Anxiety Influences the Perceptual-Motor Calibration of Visually Guided Braking to Avoid Collisions

ABSTRACT

We investigated whether anxiety influences perceptual-motor calibration in a braking to avoid a collision task. Participants performed either a discrete braking task (Experiment 1) or a continuous braking task (Experiment 2), with the goal of stopping before colliding with a stop sign. Half of participants performed the braking task after an anxiety induction. We investigated whether anxiety reduced the frequency of crashing and if it influenced the calibration of perception (visual information) and action (brake pressure) dynamically between-trials in Experiment 1 and within-trials in Experiment 2. In the discrete braking task, anxious participants crashed less often and made larger corrective adjustments trial-to-trial after crashing, suggesting that the influence of anxiety on behavior did not occur uniformly, but rather dynamically with anxiety amplifying the reaction to previous crashes. However, when performing continuous braking, anxious participants crashed more often, and their within-trial adjustments of deceleration were less related to visual information compared to controls. Taken together, these findings suggest that the timescale and nature of the task mediates the influence of anxiety on the performance of goal-directed actions.

Keywords:

• anxiety
• visually guided actions
• braking
• dynamical systems
• ecological psychology

ACKNOWLEDGMENTS

The authors thank Garrett Allen for his help with programming and Jenna Monson, Carly Straley, and Blane Anderson for their help collecting data.

Notes

¹ A number of studies show that braking behavior is regulated with respect to D_ideal. Nonetheless, even if there are individual differences in the information variable that is detected, they are likely confounded in this task, because we did not experimentally manipulate some visual information (e.g., the optic flow or the eye height, cf. Fajen, 2005a).

² Although participants' were instructed that braking would be similar to emergency braking, in the sense that pulling trigger would be like slamming on the brakes, the most important aspect of the instructions was to stop as close as possible to the stop sign. Based on observations of participants, it seems clear that they followed these instructions.

³ All participants in the control (big straw) condition were able to breathe through the straw for the full 2 min. In the anxiety (little straw) condition, participants were able to breathe through the straw for an average of 58.2 s (SD = 34.77 s). There was no correlation between average time breathing through the straw and change in SUDs ratings from before to after breathing through the straw (r = 0.011, p = 0.960) suggesting that the manipulation of anxiety did not differ across participants who spent more or less time breathing through the straw.

⁴ The use of the breathing task and the SUDS scale made it possible for participants to guess the purpose of the experiment (or at least that our manipulations involved testing anxiety). We did not ask participants whether they intuited our hypotheses; however, the breathing and SUDS scale were used in both anxiety conditions in both experiments, so the presence of demand characteristics should have equally affected both conditions, in similar directions across both experiments, if it did have an effect.

⁵ The first derivative of D_Ideal was created using a single lag toeplitz design and averaging across lags using the GOLD method (Deboeck, 2010). A single lag was used as Average Mutual Information indicated that this was the smallest lag not influenced by autocorrelations (Hausser & Strimmer, 2009).

⁶ A MLM was appropriate for modeling our data and testing our hypotheses for two major reasons: (1) MLM allows for the inclusion of interactions between continuous variables (in our case, visual information variables) and categorical variables (in our case, anxiety condition); (2) MLM uses robust estimation procedures appropriate for partitioning variance and error structures in mixed and nested designs (repeated measures nested within individuals in this case), including modeling interaction variables across levels.

⁷ Crash was coded such that 0 indicated a successful stop and 1 indicated a crash. Crash was included as a variable because the relationship among variables will necessarily differ when one does not apply enough braking force to stop. Condition was coded such that the control (big straw) condition was 0 and the anxiety (little straw) condition was 1.