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Predictive inference and error-driven learning are critical to optimal performance across many different contexts. However, the specific context determines the informativeness of errors in updating predictions. In this study, participants experienced two changing, unsignalled contexts with opposite optimal responses to errors; the change-point context, where errors were informative, and the oddball context, where they were not. The changes to the context occurred under two task structures: 1) a fixed task structure, with consecutive blocks of each context, and 2) a random task structure, with the context randomly selected for each new block. We modelled participants' performance using a Hierarchical Gaussian Filter (HGF) model. We found that performance was greater in the oddball than change-point context, with more accurate and precise estimates. The estimates from the fixed task structure were also more precise than those in the random task structure. We showed that consistency in context can improve precision.

Recent years have seen remarkable advances in the development and use of Artificial Intelligence (AI) in image classification, driving cars, and writing scientific articles. Although AI can outperform humans in many tasks, there remain domains where humans and AI working together can outperform either working alone. For humans and AI to work together effectively, the human must trust the AI bot to the right degree (calibrated). If the human does not trust the bot sufficiently, or conversely trusts the bot more than is warranted, the human-bot team will not perform as well as they could. We report three experiments examining trust in human-AI teaming. While existing studies typically collect binary responses (to trust, or not to trust), we present a novel paradigm that quantifies trust in a bot-recommendation in a continuous fashion. These data allow better precision, and in the future the development of more refined models of human-bot trust.

We present an analysis of real-life televised Dutch Auctions, where over the course of 12 hours of auctions, bidders made over 5,000 purchases of 81 unique products. In the Dutch Auction format, multiples of an item (traditionally Dutch tulips) are for sale, where prices start high and decrease over time. In the televised version, viewers of the Price-Drop TV program could wait for the price to drop over time but needed to weigh this against the risk of being scooped by the collective bidding behaviour of other viewers. We conduct analyses of the auctions to: 1) demonstrate the features of sell-out auctions where all items are sold, 2) show how time pressure increases sales in the auction, and 3) how the type of price decrease, discrete vs. continuous, affects people's decision to bid on items. We conclude by proposing an iterative process model based on the framework of Prospect Theory to extend theoretically motivated, laboratory-based findings out to real-world purchasing behaviours.

Collaboration in shared environments requires human agents to coordinate their behaviour according to the machines’ actions. In this study, we compared the performance and behaviour of Human-Machine (HM) and Human-Human (HH) teams. While HH teaming behaviour is sensitive to Collaborative contexts, little is known about HM teaming behaviour. Furthermore, teaming behaviour may impact the team’s Joint Capacity the team’s ability to handle teamwork processes and task demands. To assess teaming behaviour at every moment of a trial we used three distinct spatiotemporal measures (Momentary Distance, Highly Correlated Segments, and Running Correlation).To assess the team's joint performance, we adopted the Capacity Coefficient (Townsend & Nozawa, 1995). For both HH and HM teams, behavioural measures predicted Joint Capacity. HH teams demonstrated greater performance and less synchronous behaviour than HM teams. The reduced synchrony of HH teams likely improved their performance as they could complement each other’s behaviour rather than duplicate inefficiencies.

This is the first study to investigate instrumental activities of daily living in older autistic adults. We conducted interviews with fifteen adults (mean age = 60.1, SD = 7.4, range = 50-73) from Australia with no intellectual disability. Analysis included both deductive and inductive steps, to categorise responses using the Occupational Performance Model Australia and identify themes across participants' experiences. Strengths and challenges were unique to the individual, as were the methods they had developed to manage tasks. Challenges occurred mostly at the interaction between aspects of the environment (sensory, cognitive, social and cultural) and personal factors such as health conditions and sensory sensitivities. Enhanced person-environment fit is needed, as is a shift in wider sociocultural attitudes to enable comfort and autonomy in later life.

IntroductionThis study examines the associations of neighborhood environments with BMI, HbA1c, and diabetes across 6 years in Hispanic/Latino adults.MethodsParticipants from the Hispanic Community Health Study/Study of Latinos San Diego site (n=3,851, mean age=39.4 years, 53.3% women, 94.0% Mexican heritage) underwent assessment of metabolic risk factors and diabetes status (categorized as normoglycemia, prediabetes, and diabetes) at baseline (2008-2011) and approximately 6 years later (2014-2017). In the Study of Latinos Community and Surrounding Areas Study ancillary study (2015-2020), participant baseline addresses were geocoded, and neighborhoods were defined using 800-meter circular buffers. Neighborhood variables representing socioeconomic deprivation, residential stability, social disorder, walkability, and greenness were created using Census and other public databases. Analyses were conducted in 2020-2021.ResultsComplex survey regression analyses revealed that greater neighborhood socioeconomic deprivation was associated with higher BMI (β=0.14, p