TY - JOUR
T1 - Insights from computational models of face recognition: A reply to Blauch, Behrmann and Plaut
AU - Young, Andrew W.
AU - Burton, A. Mike
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - We agree with Blauch, Behrmann, and Plaut (2020) on a number of points, and are reassured that their data bear out our previous findings. We discuss differences in modelling style, and the usefulness of different types of model for supporting psychological understanding. We emphasise the role that within-person variability plays in recognising familiar faces and clarify the range over which it is idiosyncratic. The combination of image analysis with top-down support to cohere different images of the same person seems to be an important characteristic of successful models.
AB - We agree with Blauch, Behrmann, and Plaut (2020) on a number of points, and are reassured that their data bear out our previous findings. We discuss differences in modelling style, and the usefulness of different types of model for supporting psychological understanding. We emphasise the role that within-person variability plays in recognising familiar faces and clarify the range over which it is idiosyncratic. The combination of image analysis with top-down support to cohere different images of the same person seems to be an important characteristic of successful models.
UR - http://www.scopus.com/inward/record.url?scp=85089357089&partnerID=8YFLogxK
U2 - 10.1016/j.cognition.2020.104422
DO - 10.1016/j.cognition.2020.104422
M3 - Comment/debate/opinion
C2 - 32800311
AN - SCOPUS:85089357089
SN - 0010-0277
VL - 208
JO - Cognition
JF - Cognition
M1 - 104422
ER -