2017年3月
Understanding the Neural Basis of Cognitive Bias Modification as a Clinical Treatment for Depression
JOURNAL OF CONSULTING AND CLINICAL PSYCHOLOGY
- ,
- ,
- ,
- ,
- ,
- 巻
- 85
- 号
- 3
- 開始ページ
- 200
- 終了ページ
- 217
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1037/ccp0000165
- 出版者・発行元
- AMER PSYCHOLOGICAL ASSOC
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.
- リンク情報
- ID情報
-
- DOI : 10.1037/ccp0000165
- ISSN : 0022-006X
- eISSN : 1939-2117
- ORCIDのPut Code : 29019552
- Web of Science ID : WOS:000395720100002