Exploring the Effect of Stimuli Characteristics on Signal Detection Performance: Sex Influenced Targeting Bias

Abstract

Research has shown that the perceived sex (female versus male features) of a presented threat can influence participants’ responses. This exploratory analysis examined data from an experiment which utilized a virtual reality signal detection task. Six categorically different character models (three males and three females) transported one of five potential objects (one signal: pistol; five noise: gardening tools) across a virtual environment. The focus of our analysis was to explore the influence of participant sex and character gender on participants' perceptual sensitivity ( d’) and response criterion ( C). Results suggest that character gender had significant effects on d’ and C such that male character models resulted in greater perceptual sensitivity and a more liberal response criterion. Our findings align with previous research that characterize females as less likely to be targeted as a threat, possibly due to stereotypes or predisposed social biases, as opposed to males.

Supporting Social Interactions In Human-Ai Teams: Profiling Human Teammates From Sparse Data

“…For example, one study found a significant effect of player gender and age on game preferences such that, on average, females were more inclined to enjoy casual and artistic play, puzzles, and virtual goods, whereas males were found to be more inclined to enjoy strategy, sports, action, multiplayer, and competitive play (Tondello et al, 2019). Similarly, another study found sexrelated differences in performance on a virtual reality-based simulating the detection of threatening objects carried by otherwise inconspicuous avatars; it was found that males exhibited greater perceptual sensitivity and a more liberal response criterion than females (Williams et al, 2019). These studies are examples of a number across different domains or research approaches that demonstrate the influence that demographics have on different kinds of human behavior (Wells, 1975).…”

Towards a Conceptual Framework of Comprehensive Video Game Player Profiles: Player Models, Mental Models, and Behavior Models

“…In order to obtain a more complete understanding of gaming populations, we propose that researchers will need to capture individual differences regarding video game players and use that data to inform the construction of player models (Hooshyar et al, 2018). We consider the relevant individual differences in this population to include contributions from demographic data, such as age, race, and gender (e.g., Williams et al, 2019), as well as psychographic data including an individual's preferences, habits, idiosyncrasies, interests, etc. (Wells, 1975).…”