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 Bchange-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/.

Adaptive decision making in probabilistic environments requires individuals to use probabilities as weights in predecisional information searches and/or when making subsequent choices. Within a child-friendly computerized environment (Mousekids), we tracked 205 children's (105 children 5-6 years of age and 100 children 9-10 years of age) and 103 adults' (age range: 21-22 years) search behaviors and decisions under different probability dispersions (.17; .33, .83 vs. .50, .67, .83) and constraint conditions (instructions to limit search: yes vs. no). All age groups limited their depth of search when instructed to do so and when probability dispersion was high (range: .17-.83). Unlike adults, children failed to use probabilities as weights for their searches, which were largely not systematic. When examining choices, however, elementary school children (unlike preschoolers) systematically used probabilities as weights in their decisions. This suggests that an intuitive understanding of probabilities and the capacity to use them as weights during integration is not a sufficient condition for applying simple selective search strategies that place one's focus on weight distributions.

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Decisions are occasionally accompanied by changes-of-mind. While considered a hallmark of cognitive flexibility, the mechanisms underlying changes-of-mind remain elusive. Previous studies on perceptual decision making have focused on changes-of-mind that are primarily driven by the accumulation of additional noisy sensory evidence after the initial decision. In a motion discrimination task, we demonstrate that changes-of-mind can occur even in the absence of additional evidence after the initial decision. Unlike previous studies of changes-of-mind, the majority of changes-of-mind in our experiment occurred in trials with prolonged initial response times. This suggests a distinct mechanism underlying such changes. Using a neural circuit model of decision uncertainty and change-of-mind behaviour, we demonstrate that this phenomenon is associated with top-down signals mediated by an uncertainty-monitoring neural population. Such a mechanism is consistent with recent neurophysiological evidence showing a link between changes-of-mind and elevated top-down neural activity. Our model explains the long response times associated with changes-of-mind through high decision uncertainty levels in such trials, and accounts for the observed motor response trajectories. Overall, our work provides a computational framework that explains changes-of-mind in the absence of new post-decision evidence.

Mouse-tracking is an increasingly popular process-tracing method. It builds on the assumption that the continuity of cognitive processing leaks into the continuity of mouse movements. Because this assumption is the prerequisite for meaningful reverse inference, it is an important question whether the assumed interaction between continuous processing and movement might be influenced by the methodological setup of the measurement. Here we studied the impacts of three commonly occurring methodological variations on the quality of mouse-tracking measures, and hence, on the reported cognitive effects. We used a mouse-tracking version of a classical intertemporal choice task that had previously been used to examine the dynamics of temporal discounting and the date-delay effect (Dshemuchadse, Scherbaum, & Goschke, 2013). The data from this previous study also served as a benchmark condition in our experimental design. Between studies, we varied the starting procedure. Within the new study, we varied the response procedure and the stimulus position. The starting procedure had the strongest influence on common mouse-tracking measures, and therefore on the cognitive effects. The effects of the response procedure and the stimulus position were weaker and less pronounced. The results suggest that the methodological setup crucially influences the interaction between continuous processing and mouse movement. We conclude that the methodological setup is of high importance for the validity of mouse-tracking as a process-tracing method. Finally, we discuss the need for standardized mouse-tracking setups, for which we provide recommendations, and present two promising lines of research toward obtaining an evidence-based gold standard of mouse-tracking. Keywords Mouse-tracking. Action dynamics. Process-tracing. Boundary conditions. Intertemporal choice Decision science has experienced a paradigmatic shift evolving its focus, methods, and approaches from an outcomebased perspective toward a more process-oriented paradigm (Oppenheimer & Kelso, 2015). This process paradigm acknowledges the temporal nature of basic mental processes and, hence, builds theories of choice incorporating perceptual, attentional, memory, and decisional processes. To test these process explanations, process-tracing methods are required. In the last 60 years, decision scientists introduced a variety of process-tracing methods to the field-for example, verbal protocols (e.g., Ericson & Simon, 1984), eye tracking (e.g., Russo & Rosen, 1975), and most recently, mouse-tracking (e.g., Dale, Kehoe, & Spivey, 2007; Spivey, Grosjean, & Knoblich, 2005) (for an overview, please see Schulte-Mecklenbeck et al., 2017). Whenever scientists apply such process-tracing methods, they rely on specific prerequisites and core concepts in order to conduct the reverse inference (Poldrack, 2006): Reverse inference describes the reasoning by which the presence of a particular cognitive process is inferred from a pattern of neuroimaging or behavioral data (cf. Heit, 2015). One prerequi...