Distinct Patterns of Cognitive Conflict Dynamics in Promise Keepers and Promise Breakers

Abstract

On a daily basis, we see how different people can be in keeping or breaking a given promise. However, we know very little about the cognitive conflict dynamics that underlie the decision to keep or break a promise and whether this is shaped by inter-individual variability. In order to fill this gap, we applied an ecologically valid promise decision task with real monetary consequences for all involved interaction partners and used mouse tracking to identify the dynamic, on-line cognitive processes that underlie the decision to keep or break a promise. Our findings revealed that on average, the process of breaking a promise is associated with largely curved mouse trajectories, while the process of keeping a promise was not, indicating that breaking a promise is associated with a larger conflict. Interestingly, however, this conflict pattern was strongly shaped by individual differences. Individuals who always kept their promises did not show any signs of conflict (i.e., straight mouse trajectories), indicating that they were not tempted by the monetary benefits associated with breaking the promise. In contrast, individuals who did not always keep their promise exhibited a large conflict (i.e., curved mouse trajectories), irrespective of whether they broke or kept their promise. A possible interpretation of these findings is that these individuals were always tempted by the unchosen decision option – the desire to act in a fair manner when breaking the promise and the monetary benefits when keeping the promise. This study provides the first piece of evidence that there are substantial inter-individual differences in cognitive conflict dynamics that underlie the decision to keep or break promises and that mouse tracking is able to illuminate important insights into individual differences in complex human’s decision processes.

Cognitive Dynamics of Religiosity and Intertemporal Choice Behavior

“…MD was calculated as the maximum perpendicular deviation between the real and the theoretical trajectories—that is, straight line connecting the start- and end-point of the mouse movement—while the AUC was calculated as the geometric area between them (Freeman et al, 2011; Freeman & Ambady, 2010). High values associated with MD and AUC indicate larger deviations toward the unselected choice option and are used as a proxy for uncertainty and cognitive conflict (Calluso et al, 2018; Freeman et al, 2011). Intuitively, a straight line connecting the “start” button to the selected option implies no uncertainty (i.e., the mouse is directed to the preferred option with no hesitation), whereas deviations from the straight line imply uncertainty and cognitive conflict whose magnitude is a function of the extent of the deviation.…”

“…The statistical significance of the effects was assessed using mixed effects-models (Baayen et al, 2008; Bates et al, 2015; R Core Team, 2014) because they are able to account for between-subject differences, which are unrelated to the task design/experimental manipulation. Additionally, such approach represents a gold standard analysis when dealing with mouse kinematics data to account for participants’ basic motor properties (e.g., a subject can show higher trajectories’ curvature or can be faster/slower compared to another, independently from the decision itself) and was also employed in many previous mouse-tracking studies (Barca & Pezzulo, 2012; Calluso et al, 2018, 2019, 2020; O’Hora et al, 2013a, 2016; Quétard et al, 2016; Quinton et al, 2013; Tabatabaeian et al, 2015).…”

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

“…For instance, when evaluating tasty but unhealthy food, people experience a conflict between the short‐term satisfaction and the long‐term consequences of unhealthy eating (Gillebaart, Schneider, & De Ridder, 2016; Schneider et al, 2015; Schneider, Gillebaart, & Mattes, 2019). Similarly, in interpersonal behaviors, people experience a conflict when choosing between their desire to increase their payoff and their social duty to cooperate with others (Kieslich & Hilbig, 2014) or to keep their promises (Calluso, Saulin, Baumgartner, & Knoch, 2018). Because equity is an important value for people (Adams, 1965; Fehr & Schmidt, 1999), having to pay high costs for it may put decision makers in a difficult dilemma.…”

“…To assess the level of conflict, we used mouse tracking techniques. In recent years, mouse tracking techniques have been successfully used to study cognitive conflict (Freeman & Ambady, 2010) in different decisions domains, such as food choices (Gillebaart et al, 2016; Schneider et al, 2015; Schneider & Schwarz, 2017), temporal discounting (Stillman & Ferguson, 2019), and more complex interpersonal behaviors (Calluso et al, 2018; Kieslich & Hilbig, 2014). The advantage of these techniques is that they bypass self‐report, which can be prone to biases and social desirability.…”

Movement tracking of psychological processes: A tutorial using mousetrap

“…tinuously translates into movement. This view is clearly embodied by existing computational accounts of movement tracking processes, including attractor models (Frisch et al, 2015;Scherbaum et al, 2016;Spivey et al, 2005;Zgonnikov et al, 2017) and drift-diffusion models (Calluso et al, 2018;Wong et al, 2015). According to these models, a latent preference for the available option evolves until either a stable (attractor models) or an extreme enough (diffusion models) point of preference for one of the options is reached.…”