Dynamic computation of value signals via a common neural network in multi-attribute decision-making
- Author
- Amadeus Magrabi, Vera U. Ludwig, Christian M. Stoppel, Lena M. Paschke, David Wisniewski (UGent) , Hauke R. Heekeren and Henrik Walter
- Organization
- Abstract
- Studies in decision neuroscience have identified robust neural representations for the value of choice options. However, overall values often depend on multiple attributes, and it is not well understood how the brain evaluates different attributes and integrates them to combined values. In particular, it is not clear whether attribute values are computed in distinct attribute-specific regions or within the general valuation network known to process overall values. Here, we used a functional magnetic resonance imaging choice task in which abstract stimuli had to be evaluated based on variations of the attributes color and motion. The behavioral data showed that participants responded faster when overall values were high and attribute value differences were low. On the neural level, we did not find that attribute values were systematically represented in areas V4 and V5, even though these regions are associated with attribute-specific processing of color and motion, respectively. Instead, attribute values were associated with activity in the posterior cingulate cortex, ventral striatum and posterior inferior temporal gyrus. Furthermore, overall values were represented in dorsolateral and ventromedial prefrontal cortex, and attribute value differences in dorsomedial prefrontal cortex, which suggests that these regions play a key role for the neural integration of attribute values.
- Keywords
- Cognitive Neuroscience, Experimental and Cognitive Psychology, General Medicine, decision-making, value, attribute, salience, fMRI, STIMULUS VALUE SIGNALS, SUBJECTIVE VALUE, SELF-CONTROL, VALUATION, CORTEX, REPRESENTATIONS, NEUROBIOLOGY, SALIENCE, BRAIN, INTEGRATION
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8737411
- MLA
- Magrabi, Amadeus, et al. “Dynamic Computation of Value Signals via a Common Neural Network in Multi-Attribute Decision-Making.” SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE, vol. 17, no. 7, 2022, pp. 683–93, doi:10.1093/scan/nsab125.
- APA
- Magrabi, A., Ludwig, V. U., Stoppel, C. M., Paschke, L. M., Wisniewski, D., Heekeren, H. R., & Walter, H. (2022). Dynamic computation of value signals via a common neural network in multi-attribute decision-making. SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE, 17(7), 683–693. https://doi.org/10.1093/scan/nsab125
- Chicago author-date
- Magrabi, Amadeus, Vera U. Ludwig, Christian M. Stoppel, Lena M. Paschke, David Wisniewski, Hauke R. Heekeren, and Henrik Walter. 2022. “Dynamic Computation of Value Signals via a Common Neural Network in Multi-Attribute Decision-Making.” SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE 17 (7): 683–93. https://doi.org/10.1093/scan/nsab125.
- Chicago author-date (all authors)
- Magrabi, Amadeus, Vera U. Ludwig, Christian M. Stoppel, Lena M. Paschke, David Wisniewski, Hauke R. Heekeren, and Henrik Walter. 2022. “Dynamic Computation of Value Signals via a Common Neural Network in Multi-Attribute Decision-Making.” SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE 17 (7): 683–693. doi:10.1093/scan/nsab125.
- Vancouver
- 1.Magrabi A, Ludwig VU, Stoppel CM, Paschke LM, Wisniewski D, Heekeren HR, et al. Dynamic computation of value signals via a common neural network in multi-attribute decision-making. SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE. 2022;17(7):683–93.
- IEEE
- [1]A. Magrabi et al., “Dynamic computation of value signals via a common neural network in multi-attribute decision-making,” SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE, vol. 17, no. 7, pp. 683–693, 2022.
@article{8737411,
abstract = {{Studies in decision neuroscience have identified robust neural representations for the value of choice options. However, overall values often depend on multiple attributes, and it is not well understood how the brain evaluates different attributes and integrates them to combined values. In particular, it is not clear whether attribute values are computed in distinct attribute-specific regions or within the general valuation network known to process overall values. Here, we used a functional magnetic resonance imaging choice task in which abstract stimuli had to be evaluated based on variations of the attributes color and motion. The behavioral data showed that participants responded faster when overall values were high and attribute value differences were low. On the neural level, we did not find that attribute values were systematically represented in areas V4 and V5, even though these regions are associated with attribute-specific processing of color and motion, respectively. Instead, attribute values were associated with activity in the posterior cingulate cortex, ventral striatum and posterior inferior temporal gyrus. Furthermore, overall values were represented in dorsolateral and ventromedial prefrontal cortex, and attribute value differences in dorsomedial prefrontal cortex, which suggests that these regions play a key role for the neural integration of attribute values.}},
author = {{Magrabi, Amadeus and Ludwig, Vera U. and Stoppel, Christian M. and Paschke, Lena M. and Wisniewski, David and Heekeren, Hauke R. and Walter, Henrik}},
issn = {{1749-5016}},
journal = {{SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE}},
keywords = {{Cognitive Neuroscience,Experimental and Cognitive Psychology,General Medicine,decision-making,value,attribute,salience,fMRI,STIMULUS VALUE SIGNALS,SUBJECTIVE VALUE,SELF-CONTROL,VALUATION,CORTEX,REPRESENTATIONS,NEUROBIOLOGY,SALIENCE,BRAIN,INTEGRATION}},
language = {{eng}},
number = {{7}},
pages = {{683--693}},
title = {{Dynamic computation of value signals via a common neural network in multi-attribute decision-making}},
url = {{http://doi.org/10.1093/scan/nsab125}},
volume = {{17}},
year = {{2022}},
}
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