No abstract

Loot boxes are digital containers of randomised rewards available in many video games. Due to similarities between some loot boxes and traditional forms of gambling, concerns regarding the relationship between spending on loot boxes in video games and symptoms of problematic gambling have been expressed by policy makers and the general public. We present the first investigation of these concerns in large cross-sectional cross-national samples from three countries (Aotearoa New Zealand, Australia, and the United States). A sample of 1,049 participants were recruited through Qualtrics' Survey Targeting service from a broad cross-section of the population in Australia (n = 339), Aotearoa New Zealand (n = 323), and the United States (n = 387). Participants answered a survey assessing problem gambling, problem gaming symptomology, and how much they spent on loot boxes per month. On average, individuals with problem gambling issues spent approximately $13 USD per month more on loot boxes than those with no such symptoms. Loot box spending was also associated with both positive and negative moods, albeit with small effect sizes. Analyses showed both interactions and correlations between problematic gambling and problematic gaming symptoms, indicating both some commonality in the mechanisms underlying, and independent contributions made by, these proposed diagnostic criteria. These results provide context for dialogues regarding how best to reduce the impacts of loot box spending among those with problematic gambling symptoms.

Recent research using a calibration approach indicates that eyewitness confidence assessments obtained immediately after a positive identification decision provide a useful guide as to the likely accuracy of the identification. This study extended research on the boundary conditions of the confidence-accuracy (CA) relationship by varying the retention interval between encoding and identification test. Participants (N = 1,063) viewed one of five different targets in a community setting and attempted an identification from an 8-person target-present or -absent lineup either immediately or several weeks later. Compared to the immediate condition, the delay condition produced greater overconfidence and lower diagnosticity. However, for choosers at both retention intervals there was a meaningful CA relationship and diagnosticity was much stronger at high than low confidence levels.

Eyewitness identification decisions are vulnerable to various influences on witnesses' decision criteria that contribute to false identifications of innocent suspects and failures to choose perpetrators. An alternative procedure using confidence estimates to assess the degree of match between novel and previously viewed faces was investigated. Classification algorithms were applied to participants' confidence data to determine when a confidence value or pattern of confidence values indicated a positive response. Experiment 1 compared confidence group classification accuracy with a binary decision control group's accuracy on a standard old-new face recognition task and found superior accuracy for the confidence group for target-absent trials but not for target-present trials. Experiment 2 used a face mini-lineup task and found reduced target-present accuracy offset by large gains in target-absent accuracy. Using a standard lineup paradigm, Experiments 3 and 4 also found improved classification accuracy for target-absent lineups and, with a more sophisticated algorithm, for target-present lineups. This demonstrates the accessibility of evidence for recognition memory decisions and points to a more sensitive index of memory quality than is afforded by binary decisions.