How Mouse-tracking Can Advance Social Cognitive Theory

Stillman, Paul E.; Shen, Xi; Ferguson, Melissa J.

doi:10.1016/j.tics.2018.03.012

Cited by 128 publications

(183 citation statements)

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“…Mouse-tracking is an increasingly popular process tracing technique that has been applied to a wide range of questions throughout many fields of psychology (see Chapters 9-10; see also Freeman, Dale, & Farmer, 2011;Stillman, Shen, & Ferguson, 2018).…”

Section: Mouse-tracking: a Practical Guide To Implementation And Analmentioning

confidence: 99%

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Mouse-tracking: A practical guide to implementation and analysis

Kieslich¹,

Henninger²,

Wulff³

et al. 2018

Preprint

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category (e.g., a bat, sharing both features with the correct category mammal and the incorrect category bird). Dale et al. (2007) hypothesized that for atypical exemplars, both response options would receive some degree of activation, whereas for the typical exemplars, activation would largely be limited to the correct category. Consequently, for atypical exemplars, the incorrect category should exert a stronger attraction, and mouse movements should deviate more in its direction even if participants finally choose the correct option. 2 Creating mouse-tracking experimentsIn this section we demonstrate how a mouse-tracking experiment can be created in OpenSesame (Mathôt et al., 2012). OpenSesame is a free, open-source software for creating experiments via a graphical user interface which additionally allows for full customization of studies using Python code. 3 To simplify the creation of mouse-tracking experiments inside this framework, we developed the mousetrap plugin for OpenSesame. Installation instructions and additional documentation for the plugin are available in its GitHub repository at https://github.com/pascalkieslich/mousetrap-os. Creating an experimentThe first step is to start OpenSesame and create a new experiment by clicking on File/New and selecting the default template. Experiments in OpenSesame are assembled from a set of items, for example, a sketchpad item for presenting graphical content on the screen, a keyboard_response item for collecting key presses, and a logger item for writing data into log files. Figure 1 shows the OpenSesame interface with the item toolbar on the left-hand side. To its right, the overview area represents the study's structure, in that the items therein are run sequentially from top to bottom.An experiment is built by dragging and dropping items from the toolbar into the overview area.Sequences can be used to run a number of items in succession. In addition, loop items can be used to repeatedly run sequences with some degree of variation, for example, trials with varying stimuli (Figure 1, right panel).

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Section: Mouse-tracking: a Practical Guide To Implementation And Analmentioning

confidence: 99%

“…A number of different indices have been suggested to quantify curvature (cf., Table 1). Their exact computation differs, but they are often highly correlated in practice (see Kieslich et al, 2018;Stillman et al, 2018). Deviations/areas above the direct path receive a positive sign and deviations/areas below receive a negative sign.…”

Section: Curvaturementioning

confidence: 99%

Mouse-tracking: A practical guide to implementation and analysis

Kieslich¹,

Henninger²,

Wulff³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, much attention has been paid to mouse-tracking. Mouse-tracking (MT), assessing the movement of the computer cursor in a two-alternative-choice task, is said to provide a real-time window into cognitive processing underlying conflict-driven judgments (Freeman, 2018;Stillman et al, 2018;Spivey, 2007.…”

Section: Discerning Mouse Trajectory Features With Drift Diffusion Modelmentioning

confidence: 99%

“…On the basis of this assumption, this new method has been applied in nearly all aspects of cognitive and social decision making studies, including delay discounting and stop-signal tasks (Dschemuchadse et al, 2013, O'Hora et al, 2016, numerical judgment (Xiao & Yamauchi, 2015), categorization (Dale, Kehoe, & Spivey, 2007), inductive reasoning (Yamauchi, Kohn, & Yu, 2007), linguistic judgment (Spivey, Grosjean, & Knoblich, 2005), racial and gender judgment of morphed face pictures Freeman, Pauker, Apfelbaum, & Ambady, 2009), attitudinal ambivalence toward certain topics (e.g., abortion) (Schneider et al, 2015;Wojnowicz, Ferguson, Dale, & Spivey, 2009), uncertainty in economic choices (Calluso, Committeri, Pezzulo, Lepora, & Tosoni, 2015), and among others (see for review , Freeman, 2018;Stillman et al, 2018;Yamauchi, Leontyev, & Wolfe, 2017).…”

Section: Figure 1 Comparison Between Keypress (Upper Panel) and Mousmentioning

confidence: 99%

“…Second, it is unknown how specific decision-making processes are linked to concrete mouse-movement features. For example, many studies investing decision-conflict effects assume that mouse movement features, such as the area under the curve (AUC), is associated with uncertainty and ambivalence of the decision-maker (Stillman et al, 2018). Although such a claim is intuitively appealing, it is unknown if this specific feature is linked to concrete parameters of decision making such as the decision threshold and evidence accumulation time (Ratcliff & McKoon, 2008).…”

Section: Figure 1 Comparison Between Keypress (Upper Panel) and Mousmentioning

confidence: 99%

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Discerning Mouse Trajectory Features with Drift Diffusion Model

Leontyev¹,

Yamauchi²

2020

Preprint

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In decision-making tasks, participants are commonly instructed to respond by pressing a key. This design provides information about how fast and accurate an individual can respond but does not allow a researcher to directly study the process of response selection. Recently, mouse cursor tracking has been applied to offset this limitation. However, it is unclear whether RT/accuracy-based measures and mouse movement features (e.g. velocity) assess the same cognitive processes. To clarify the relationship between mouse movement features and cognitive processes, we developed mouse-tracking versions of the stop-signal and delay discounting tasks and investigated a) whether people respond similarly in the tasks with traditional design and tasks employing mouse cursor tracking; b) which features of the decision-making process mouse movement measures correspond with. Although participants responded similarly in tasks with traditional and mouse tracking design, only a few mouse movement features were related to the elements of the decision-making process.

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Leaving with something: When do people experience an equity–efficiency conflict?

Gordon‐Hecker

Schneider

Shalvi

et al. 2020

Behavioral Decision Making

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When allocating resources, people often have to resolve a conflict between equity and efficiency concerns. That is, sometimes for everyone to receive the same amount of resources, some resources must be used suboptimally. However, it is unclear whether and how people account for the impact their allocation decisions would have on the recipients' outcome. In three experiments, we examine how the amount of resources allocated to the recipients influences allocators' decisions and use mouse tracking techniques to assess their conflict during the decision process. The results reveal that when an equitable allocation of resources led to neither recipients receiving anything nor imposed losses, people tended to prefer efficient allocations. Such allocations between recipients who may end up with no resources also evoke a greater conflict compared with allocations in which both recipients have some secured gains, suggesting that, in general, people want to be equitable but not when equity means that nobody gets anything. When maintaining equity can only be done by leaving recipients with no resources at all, equitable allocations evoke a greater conflict, and people are more likely to refrain from them.

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How Mouse-tracking Can Advance Social Cognitive Theory

Cited by 128 publications

References 100 publications

Mouse-tracking: A practical guide to implementation and analysis

Mouse-tracking: A practical guide to implementation and analysis

Discerning Mouse Trajectory Features with Drift Diffusion Model

Leaving with something: When do people experience an equity–efficiency conflict?

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