| Title |
Electrophysiological correlates reflect the integration of model-based and model-free decision information
|
|---|---|
| Published in |
Cognitive, Affective, & Behavioral Neuroscience, January 2017
|
| DOI | 10.3758/s13415-016-0487-3 |
| Pubmed ID | |
| Authors |
Ben Eppinger, Maik Walter, Shu-Chen Li |
| Abstract |
In this study, we investigated the interplay of habitual (model-free) and goal-directed (model-based) decision processes by using a two-stage Markov decision task in combination with event-related potentials (ERPs) and computational modeling. To manipulate the demands on model-based decision making, we applied two experimental conditions with different probabilities of transitioning from the first to the second stage of the task. As we expected, when the stage transitions were more predictable, participants showed greater model-based (planning) behavior. Consistent with this result, we found that stimulus-evoked parietal (P300) activity at the second stage of the task increased with the predictability of the state transitions. However, the parietal activity also reflected model-free information about the expected values of the stimuli, indicating that at this stage of the task both types of information are integrated to guide decision making. Outcome-related ERP components only reflected reward-related processes: Specifically, a medial prefrontal ERP component (the feedback-related negativity) was sensitive to negative outcomes, whereas a component that is elicited by reward (the feedback-related positivity) increased as a function of positive prediction errors. Taken together, our data indicate that stimulus-locked parietal activity reflects the integration of model-based and model-free information during decision making, whereas feedback-related medial prefrontal signals primarily reflect reward-related decision processes. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 1% |
| Unknown | 80 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 25% |
| Researcher | 12 | 15% |
| Student > Doctoral Student | 8 | 10% |
| Student > Master | 8 | 10% |
| Student > Bachelor | 5 | 6% |
| Other | 9 | 11% |
| Unknown | 19 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 26 | 32% |
| Neuroscience | 13 | 16% |
| Medicine and Dentistry | 5 | 6% |
| Agricultural and Biological Sciences | 4 | 5% |
| Engineering | 2 | 2% |
| Other | 6 | 7% |
| Unknown | 25 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 January 2017.
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#22,758,309
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Outputs from Cognitive, Affective, & Behavioral Neuroscience
#1,002
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Outputs of similar age from Cognitive, Affective, & Behavioral Neuroscience
#14
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