Presentation description

Our research investigates the impact of user-controlled cues on visual search performance and quitting behavior in a controlled experimental setting. Visual search tasks require participants to locate specific targets among distractors, a skill critical in fields such as radiology for accurate diagnosis from medical images.
Participants engaged in a visual search task where they identified a rotated "T" among rotated "L"s presented on a complex visual background. The study employed a between-subjects design with four experimental conditions: Addition Cue, Removal Cue, Automatic, and Baseline. In the Addition Cue condition, participants triggered a red circle cue by pressing a key, which sometimes highlighted the target "T". Conversely, in the Removal Cue condition, participants removed this cue by keypress before responding. The Automatic condition presented cues simultaneously with the display, while the Baseline condition had no cues.
Key interests included reaction times (RTs) and accuracy rates across different trial types: target-present (where a "T" was present among "L"s) and target-absent (where all items were "L"s). The study hypothesized that user-controlled cues would mitigate early quitting behavior observed in visual search tasks. Early quitting refers to premature task abandonment due to distracting cues.
Understanding how user-controlled cues influence visual search efficiency can inform the development of enhanced imaging systems and training protocols, particularly in medical imaging contexts. By improving radiologists' ability to detect subtle abnormalities amidst distractors, this research has practical implications for enhancing diagnostic accuracy and patient outcomes in clinical practice.
The study utilized a rigorous experimental approach with random assignment and multiple blocks of trials, ensuring robust findings regarding the effects of user-controlled cues on visual search behavior. These findings contribute to advancing knowledge on cognitive control mechanisms in complex visual environments, with potential applications beyond medical