Select Publications
Preprints
, 2025, Super-recognisers sample visual information of superior computational value for facial recognition, http://dx.doi.org/10.31219/osf.io/3h5uj_v3
, 2025, Using face averages to measure differential accuracy for demographic groups in facial recognition, http://dx.doi.org/10.31234/osf.io/h82dz_v1
, 2025, Super-recognisers sample visual information of superior computational value for facial recognition, http://dx.doi.org/10.31219/osf.io/3h5uj_v2
, 2025, Super-Recognisers can Detect AI-hyperrealism, http://dx.doi.org/10.31234/osf.io/fwjsb_v2
, 2025, The State of Modeling Face Processing in Humans with Deep Learning, http://dx.doi.org/10.31234/osf.io/hfjmq_v2
, 2024, What is special about super-recognisers?, http://dx.doi.org/10.31234/osf.io/sd95m
, 2024, AI-generated face detection: Why super-recognisers succeed where others fail, http://dx.doi.org/10.31234/osf.io/fwjsb
, 2024, Flexible use of facial features supports face identity processing, http://dx.doi.org/10.31234/osf.io/c7yfu
, 2024, Normative face recognition ability test scores vary across online participant pools, http://dx.doi.org/10.31234/osf.io/52k7w
, 2024, Super-Recognisers can Detect AI-hyperrealism, http://dx.doi.org/10.31234/osf.io/fwjsb_v1
, 2024, The State of Modeling Face Processing in Humans with Deep Learning, http://dx.doi.org/10.31234/osf.io/hfjmq
, 2024, The State of Modeling Face Processing in Humans with Deep Learning, http://dx.doi.org/10.31234/osf.io/hfjmq_v1
, 2023, Superior computational value of face information sampled by super-recognizers, http://dx.doi.org/10.31219/osf.io/3h5uj
, 2023, Superior computational value of face information sampled by super-recognizers, http://dx.doi.org/10.31219/osf.io/3h5uj_v1
, 2022, Looking at faces in the wild, http://dx.doi.org/10.31234/osf.io/v7df6
, 2021, Diverse routes to expertise in facial recognition, http://dx.doi.org/10.31234/osf.io/fmznh
, 2021, Face information sampling in super-recognizers, http://dx.doi.org/10.31234/osf.io/z2k4a
, 2021, GFMT2: A psychometric measure of face matching ability, http://dx.doi.org/10.31234/osf.io/a3fh4
, 2021, Masked face identification is improved by diagnostic feature training, http://dx.doi.org/10.31234/osf.io/e9fq3
, 2021, Partitioning natural face image variability emphasises within-identity over between-identity representation for understanding accurate recognition, http://dx.doi.org/10.31234/osf.io/w58k3
, 2020, Performance of Typical and Superior Face Recognisers on a Novel Interactive Face Matching Procedure, http://dx.doi.org/10.31234/osf.io/kbm2g
, 2020, Can face identification ability be trained? Evidence for two routes to expertise, http://dx.doi.org/10.31234/osf.io/g7qfd
, 2020, Understanding professional expertise in unfamiliar face matching, http://dx.doi.org/10.31234/osf.io/z2ugp
, 2020, UNSW Face Test: A screening tool for super-recognizers, http://dx.doi.org/10.31234/osf.io/k7mf6