Insights from computational models of face recognition: A reply to Blauch, Behrmann and Plaut

TY - JOUR

T1 - Insights from computational models of face recognition

T2 - A reply to Blauch, Behrmann and Plaut

AU - Young, Andrew W.

AU - Burton, A. Mike

PY - 2021/2/1

Y1 - 2021/2/1

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.

KW - Computational models

KW - Expertise

KW - Face recognition

KW - Familiarity

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 - Article

AN - SCOPUS:85089357089

SN - 0010-0277

JO - Cognition

JF - Cognition

M1 - 104422

ER -