Anna J Watters1, Leanne M Williams. 1. Sydney Medical School, Westmead Millennium Institute, The Brain Dynamics Center, Westmead Hospital, Westmead, Sydney, NSW, Australia. anna.watters@sydney.edu.au
Abstract
BACKGROUND: Negativity biases and their impact on reactivity to negative emotion are implicated in the mechanisms of risk for depression. The aim of this study was to determine whether self-reported negativity bias is related to objective cognitive measures of emotional reactivity. METHODS: A previously established Web self-report measure of negativity bias was used to assess 1,080 volunteers from the Brain Resource International Database (overseen by the nonprofit BRAINnet Foundation). We identified matched subgroups of "High Risk" (n = 216) and "Low Risk" (n = 216) participants using a psychometric high-risk method, which classified High Risk as the sample's top 30% of negativity bias scores and Low Risk as the bottom 30%. These subsamples also completed the WebNeuro cognitive tasks for assessing both conscious and nonconscious reactions to facial emotions. Task performance was quantified by accuracy, reaction time, and misidentification errors. RESULTS: The High Risk (high negativity bias) subgroup was distinguished by greater reactivity to negative emotion in both conscious and nonconscious processing. The High Risk profile was reflected in higher accuracy for sadness (nonconsciously) and disgust (consciously), and more frequent misidentification of neutral as anger (consciously). CONCLUSIONS: These results are consistent with seminal theories that a systematic cognitive negativity bias produces a hyper-reactivity to negative emotion, which can impact nonconscious as well as conscious processing. The results provide a step toward objective markers of risk for depression that would help the community act regarding preventative programs. Replication in patient samples is warranted.
BACKGROUND: Negativity biases and their impact on reactivity to negative emotion are implicated in the mechanisms of risk for depression. The aim of this study was to determine whether self-reported negativity bias is related to objective cognitive measures of emotional reactivity. METHODS: A previously established Web self-report measure of negativity bias was used to assess 1,080 volunteers from the Brain Resource International Database (overseen by the nonprofit BRAINnet Foundation). We identified matched subgroups of "High Risk" (n = 216) and "Low Risk" (n = 216) participants using a psychometric high-risk method, which classified High Risk as the sample's top 30% of negativity bias scores and Low Risk as the bottom 30%. These subsamples also completed the WebNeuro cognitive tasks for assessing both conscious and nonconscious reactions to facial emotions. Task performance was quantified by accuracy, reaction time, and misidentification errors. RESULTS: The High Risk (high negativity bias) subgroup was distinguished by greater reactivity to negative emotion in both conscious and nonconscious processing. The High Risk profile was reflected in higher accuracy for sadness (nonconsciously) and disgust (consciously), and more frequent misidentification of neutral as anger (consciously). CONCLUSIONS: These results are consistent with seminal theories that a systematic cognitive negativity bias produces a hyper-reactivity to negative emotion, which can impact nonconscious as well as conscious processing. The results provide a step toward objective markers of risk for depression that would help the community act regarding preventative programs. Replication in patient samples is warranted.
Authors: Elizabeth P Hayden; Thomas M Olino; Sara J Bufferd; Anna Miller; Lea R Dougherty; Haroon I Sheikh; Shiva M Singh; Daniel N Klein Journal: Dev Psychopathol Date: 2013-08
Authors: Heather A Berlin; Emily R Stern; Johnny Ng; Sam Zhang; David Rosenthal; Rachel Turetzky; Cheuk Tang; Wayne Goodman Journal: Psychiatry Res Neuroimaging Date: 2017-02-09 Impact factor: 2.376
Authors: Leanne M Williams; Adam Pines; Andrea N Goldstein-Piekarski; Lisa G Rosas; Monica Kullar; Matthew D Sacchet; Olivier Gevaert; Jeremy Bailenson; Philip W Lavori; Paul Dagum; Brian Wandell; Carlos Correa; Walter Greenleaf; Trisha Suppes; L Michael Perry; Joshua M Smyth; Megan A Lewis; Elizabeth M Venditti; Mark Snowden; Janine M Simmons; Jun Ma Journal: Behav Res Ther Date: 2017-10-07