Premium
An expert–novice comparison of feature choice
Author(s) -
Robson Samuel G.,
Searston Rachel A.,
Edmond Gary,
McCarthy Duncan J.,
Tangen Jason M.
Publication year - 2020
Publication title -
applied cognitive psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 100
eISSN - 1099-0720
pISSN - 0888-4080
DOI - 10.1002/acp.3676
Subject(s) - salient , clarity , psychology , feature (linguistics) , perception , cognitive psychology , set (abstract data type) , fingerprint (computing) , domain (mathematical analysis) , artificial intelligence , pattern recognition (psychology) , computer science , linguistics , mathematical analysis , biochemistry , chemistry , philosophy , mathematics , neuroscience , programming language
Summary Perceptual experts have learned to rapidly and accurately perceive the structural regularities that define categories and identities within a domain. They extract important features and their relations more efficiently than novices. We used fingerprint examination to investigate expert–novice differences in feature choice. On each fingerprint within our set, experts and novices selected one feature they thought was most useful for distinguishing a particular print and one feature they thought was least useful. We found that experts and novices often differed in the features they chose, and experts tended to agree more with each other. However, any such expert–novice difference appeared to depend on the image at hand typically emerging when salient or more conspicuous features of a fingerprint were unclear. We suggest that perceptual training ought to direct attention to useful features with the understanding that what is useful may change depending on the clarity of the stimuli.