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Over the past decade, mouse tracking in choice tasks has become a popular method across psychological science. This method exploits hand movements as a measure of multiple response activations that can be tracked continuously over hundreds of milliseconds. Whereas early mouse-tracking research focused on specific debates, researchers have realized that the methodology has far broader theoretical value. This more recent work demonstrates that mouse tracking is a widely applicable measure across the field, capable of exposing the microstructure of real-time decisions, including their component processes and millisecond-resolution time course, in ways that inform theory. In this article, recent advances in the mouse-tracking approach are described, and comparisons with the gold standard measure of reaction time and other temporally sensitive methodologies are provided. Future directions, including mapping to neural representations with brain imaging and ways to improve our theoretical understanding of mouse-tracking methodology, are discussed.

Decision research has experienced a shift from simple algebraic theories of choice to an appreciation of mental processes underlying choice. A variety of process-tracing methods has helped researchers test these process explanations. Here, we provide a survey of these methods, including specific examples for subject reports, movement-based measures, peripheral psychophysiology, and neural techniques. We show how these methods can inform phenomena as varied as attention, emotion, strategy use, and understanding neural correlates. Two important future developments are identified: broadening the number of explicit tests of proposed processes through formal modeling and determining standards and best practices for data collection.

Emotion can influence important user behaviors, including purchasing decisions, technology use, and customer loyalty. The ability to easily assess users’ emotion during live system use therefore has practical significance for the design and improvement of information systems. In this paper, we discuss using human–computer interaction input devices to infer emotion. Specifically, we utilize attentional control theory to explain how movement captured via a computer mouse (i.e., mouse cursor movements) can be a real-time indicator of negative emotion. We report three studies. In Study 1, an experiment with 65 participants from Amazon’s Mechanical Turk, we randomly manipulated negative emotion and then monitored participants’ mouse cursor movements as they completed a number-ordering task. We found that negative emotion increases the distance and reduces the speed of mouse cursor movements during the task. In Study 2, an experiment with 126 participants from a U.S. university, we randomly manipulated negative emotion and then monitored participants’ mouse cursor movements while they interacted with a mock e-commerce site. We found that mouse cursor distance and speed can be used to infer the presence of negative emotion with an overall accuracy rate of 81.7 percent. In Study 3, an observational study with 80 participants from universities in Germany and Hong Kong, we monitored mouse cursor movements while participants interacted with an online product configurator. Participants reported their level of emotion after each step in the configuration process. We found that mouse cursor distance and speed can be used to infer the level of negative emotion with an out-of-sample R² of 0.17. The results enable researchers to assess negative emotional reactions during live system use, examine emotional reactions with more temporal precision, conduct multimethod emotion research, and create more unobtrusive affective and adaptive systems.

Navigating conflict is integral to decision-making, serving a central role both in the subjective experience of choice as well as contemporary theories of how we choose. However, the lack of a sensitive, accessible, and interpretable metric of conflict has led researchers to focus on choice itself rather than how individuals arrive at that choice. Using mouse-tracking—continuously sampling computer mouse location as participants decide—we demonstrate the theoretical and practical uses of dynamic assessments of choice from decision onset through conclusion. Specifically, we use mouse tracking to index conflict, quantified by the relative directness to the chosen option, in a domain for which conflict is integral: decisions involving risk. In deciding whether to accept risk, decision makers must integrate gains, losses, status quos, and outcome probabilities, a process that inevitably involves conflict. Across three preregistered studies, we tracked participants’ motor movements while they decided whether to accept or reject gambles. Our results show that 1) mouse-tracking metrics of conflict sensitively detect differences in the subjective value of risky versus certain options; 2) these metrics of conflict strongly predict participants’ risk preferences (loss aversion and decreasing marginal utility), even on a single-trial level; 3) these mouse-tracking metrics outperform participants’ reaction times in predicting risk preferences; and 4) manipulating risk preferences via a broad versus narrow bracketing manipulation influences conflict as indexed by mouse tracking. Together, these results highlight the importance of measuring conflict during risky choice and demonstrate the usefulness of mouse tracking as a tool to do so.

Fallacies are a particular type of informal argument that are psychologically compelling and often used for rhetorical purposes. Fallacies are unreasonable because the reasons they provide for their claims are irrelevant or insufficient. Ability to recognize the weakness of fallacies is part of what we call argument literacy and imporatant in rational thinking. Here we examine classic fallacies of types found in textbooks. In an experiment, participants evaluated the quality of fallacies and reasonable arguments. We instructed participants to think either intuitively, using their first impressions, or analytically, using rational deliberation. We analyzed responses, response times, and cursor trajectories (captured using mouse tracking). The results indicate that instructions to think analytically made people spend more time on the task but did not make them change their minds more often. When participants made errors, they were drawn towards the correct response, while responding correctly was more straightforward. The results are compatible with “smart intuition” accounts of dual-process theories of reasoning, rather than with corrective default-interventionist accounts. The findings are discussed in relation to whether theories developed to account for formal reasoning can help to explain the processing of everyday arguments.

Mouse tracking is an important source of data in cognitive science. Most contemporary mouse tracking studies use binary-choice tasks and analyze the curvature or velocity of an individual mouse movement during an experimental trial as participants select from one of the two options. However, there are many types of mouse tracking data available beyond what is produced in a binary-choice task, including naturalistic data from web users. In order to utilize these data, cognitive scientists need tools that are robust to the lack of trial-by-trial structure in most normal computer tasks. We use singular value decomposition (SVD) and detrended fluctuation analysis (DFA) to analyze whole time series of unstructured mouse movement data. We also introduce a new technique for describing two-dimensional mouse traces as complex-valued time series, which allows SVD and DFA to be applied in a straightforward way without losing important spatial information. We find that there is useful information at the level of whole time series, and we use this information to predict performance in an online task. We also discuss how the implications of these results can advance the use of mouse tracking research in cognitive science.

One important distinction in the syntax literature is between agreement that is to the nominal phrase and agreement that is to it (sometimes called ). How this type of agreement impacts sentence processing, however, is not well understood. In this paper, we ask whether agreement errors are processed differently based on their internal vs. external status. We investigate this question in Russian, a Slavic language that has a rich morphological agreement system and flexible word order, allowing us to control for several confounds. Our results are not fully conclusive but do provide moderate evidence that processing of agreement is modulated by internal vs. external status. We measure real-time language processing using Mouse Tracking for Reading (MoTR), a new web-deployable measurement tool that has been argued to improve over previous methods (e.g., self-paced reading) but has so far been tested only in English, and never for agreement processing phenomena. We find that MoTR can successfully pick up differences in our factorized psycholinguistic experiment in Russian, validating MoTR as a reliable tool for investigating agreement (error) processing. A direct comparison with existing data collected using in-lab eye-tracking-while-reading with similar experimental materials (Fuchs et al., ) suggests MoTR data yields larger effect sizes than does eye-tracking data.

Researchers have long sought to distinguish between single-process and dual-process cognitive phenomena, using responses such as reaction times and, more recently, hand movements. Analysis of a response distribution’s modality has been crucial in detecting the presence of dual processes, because they tend to introduce bimodal features. Rarely, however, have bimodality measures been systematically evaluated. We carried out tests of readily available bimodality measures that any researcher may easily employ: the bimodality coefficient (BC), Hartigan’s dip statistic (HDS), and the difference in Akaike’s information criterion between one-component and two-component distribution models (AIC diff ). We simulated distributions containing two response populations and examined the influences of (1) the distances between populations, (2) proportions of responses, (3) the amount of positive skew present, and (4) sample size. Distance always had a stronger effect than did proportion, and the effects of proportion greatly differed across the measures. Skew biased the measures by increasing bimodality detection, in some cases leading to anomalous interactive effects. BC and HDS were generally convergent, but a number of important discrepancies were found. AIC diff was extremely sensitive to bimodality and identified nearly all distributions as bimodal. However, all measures served to detect the presence of bimodality in comparison to unimodal simulations. We provide a validation with experimental data, discuss methodological and theoretical implications, and make recommendations regarding the choice of analysis.

Mouse-tracking is regarded as a powerful technique to investigate latent cognitive and emotional states. However, drawing inferences from this manifold data source carries the risk of several pitfalls, especially when using aggregated data rather than single-trial trajectories. Researchers might reach wrong conclusions because averages lump together two distinct contributions that speak towards fundamentally different mechanisms underlying between-condition differences: influences from online-processing during action execution and influences from incomplete decision processes. Here, we propose a simple method to assess these factors, thus allowing us to probe whether process-pure interpretations are appropriate. By applying this method to data from 12 published experiments on ideomotor action control, we show that the interpretation of previous results changes when dissociating online processing from decision and initiation errors. Researchers using mouse-tracking to investigate cognition and emotion are therefore well advised to conduct detailed trial-by-trial analyses, particularly when they test for direct leakage of ongoing processing into movement trajectories.

Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon.