Javascript must be enabled to continue!
Decoding non-conscious thought representations during successful thought suppression
View through CrossRef
Abstract
Controlling our own thoughts is central to mental wellbeing and its failure is at the crux of a number of mental disorders. Paradoxically, behavioural evidence shows that thought-suppression often fails. Despite the broad importance of understanding the mechanisms of thought control, little is known about the fate of neural representations of suppressed thoughts. Using functional MRI, we investigated the brain areas involved in controlling visual thoughts and tracked suppressed thought representations using multi-voxel pattern analysis (MVPA). Participants were asked to either visualize a vegetable/fruit or suppress any visual thoughts about those objects. Surprisingly, the content (object identity) of successfully suppressed thoughts was still decodable in visual and executive areas with algorithms trained on perception or imagery. This suggests that pictorial representations of the suppressed thoughts are still present despite individuals reporting they are not. Thought generation was associated with the left hemisphere, whereas thought suppression with right hemisphere engagement. Further, GLM analyses showed that subjective success in thought suppression was correlated with engagement of executive areas, while thought-suppression failure was associated with engagement of visual and memory related areas. These results reveal that the content of suppressed thoughts exist hidden from awareness, seemingly without an individual’s knowledge, providing a compelling reason why thought suppression is so ineffective. These data inform models of unconscious thought production and could be used to develop new treatment approaches to disorders involving maladaptive thoughts.
Title: Decoding non-conscious thought representations during successful thought suppression
Description:
Abstract
Controlling our own thoughts is central to mental wellbeing and its failure is at the crux of a number of mental disorders.
Paradoxically, behavioural evidence shows that thought-suppression often fails.
Despite the broad importance of understanding the mechanisms of thought control, little is known about the fate of neural representations of suppressed thoughts.
Using functional MRI, we investigated the brain areas involved in controlling visual thoughts and tracked suppressed thought representations using multi-voxel pattern analysis (MVPA).
Participants were asked to either visualize a vegetable/fruit or suppress any visual thoughts about those objects.
Surprisingly, the content (object identity) of successfully suppressed thoughts was still decodable in visual and executive areas with algorithms trained on perception or imagery.
This suggests that pictorial representations of the suppressed thoughts are still present despite individuals reporting they are not.
Thought generation was associated with the left hemisphere, whereas thought suppression with right hemisphere engagement.
Further, GLM analyses showed that subjective success in thought suppression was correlated with engagement of executive areas, while thought-suppression failure was associated with engagement of visual and memory related areas.
These results reveal that the content of suppressed thoughts exist hidden from awareness, seemingly without an individual’s knowledge, providing a compelling reason why thought suppression is so ineffective.
These data inform models of unconscious thought production and could be used to develop new treatment approaches to disorders involving maladaptive thoughts.
Related Results
Improving Decodability of Polar Codes by Adding Noise
Improving Decodability of Polar Codes by Adding Noise
This paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro...
Best practices and pitfalls in multivariate pattern analysis of event-related potentials: A systematic review of preprocessing and analytical configurations
Best practices and pitfalls in multivariate pattern analysis of event-related potentials: A systematic review of preprocessing and analytical configurations
Multivariate pattern analysis (MVPA, decoding) has been increasingly used in event-related potential (ERP) research. This growing use reflects several advantages of decoding approa...
Optimized Generalized LDPC Convolutional Codes
Optimized Generalized LDPC Convolutional Codes
In this paper, some optimized encoding and decoding schemes are proposed for the generalized LDPC convolutional codes (GLDPC–CCs). In terms of the encoding scheme, a flexible dopin...
Dissociable dynamic effects of expectation during statistical learning
Dissociable dynamic effects of expectation during statistical learning
Abstract
The brain is thought to generate internal predictions, based on previous statistical regularities in the environment, to optimise behaviour. Predictive pro...
Dissociable dynamic effects of expectation during statistical learning.
Dissociable dynamic effects of expectation during statistical learning.
Abstract
The brain is thought to generate internal predictions, based on previous statistical regularities in the environment, to optimise behaviour. Predictive pro...
Meta-Representations as Representations of Processes
Meta-Representations as Representations of Processes
In this study, we explore how the notion of meta-representations in Higher-Order Theories (HOT) of consciousness can be implemented in computational models. HOT suggests that consc...
PARENTAL KNOWLEDGE AND PERCEPTION TOWARDS THE USE OF CONSCIOUS SEDATION
PARENTAL KNOWLEDGE AND PERCEPTION TOWARDS THE USE OF CONSCIOUS SEDATION
Background: Behavioural management alleviates dental anxiety and instils a positive dental attitude. Conscious Sedation is a pharmacological behavioural management where medication...
Robust Neural Decoding with low-density EEG
Robust Neural Decoding with low-density EEG
Abstract
High-density Electroencephalography (EEG) recording enhances spatial resolution for neural signal decoding, yet the relationship between electrode density ...

