Cargando…

Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia

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-relationship...

Descripción completa

Detalles Bibliográficos
Autores principales: Badal, Varsha D., Depp, Colin A., Hitchcock, Peter F., Penn, David L., Harvey, Philip D., Pinkham, Amy E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8093458/
https://www.ncbi.nlm.nih.gov/pubmed/33996517
http://dx.doi.org/10.1016/j.scog.2021.100196
_version_ 1783687813579931648
author Badal, Varsha D.
Depp, Colin A.
Hitchcock, Peter F.
Penn, David L.
Harvey, Philip D.
Pinkham, Amy E.
author_facet Badal, Varsha D.
Depp, Colin A.
Hitchcock, Peter F.
Penn, David L.
Harvey, Philip D.
Pinkham, Amy E.
author_sort Badal, Varsha D.
collection PubMed
description 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).
format Online
Article
Text
id pubmed-8093458
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-80934582021-05-13 Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia Badal, Varsha D. Depp, Colin A. Hitchcock, Peter F. Penn, David L. Harvey, Philip D. Pinkham, Amy E. Schizophr Res Cogn Research Paper 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). Elsevier 2021-04-22 /pmc/articles/PMC8093458/ /pubmed/33996517 http://dx.doi.org/10.1016/j.scog.2021.100196 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Paper
Badal, Varsha D.
Depp, Colin A.
Hitchcock, Peter F.
Penn, David L.
Harvey, Philip D.
Pinkham, Amy E.
Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia
title Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia
title_full Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia
title_fullStr Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia
title_full_unstemmed Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia
title_short Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia
title_sort computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8093458/
https://www.ncbi.nlm.nih.gov/pubmed/33996517
http://dx.doi.org/10.1016/j.scog.2021.100196
work_keys_str_mv AT badalvarshad computationalmethodsforintegrativeevaluationofconfidenceaccuracyandreactiontimeinfacialaffectrecognitioninschizophrenia
AT deppcolina computationalmethodsforintegrativeevaluationofconfidenceaccuracyandreactiontimeinfacialaffectrecognitioninschizophrenia
AT hitchcockpeterf computationalmethodsforintegrativeevaluationofconfidenceaccuracyandreactiontimeinfacialaffectrecognitioninschizophrenia
AT penndavidl computationalmethodsforintegrativeevaluationofconfidenceaccuracyandreactiontimeinfacialaffectrecognitioninschizophrenia
AT harveyphilipd computationalmethodsforintegrativeevaluationofconfidenceaccuracyandreactiontimeinfacialaffectrecognitioninschizophrenia
AT pinkhamamye computationalmethodsforintegrativeevaluationofconfidenceaccuracyandreactiontimeinfacialaffectrecognitioninschizophrenia