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The Effect of Task-Relevant Salient Signal on Visual Search Tasks

Semester: Summer 2023

Presentation description

In this project we tracked and recorded eye movements during a visual search task with salient signals to explore the strategies employed by participants while looking for a target. Previous research (Moher, 2020) found that task-irrelevant salient distractors influenced the participants to quit the search earlier , a phenomenon referred to as the early quitting effect. Our objective in this project is to explore if task-relevant salient signals cause a similar quitting effect.

Participants are asked to search for the rotated letter Ts among rotated letter Ls in a noisy background while our eye tracker records their eye movements. A between-subject design is employed; half of the participants are assigned to a salient signal present condition, in which red circles sometimes appear around a letter and the other half of the participants are assigned to no salient signal present condition. For the first group of subjects, when a target is present, 75% of the time, the salient signals highlight the target, while the remaining 25% of salient signals highlight the different parts, making the trial task-relevant. Salient signals are inspired by computer-aided detection (CAD), in which computers process medical images, attempt to find relevant locations for more detailed observation, and convey these locations to human medical image readers, often via salient signals overlaid on a medical image.

We hypothesized that when CAD is present but not highlighting the target, it will cause early quitting coupled with lower accuracy, and smaller search coverage compared to no-CAD condition. Furthermore, in comparison to the previous findings, this quitting effect may be even greater as the salient signals become more task-relevant. The results of this study will further the understanding of human information processing and carry potential practical relevance for similar search tasks such as those employed by radiologists analyzing medical images for oncological screenings.

Presenter Name: Eunseo Han, Maiko Okamura

Presentation Type: Poster
Presentation Format: In Person
Presentation #8
College: Social & Behavioral Science
School / Department: Psychology
Research Mentor: Brennan Payne
Date | Time: Thursday, Aug 3rd | 9:00 AM