Purchase this article with an account.
Liam Rourke, Verena Willenbockel, Leanna Cruickshank, Jim Tanaka; The neural correlates of medical expertise.. Journal of Vision 2015;15(12):1131. doi: 10.1167/15.12.1131.
Download citation file:
© 2017 Association for Research in Vision and Ophthalmology.
Previous research using event-related potentials (ERPs) has shown that the N170 component is enhanced when experts categorize objects in their domain of expertise relative to when they categorize objects outside of their domain (Tanaka & Curran, 2001). Here, we replicated Tanaka and Curran’s study on bird and dog experts with medical experts in reading electrocardiography (ECG) and chest X-ray (CXR) images. 16 physicians (8 cardiologists, 8 pulmonologists) participated in the ERP study. On a scale of 1 (zero expertise) to 9 (much expertise), cardiologists and pulmonologists rated their expertise with ECG images as 8.50 and 5.75, respectively, and with CXR images as 5.00 and 7.63, respectively. On each of 520 trials, participants viewed one of 7 ECG or CXR patterns. The pattern image was preceded by a correct or incorrect label either at the basic (“ECG”, “CXR”) or subordinate (e.g., “attrial flutter”, “pneumonia”) level. Participants were asked to indicate with a key press whether the label and diagnostic image matched or not. A mixed ANOVA on the accuracy data from both groups showed a significant interaction between stimulus type and expertise—participants were better at categorizing ECG than CXR images, particularly the Cardiologists (F = 5.8, p < .05). Closely reflecting the behavioural results, a mixed ANOVA on the N170 mean amplitudes from correct trials showed a significant interaction between stimulus type, expertise, and hemisphere. The N170 was larger to ECG than CXR images, especially for Cardiologists, and in the right hemisphere (F = 6.97, p < .05). In contrast, no significant effects were found for the P100. These findings indicate that the N170 amplitude not only reflects object expertise but can also be modulated by expertise in pattern recognition.
Meeting abstract presented at VSS 2015
This PDF is available to Subscribers Only