Back

The Effect of the Glossary on the Reliability and Performance of the Reading the Mind in the Eyes Test (RMET)

Toloza-Munoz, J.; Gonzalez-Mendoza, J.; Castillo, R. D.; Morales-Bader, D.

2020-03-25 animal behavior and cognition
10.1101/2020.03.25.007575 bioRxiv
Show abstract

The Reading the Mind in the Eyes Test (RMET) is used to measure high-level Theory of Mind. RMET consists of images of the regions surrounding the eyes and a glossary of terms that defines words associated with the gazes depicted in the images. People must identify the meaning associated with each gaze and can consult the glossary as they respond. The results indicate that typically developed adults perform better than adults with neurodevelopmental disorders. However, the evidence regarding the validity and reliability of the test is contradictory. This study evaluated the effect of the glossary on the performance, internal consistency, and temporal stability of the test. A total of 89 undergraduate students were randomly assigned to three conditions. The first group used the original glossary (Ori-G). The second group developed a self-generated glossary of gazes (Self-G). Finally, the third group developed a glossary that did not define gazes, but unrelated words instead (No-G). The test was administered before and after participants drew a randomly assigned image as a secondary task. The findings show that the number of correct answers was similar among the three conditions before and after the secondary task. However, the Self-G and No-G groups took less time to finish the test. The type of glossary affected the consistency and stability of the test. In our case, the Self-G condition made the responses faster, more consistent, and more stable. The results are discussed in terms of levels of processing and the detection of mental states based on gazes.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.