Individual interpretations of published experimental stimulus sets can diverge significantly from normative ratings. Prior research suggests that factors like response times and mouse trajectory toward or away from response options can be informative about individuals’ dynamic decision-making processes. The present study collected ratings of interpretations of published emotional images to inform whether responses on an emotional Stop-Signal Task (SST) reliably indicate inhibition performance or are impacted by atypical interpretation of emotional stimuli. It was hypothesized that typicality would not significantly affect response times (RTs), but would impact trajectory complexity, in that the trajectories of atypical ratings would yield greater areas under the curve (AUCs) and maximum deviations (MDs) than trajectories of typical ratings. Older (age = 66-86 yrs) and younger (age = 18-29 yrs) adults (N=59) completed an SST where 240 images with emotional valence (unpleasant/pleasant) served as the stop signals. After completing the SST participants rated each image (pleasant/unpleasant) according to their individual interpretations. We observed that counts for atypical ratings were low, with the average person rating only a few images atypically. Individual RTs, AUCs, and MDs were significantly greater for atypical than typical ratings, ts(46) > 2.998, ps< .01. The higher RTs, AUCs, and MDs for images with atypical than typical ratings suggest potential ambiguity or protracted deliberation in evaluations. Higher AUCs and MDs indicate attraction to opposite valence during affective processing, and more complex trajectories toward response selections require additional time. Evaluating RTs together with response trajectories offers insights into dynamic processes during affective decision making.

Dr. Jill Waring was highly influential in completing this project, as she provided one-on-one mentorship on the processes of designing computer tasks, secure data management, and data analysis. She guided Emily through every step of the project, providing critical feedback and insights that helped her grow as a scientist. 

Emily would also like to thank the members of the Cognitive and Affective Neuroscience of Aging lab for their contributions.