"Understanding the neural basis of cognitive bias modification as a clinical treatment for depression": Correction to Eguchi et al. (2016)

Abstract

Reports an error in "Understanding the neural basis of cognitive bias modification as a clinical treatment for depression" by Akihiro Eguchi, Daniel Walters, Nele Peerenboom, Hannah Dury, Elaine Fox and Simon Stringer (Journal of Consulting and Clinical Psychology, Advanced Online Publication, Dec 19, 2016, np). In the article, there was an error in the Discussion section's first paragraph for Implications and Future Work. The in-text reference citation for Penton-Voak et al. (2013) was incorrectly listed as "Blumenfeld, Preminger, Sagi, and Tsodyks (2006)". All versions of this article have been corrected. (The following abstract of the original article appeared in record 2016-60713-001.) Objective: Cognitive bias modification (CBM) eliminates cognitive biases toward negative information and is efficacious in reducing depression recurrence, but the mechanisms behind the bias elimination are not fully understood. The present study investigated, through computer simulation of neural network models, the neural dynamics underlying the use of CBM in eliminating the negative biases in the way that depressed patients evaluate facial expressions.

Method: We investigated 2 new CBM methodologies using biologically plausible synaptic learning mechanisms-continuous transformation learning and trace learning-which guide learning by exploiting either the spatial or temporal continuity between visual stimuli presented during training. We first describe simulations with a simplified 1-layer neural network, and then we describe simulations in a biologically detailed multilayer neural network model of the ventral visual pathway.

Results: After training with either the continuous transformation learning rule or the trace learning rule, the 1-layer neural network eliminated biases in interpreting neutral stimuli as sad. The multilayer neural network trained with realistic face stimuli was also shown to be able to use continuous transformation learning or trace learning to reduce biases in the interpretation of neutral stimuli.

Conclusions: The simulation results suggest 2 biologically plausible synaptic learning mechanisms, continuous transformation learning and trace learning, that may subserve CBM. The results are highly informative for the development of experimental protocols to produce optimal CBM training methodologies with human participants. (PsycINFO Database Record