Real World Goals Are Fickle and Volatile: Consuming High Fat Foods Reduces Distraction from Entirely Irrelevant High-Fat Foods

Abstract

Due to the limited processing capacity of the human visual system, it is critical to selectively respond to stimuli that are the most relevant to our current goals. While much of the research on goal-driven attentional capture investigates goals developed through artificial training (e.g. red stimuli are worth $1.00), it has failed to investigate whether naturally occurring goal states also influence attentional deployment. In the present study, we used images of high-fat foods as a case study. We hypothesized that since high-fat foods are desired by most individuals, we should see a larger amount of distraction for these images compared to other non-food objects (e.g. a bicycle). Utilizing a similar task to Forster & Lavie (2011), observers were asked to make continuous sequential forced-choice responses to each of 4 symbols in the center of the screen (e.g., is it a letter or number?). This task provided a unique experimental setup that allowed for the brief onset (125 msecs) of distractor images to be entirely irrelevant. In Experiment 1, we presented observers with three kinds of distractor images: high-fat foods, low-fat foods, and non-food objects. Results revealed that observers were significantly more distracted by high-fat foods, compared to non-food objects and even low-fat foods, suggesting observers rapidly assessed the nutritional value of the distractor images. In Experiment 2, we investigated the lability of these naturally occurring goal states by having observers consume a high-fat food (a candy bar) prior to the task. The amount of distraction by the high-fat food images was significantly less than distraction by images of low-fat foods and images of non-food objects, resulting in a significant interaction across the two experiments. These results (replicated in a second pair of experiments) demonstrate that while naturally occurring goal states can be difficult to ignore, they are also highly flexible. Meeting abstract presented at VSS 2016