Abstract: Researchers developed methods to predict emotions in spontaneous thoughts using fMRI and machine learning. They created personalized narratives that participants read while monitoring their brain activity, with the goal of decoding the emotional dimensions of the thoughts.
By analyzing fMRI data, the team identified key brain regions involved in processing personal relevance and emotional balance. Their breakthrough offers insight into the emotional patterns of daydreaming and could revolutionize mental health diagnosis.
Important facts:
- The study used fMRI and machine learning to predict subjective feelings during story reading and spontaneous thinking.
- Key brain regions such as the anterior insula and midcingulate cortex were important in predicting personal relevance and emotional tone.
- The research suggests potential applications in understanding individual emotional experiences and improving mental health assessments.
Source: Institute for Basic Science
A team of researchers led by KIM Hong Ji and WOO Choong-Wan at the Center for Neuroscience Imaging Research (CNIR) within the Institute for Basic Science (IBS), in collaboration with Emily FINN at Dartmouth College, have explored a new realm of understanding. has been opened. Inside the human mind
The team demonstrated the possibility of using functional magnetic resonance imaging (fMRI) and machine learning algorithms to predict subjective feelings in people’s thoughts while reading stories or in a free-thinking state.
The mind is constantly active, and spontaneous thoughts occur even during rest or sleep. These thoughts can be anything from memories of the past to wishes for the future, and are often connected to emotions and personal concerns.
However, since spontaneous thoughts usually occur without the interruption of consciousness, researching them poses challenges – even simply asking individuals what they are thinking at the moment, their thoughts. can change the nature of
New research shows that by combining personal narratives with fMRI it is possible to develop predictive models of affective content during spontaneous thinking. Narrative and spontaneous thoughts share similar characteristics, including rich semantic information and a temporally emergent nature.
To capture a diverse range of thought patterns, participants engaged in one-on-one interviews to develop personalized narrative prompts reflecting on their past experiences and emotions. While the participants read their stories inside the MRI scanner, their brain activity was recorded.
After the fMRI scan, participants were asked to reread the stories and rate each moment’s self-relevance (i.e., how relevant the content is to oneself) and valence (i.e., how positive or negative the content is). Report the
Using a quintile (five levels) from each participant’s self-congruence and valence ratings, 25 (5 levels of self-relevance ratings × 5 levels of valence ratings) were generated from the fMRI and potential segments of the rating data.
The team then used machine learning techniques to train predictive models, combining that data with fMRI brain scans of 49 people to decode the “emotional dimensions” of thoughts in real time.
To interpret the brain representations of the predictive models, the research team used multiple methods, such as virtual lesion and virtual isolation analysis at both the region and network levels.
Through these analyses, they explored the importance of default mode, ventral attention, and frontoparietal networks in both self-congruence and valence predictions.
Specifically, they identified the involvement of the anterior insula and midcingulate cortex in self-relevant prediction, while the left temporoparietal junction and dorsomedial prefrontal cortex played a significant role in valence prediction.
Furthermore, the predictive models were applied to data from 199 individuals not only during story reading but also during spontaneous, task-free thinking or at rest to predict both self-congruence and coherence. Show ability. These results show the promise of daydream decoding.
“Many tech companies and research teams are currently trying to decode words or images directly from brain activity, but limited efforts are being made to decode the intimate emotions within those thoughts,” Dr. said WOO Chong Wan, led by IBS Associate Director. the study.
“Our research focuses on human emotion, with the goal of decoding emotions within the natural flow of thoughts to extract information that can benefit people’s mental health.”
“This study is important because we decoded emotional states associated with general thoughts, rather than targeting emotions limited to specific tasks,” emphasized KIM Hongji, doctoral candidate and first author of the study.
“These findings advance our understanding of the internal states and contexts that influence subjective experiences, potentially shed light on individual differences in thoughts and emotions, and aid in the assessment of mental well-being. “
About this AI and emotion research news
the author: William Sue
Source: Institute for Basic Science
contact: William Suh – Institute for Basic Science
Image: This image is credited to Neuroscience News.
Original research: closed access
“Brain Regulation of Spontaneous Thought: Predictive Modeling of Self-Congruency and Balance Using Personal Narratives” KIM Hong Ji et al. PNAS
Abstract
Brain decoding of spontaneous thought: Predictive modeling of self-congruence and balance using personal narratives.
The content and dynamics of spontaneous thought are important factors in personality traits and mental health. However, spontaneous thoughts are difficult to assess because of their unconstrained nature, and directing participants’ attention to report their thoughts can fundamentally change them.
Here, we aimed to decode two important content dimensions of spontaneous thought—self-congruence and balance—directly from brain activity.
To train predictive models based on functional MRI, we used individually generated personal stories as stimuli in a story-reading task to simulate spontaneous thoughts such as narrative.n = 49). We then tested these models on several test datasets (total n = 199).
Default mood, ventral attention, and frontoparietal networks played key roles in predictions, with the anterior insula and midcingulate cortex contributing to self-relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex to valence prediction. Participated.
Overall, the study provides models of the brain’s internal thoughts and emotions, highlighting the brain’s ability to decode spontaneous thought.