How Mouse-tracking Can Advance Social Cognitive Theory

Mouse movement measures enhance the stop-signal task in adult ADHD assessment

The accurate detection of attention-deficit/hyperactivity disorder (ADHD) symptoms, such as inattentiveness and behavioral disinhibition, is crucial for delivering timely assistance and treatment. ADHD is commonly diagnosed and studied with specialized questionnaires and behavioral tests such as the stop-signal task. However, in cases of late-onset or mild forms of ADHD, behavioral measures often fail to gauge the deficiencies well-highlighted by questionnaires. To improve the sensitivity of behavioral tests, we propose a novel version of the stop-signal task (SST), which integrates mouse cursor tracking. In two studies, we investigated whether introducing mouse movement measures to the stop-signal task improves associations with questionnaire-based measures, as compared to the traditional (keypress-based) version of SST. We also scrutinized the influence of different parameters of stop-signal tasks, such as the method of stop-signal delay setting or definition of response inhibition failure, on these associations. Our results show that a) SSRT has weak association with impulsivity, while mouse movement measures have strong and significant association with impulsivity; b) machine learning models trained on the mouse movement data from "known" participants using nested cross-validation procedure can accurately predict impulsivity ratings of "unknown" participants; c) mouse movement features such as maximum acceleration and maximum velocity are among the most important predictors for impulsivity; d) using preset stop-signal delays prompts behavior that is more indicative of impulsivity.

Social Judgments of Digitally Manipulated Stuttered Speech: An Evaluation of Self-Disclosure on Cognition

Purpose Persons who stutter (PWS) may be susceptible to discrimination because of negative judgments made by listeners. The current study sought to determine how the cognitive system's explicit (i.e., conscious) and implicit (i.e., nonconscious) biases about PWS are impacted by self-disclosure. Method A computer mouse-tracking paradigm was used to evaluate categorical social judgments about PWS. Computer mouse trajectories, which have been shown to reveal underlying cognitive pull or competition between opposing concepts, were used to measure implicit bias (i.e., nonconscious stereotypes). Participants were asked to explicitly categorize the speaker as either intelligent or unintelligent before and after listening to a speaker self-disclose. Mouse cursor trajectories during the explicit response categorization were used to evaluate implicit bias associated with the decision-making process. Results Results indicated that participants chose "intelligent" for a higher proportion of the trials in the disclosure condition compared to baseline, showing that listeners' explicit biases changed after listening to a self-disclosure that the speaker stutters. Results also indicated listeners exhibited a more negative implicit bias, based on computer mouse trajectories, when rating the PWS relative to the "persons who do not stutter" talker, but this negative implicit bias did seem to reduce over time after the disclosure was made. Conclusions These findings indicate that, even though explicit and implicit biases were evident when listeners heard stuttering, both explicit and implicit biases seemed to extinguish over time after a self-disclosure. Although the bias was not completely extinguished, these results provide promising evidence toward developing methods to reduce negative beliefs and reactions toward PWS. Supplemental Material http://osf.io/mwrp7/.

Dynamic interactive theory as a domain-general account of social perception

The perception of social categories, emotions, and personality traits from others' faces each have been studied extensively but in relative isolation. We synthesize emerging findings suggesting that, in each of these domains of social perception, both a variety of bottom-up facial features and top-down social cognitive processes play a part in driving initial perceptions. Among such top-down processes, social-conceptual knowledge in particular can have a fundamental structuring role in how we perceive others' faces. Extending the Dynamic Interactive framework (Freeman & Ambady, 2011), we outline a perspective whereby the perception of social categories, emotions, and traits from faces can all be conceived as emerging from an integrated system relying on domain-general cognitive properties. Such an account of social perception would envision perceptions to be a rapid, but gradual, process of negotiation between the variety of visual cues inherent to a person and the social cognitive knowledge an individual perceiver brings to the perceptual process. We describe growing evidence in support of this perspective as well as its theoretical implications for social psychology.

Lost to translation: How design factors of the mouse-tracking procedure impact the inference from action to cognition

From an embodiment perspective, action and cognition influence each other constantly. This interaction has been utilized in mouse-tracking studies to infer cognitive states from movements, assuming a continuous manifestation of cognitive processing into movement. However, it is mostly unknown how this manifestation is affected by the variety of possible design choices in mouse-tracking paradigms. Here we studied how three design factors impact the manifestation of cognition into movement in a Simon task with mouse tracking. We varied the response selection (i.e., with or without clicking), the ratio between hand and mouse cursor movement, and the location of the response boxes. The results show that all design factors can blur or even prevent the manifestation of cognition into movement, as reflected by a reduction in movement consistency and action dynamics, as well as by the adoption of unsuitable movement strategies. We conclude that deliberate and careful design choices in mouse-tracking experiments are crucial to ensuring a continuous manifestation of cognition in movement. We discuss the importance of developing a standard practice in the design of mouse-tracking experiments.

Resolving the Human–Object Divide in Sexual Objectification: How We Settle the Categorization Conflict When Categorizing Objectified and Nonobjectified Human Targets

Using a mouse-tracking technique, we measured the strength and the temporal unfolding of the conflict when people categorize objectified and nonobjectified human stimuli in the human or object category. We recorded participants’ hand movements when they categorized male and female, objectified and nonobjectified, human, and doll-like stimuli in the person and object categories. As expected, objectified women created a stronger categorization conflict compared to all other human stimuli. The nature of the mouse trajectories indicated that this response competition was caused by the distractor (object category) rather than the target (person category) and showed to be smooth rather than abrupt suggesting dynamic competition between the object–human categories rather than the sequential unfolding of a dual process. These findings demonstrate that the human–object divide fades when women (but not men) are objectified. The implications of the current findings for theorizing on processes of sexual objectification are discussed.

Design factors in mouse-tracking: What makes a difference?

Investigating cognitive processes by analyzing mouse movements has become a popular method in many psychological disciplines. When creating mouse-tracking experiments, researchers face many design choices-for example, whether participants indicate responses by clicking a button or just by entering the button area. Hitherto, numerous different settings have been employed, but little is known about how these methodological differences affect mouse-tracking data. We systematically investigated the influences of three central design factors, using a classic mouse-tracking paradigm in which participants classified typical and atypical exemplars. In separate experiments, we manipulated the response indication, mouse sensitivity, and starting procedure. The core finding that mouse movements deviate more toward the nonchosen option for atypical exemplars was replicated in all conditions. However, the size of this effect varied. Specifically, it was larger when participants indicated responses via click and when they were instructed to initialize the movement early. Trajectory shapes also differed between setups. For example, a dynamic start led to mostly curved trajectories, responses via click led to a mix of straight and "change-of-mind" trajectories, and responses via touch led to mostly straight trajectories. Moreover, the distribution of curvature indices was classified as bimodal in some setups and as unimodal in others. Because trajectory curvature and shape are frequently used to make inferences about psychological theories, such as differentiating between dynamic and dual-system models, this study shows that the specific design must be carefully considered when drawing theoretical inferences. All methodological designs and analyses were implemented using open-source software and are available from https://osf.io/xdp7a/.

Minding One's Reach (To Eat): The Promise of Computer Mouse-Tracking to Study Self-Regulation of Eating

In this review, we present the case for using computer mouse-tracking techniques to examine psychological processes that support (and hinder) self-regulation of eating. We first argue that computer mouse-tracking is suitable for studying the simultaneous engagement of-and dynamic interactions between-multiple perceptual and cognitive processes as they unfold and interact over a fine temporal scale (i.e., hundreds of milliseconds). Next, we review recent work that implemented mouse-tracking techniques by measuring mouse movements as participants chose between various food items (of varying nutritional content). Lastly, we propose next steps for future investigations to link behavioral features from mouse-tracking paradigms, corresponding neural correlates, and downstream eating behaviors.