Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia
Journal article
Badal, Varsha D., Depp, Colin A., Hitchcock, Peter F., Penn, David L., Harvey, Philip D. and Pinkham, Amy E.. (2021). Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia. Schizophrenia Research: Cognition. 25, p. Article 100196. https://doi.org/10.1016/j.scog.2021.100196
Authors | Badal, Varsha D., Depp, Colin A., Hitchcock, Peter F., Penn, David L., Harvey, Philip D. and Pinkham, Amy E. |
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Abstract | People with schizophrenia (SZ) process emotions less accurately than do healthy comparators (HC), and emotion recognition have expanded beyond accuracy to performance variables like reaction time (RT) and confidence. These domains are typically evaluated independently, but complex inter-relationships can be evaluated through machine learning at an item-by-item level. Using a mix of ranking and machine learning tools, we investigated item-by-item discrimination of facial affect with two emotion recognition tests (BLERT and ER-40) between SZ and HC. The best performing multi-domain model for ER40 had a large effect size in differentiating SZ and HC (d = 1.24) compared to a standard comparison of accuracy alone (d = 0.48); smaller increments in effect sizes were evident for the BLERT (d = 0.87 vs. d = 0.58). Almost half of the selected items were confidence ratings. Within SZ, machine learning models with ER40 (generally accuracy and reaction time) items predicted severity of depression and overconfidence in social cognitive ability, but not psychotic symptoms. Pending independent replication, the results support machine learning, and the inclusion of confidence ratings, in characterizing the social cognitive deficits in SZ. This moderate-sized study (n = 372) included subjects with schizophrenia (SZ, n = 218) and healthy controls (HC, n = 154). |
Keywords | machine learning; neural networks; social cognition; psychosis |
Year | 2021 |
Journal | Schizophrenia Research: Cognition |
Journal citation | 25, p. Article 100196 |
Publisher | Elsevier Inc. |
ISSN | 2215-0013 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.scog.2021.100196 |
PubMed ID | 33996517 |
Scopus EID | 2-s2.0-85104761705 |
PubMed Central ID | PMC8093458 |
Open access | Published as ‘gold’ (paid) open access |
Research or scholarly | Research |
Page range | 1-7 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 22 Apr 2021 |
Publication process dates | |
Accepted | 10 Mar 2021 |
Deposited | 10 Nov 2021 |
https://acuresearchbank.acu.edu.au/item/8x061/computational-methods-for-integrative-evaluation-of-confidence-accuracy-and-reaction-time-in-facial-affect-recognition-in-schizophrenia
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Publisher's version
OA_Badal_2021_Computational_methods_for_integrative_evaluation_of.pdf | |
License: CC BY-NC-ND 4.0 | |
File access level: Open |
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