changes in face impressions (Experiment 2). Partisan disclosure effects on face impressions were paralleled by the extent of people's partisan threat perceptions (Experiments 1 and 2). These findings suggest that partisan biases appear in basic aspects of person perception and may emerge concomitant with perceived partisan threat.
Project description:People typically rely heavily on visual information when finding their way to unfamiliar locations. For individuals with reduced vision, there are a variety of navigational tools available to assist with this task if needed. However, for wayfinding in unfamiliar indoor environments the applicability of existing tools is limited. One potential approach to assist with this task is to enhance visual information about the location and content of existing signage in the environment. With this aim, we developed a prototype software application, which runs on a consumer head-mounted augmented reality (AR) device, to assist visually impaired users with sign-reading. The sign-reading assistant identifies real-world text (e.g., signs and room numbers) on command, highlights the text location, converts it to high contrast AR lettering, and optionally reads the content aloud via text-to-speech. We assessed the usability of this application in a behavioral experiment. Participants with simulated visual impairment were asked to locate a particular office within a hallway, either with or without AR assistance (referred to as the AR group and control group, respectively). Subjective assessments indicated that participants in the AR group found the application helpful for this task, and an analysis of walking paths indicated that these participants took more direct routes compared to the control group. However, participants in the AR group also walked more slowly and took more time to complete the task than the control group. The results point to several specific future goals for usability and system performance in AR-based assistive tools.
Project description:Despite pronouncements about the inevitable diffusion of artificial intelligence and autonomous technologies, in practice, it is human behavior, not technology in a vacuum, that dictates how technology seeps into—and changes—societies. To better understand how human preferences shape technological adoption and the spread of AI-enabled autonomous technologies, we look at representative adult samples of US public opinion in 2018 and 2020 on the use of four types of autonomous technologies: vehicles, surgery, weapons, and cyber defense. By focusing on these four diverse uses of AI-enabled autonomy that span transportation, medicine, and national security, we exploit the inherent variation between these AI-enabled autonomous use cases. We find that those with familiarity and expertise with AI and similar technologies were more likely to support all of the autonomous applications we tested (except weapons) than those with a limited understanding of the technology. Individuals that had already delegated the act of driving using ride-share apps were also more positive about autonomous vehicles. However, familiarity cut both ways; individuals are also less likely to support AI-enabled technologies when applied directly to their life, especially if technology automates tasks they are already familiar with operating. Finally, we find that familiarity plays little role in support for AI-enabled military applications, for which opposition has slightly increased over time. Supplementary Information The online version contains supplementary material available at 10.1007/s00146-023-01666-5.
Project description:Individuals exposed to community violence are more likely to engage in antisocial behavior, resulting in a dramatic increase in contact with justice and social service systems. Theoretical accounts suggest that disruptions in learning underlie the link between exposure to violence and maladaptive behaviors. However, empirical evidence specifying these processes is sparse. Here, in a sample of incarcerated males, we investigated how exposure to violence affects the ability to learn about the harmfulness of others and use this information to adaptively modulate trust behavior. Exposure to violence does not impact the ability to accurately develop beliefs about agents' harm preferences and predict their choices. However, exposure to violence disrupts the ability to form moral impressions that dissociate between agents with distinguishable harm preferences, and subsequently, the ability to adjust trust behavior towards different agents. These findings reveal a process that may explain the association between exposure to violence and maladaptive behavior.
Project description:What factors influence how accurately we express our personalities? Here, we investigated the role of targets' nonverbal expressivity or the intrapersonal coordination between head and body movements. To do so, using a novel movement quantification method, we examined whether variability in a person's behavioral coordination was related to how accurately their personality was perceived by naive observers. Targets who exhibited greater variability in intrapersonal behavior coordination, indicating more expressive behavior, were perceived more accurately on high observability personality items, such as how energetic and helpful they are. Moreover, these associations held controlling for other indicators of overall movement, self- and perceiver-rated extroversion, as well as how engaging and likable targets were perceived to be. This provides preliminary evidence that variability in intrapersonal behavioral coordination may be a unique behavioral indicator of expressive accuracy, although further research that replicates these findings and examines the causal associations is needed.