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Watching diagnoses develop: Eye movements reveal symptom processing during diagnostic reasoning

Overview of attention for article published in Psychonomic Bulletin & Review, April 2017
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Title
Watching diagnoses develop: Eye movements reveal symptom processing during diagnostic reasoning
Published in
Psychonomic Bulletin & Review, April 2017
DOI 10.3758/s13423-017-1294-8
Pubmed ID
Authors

Agnes Scholz, Josef F. Krems, Georg Jahn

Abstract

Finding a probable explanation for observed symptoms is a highly complex task that draws on information retrieval from memory. Recent research suggests that observed symptoms are interpreted in a way that maximizes coherence for a single likely explanation. This becomes particularly clear if symptom sequences support more than one explanation. However, there are no existing process data available that allow coherence maximization to be traced in ambiguous diagnostic situations, where critical information has to be retrieved from memory. In this experiment, we applied memory indexing, an eye-tracking method that affords rich time-course information concerning memory-based cognitive processing during higher order thinking, to reveal symptom processing and the preferred interpretation of symptom sequences. Participants first learned information about causes and symptoms presented in spatial frames. Gaze allocation to emptied spatial frames during symptom processing and during the diagnostic response reflected the subjective status of hypotheses held in memory and the preferred interpretation of ambiguous symptoms. Memory indexing traced how the diagnostic decision developed and revealed instances of hypothesis change and biases in symptom processing. Memory indexing thus provided direct online evidence for coherence maximization in processing ambiguous information.

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The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 5 16%
Student > Master 5 16%
Student > Ph. D. Student 5 16%
Professor 4 13%
Student > Doctoral Student 2 6%
Other 8 25%
Unknown 3 9%
Readers by discipline Count As %
Psychology 15 47%
Unspecified 5 16%
Neuroscience 2 6%
Computer Science 2 6%
Linguistics 1 3%
Other 2 6%
Unknown 5 16%