Costly avoidance of Pavlovian fear stimuli and the temporal dynamics of its decision process

Abstract

Conflicts between avoiding feared stimuli versus approaching them for competing rewards are essential for functional behavior and anxious psychopathology. Yet, little is known about the underlying decision process. We examined approach-avoidance decisions and their temporal dynamics when avoiding Pavlovian fear stimuli conflicted with gaining rewards. First, a formerly neutral stimulus (CS+) was repeatedly paired with an aversive stimulus (US) to establish Pavlovian fear. Another stimulus (CS−) was never paired with the US. A control group received neutral tones instead of aversive USs. Next, in each of 324 trials, participants chose between a CS−/low reward and a CS+/high reward option. For the latter, probability of CS+ presentation (Pavlovian fear information) and reward magnitude (reward information) varied. Computer mouse movements were tracked to capture the decision dynamics. Although no more USs occurred, pronounced and persistent costly avoidance of the Pavlovian fear CS+ was found. Time-continuous multiple regression of movement trajectories revealed a stronger and faster impact of Pavlovian fear compared to reward information during decision-making. The impact of fear information, but not reward information, modestly decreased across trials. These findings suggest a persistently stronger weighting of fear compared to reward information during approach-avoidance decisions, which may facilitate the development of pathological avoidance.

Hand movement trajectories illustrate the mechanism underlying Kurt Lewin’s distinction between approach–approach and avoidance–avoidance motivational conflicts.

“…We computed conflict-strength measures for each of the trajectories: (a) Maximum Deviation (MD; Boschet et al, 2022; Cummins & De Houwer, 2021; Dale & Duran, 2011; Freeman, 2014; Freeman & Ambady, 2009; Freeman et al, 2008; Garcia-Guerrero et al, 2023; Maldonado et al, 2019; McKinstry et al, 2008; Stillman et al, 2017)—the largest perpendicular deviation of the actual trajectory from an ideal straight line between the start and end points, and (b) Area under the Curve (AUC; Boschet et al, 2022; Cummins & De Houwer, 2021; Dale et al, 2007; Freeman & Ambady, 2010; Garcia-Guerrero et al, 2023; Leontyev & Yamauchi, 2021; Spivey et al, 2005; Stillman et al, 2017, 2020)—the geometric area between the actual trajectory and an ideal line. We further computed two measures corresponding to the mechanistic properties of the force-fields theory: (c) x-flips (to measure oscillatory behavior; Dale & Duran, 2011; Dale et al, 2008; Freeman, 2014; Freeman & Ambady, 2010; Freeman & Johnson, 2016)—the number of reversals of direction along the x -axis, and (d) Returns to Point of Balance (RPB; to quantify the extent to which participants were stuck in the middle between the options in a given trial)—the number of returns to the line where the x coordinate equals 0 (Koop & Johnson, 2013).…”

“…We used MD and AUC to quantify conflict-strength as reflected in the mouse trajectories (Boschet et al, 2022;Garcia-Guerrero et al, 2023;Stillman et al, 2017Stillman et al, , 2020. The average mouse trajectories across all participants and all trials in each condition are plotted in Figure 1.…”