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Xu S, Qi S, Duan H, Zhang J, Akioma M, Gao F, Wu AMS, Yuan Z. Task Difficulty Regulates How Conscious and Unconscious Monetary Rewards Boost the Performance of Working Memory: An Event-Related Potential Study. Front Syst Neurosci 2022; 15:716961. [PMID: 35111000 PMCID: PMC8802761 DOI: 10.3389/fnsys.2021.716961] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 10/29/2021] [Indexed: 11/21/2022] Open
Abstract
The performance of working memory can be improved by the corresponding high-value vs. low-value rewards consciously or unconsciously. However, whether conscious and unconscious monetary rewards boosting the performance of working memory is regulated by the difficulty level of working memory task is unknown. In this study, a novel paradigm that consists of a reward-priming procedure and N-back task with differing levels of difficulty was designed to inspect this complex process. In particular, both high-value and low-value coins were presented consciously or unconsciously as the reward cues, followed by the N-back task, during which electroencephalogram signals were recorded. It was discovered that the high-value reward elicited larger event-related potential (ERP) component P3 along the parietal area (reflecting the working memory load) as compared to the low-value reward for the less difficult 1-back task, no matter whether the reward was unconsciously or consciously presented. In contrast, this is not the case for the more difficult 2-back task, in which the difference in P3 amplitude between the high-value and low-value rewards was not significant for the unconscious reward case, yet manifested significance for the conscious reward processing. Interestingly, the results of the behavioral analysis also exhibited very similar patterns as ERP patterns. Therefore, this study demonstrated that the difficulty level of a task can modulate the influence of unconscious reward on the performance of working memory.
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Affiliation(s)
- Shiyang Xu
- Faculty of Health Sciences, University of Macau, Taipa, Macao SAR, China
- Centre for Cognitive and Brain Science, University of Macau, Shanghai, Macao SAR, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Taipa, Macao SAR, China
| | - Senqing Qi
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, China
| | - Haijun Duan
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, China
| | - Juan Zhang
- Centre for Cognitive and Brain Science, University of Macau, Shanghai, Macao SAR, China
- Faculty of Education, University of Macau, Taipa, Macau SAR, China
| | - Miriam Akioma
- Faculty of Health Sciences, University of Macau, Taipa, Macao SAR, China
- Centre for Cognitive and Brain Science, University of Macau, Shanghai, Macao SAR, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Taipa, Macao SAR, China
| | - Fei Gao
- Centre for Cognitive and Brain Science, University of Macau, Shanghai, Macao SAR, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Taipa, Macao SAR, China
- Faculty of Arts and Humanities, University of Macau, Taipa, Macao SAR, China
| | - Anise M. S. Wu
- Centre for Cognitive and Brain Science, University of Macau, Shanghai, Macao SAR, China
- Faculty of Social Sciences, University of Macau, Taipa, Macau SAR, China
| | - Zhen Yuan
- Faculty of Health Sciences, University of Macau, Taipa, Macao SAR, China
- Centre for Cognitive and Brain Science, University of Macau, Shanghai, Macao SAR, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Taipa, Macao SAR, China
- *Correspondence: Zhen Yuan,
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Wang G, Li J, Li S, Zhu C. Neural Dynamics of the Combined Discounting of Delay and Probability During the Evaluation of a Delayed Risky Reward. Front Psychol 2020; 11:576460. [PMID: 33132984 PMCID: PMC7550637 DOI: 10.3389/fpsyg.2020.576460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 09/07/2020] [Indexed: 11/13/2022] Open
Abstract
Delay discounting and probability discounting are two important processes, but in daily life there are many more situations that involve delayed risky outcomes. Although neuroscience research has extensively investigated delay and probability discounting in isolation, little research has explored the neural correlates of the combined discounting of delay and probability. Using the event-related brain potentials (ERPs) technique, we designed a novel paradigm to investigate neural processes related to the combined discounting of delay and probability during the evaluation of a delayed risky reward. ERP results suggested distinct temporal dynamics for delay and probability processing during combined discounting. Both the early frontal P200 and the N2 reflected only probability, not delay, while the parietal P300 was sensitive to both probability and delay. Furthermore, the late positive potential (LPP) was sensitive to probability, but insensitive to delay. These results suggest that probability has a prolonged modulatory effect on reward evaluation in the information processing stream. These findings contribute to an understanding of the neural processes underlying the combined discounting of delay and probability. The limitation of this study is to only consider four delay and probability combinations. Future studies can explore the combined discounting of more probability and delay combinations to further test the robustness of the conclusion.
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Affiliation(s)
- Guangrong Wang
- Neural Decision Science Laboratory, School of Economics and Management, Weifang University, Weifang, China.,School of Economics, Institute for Study of Brain-Like Economics, Shandong University, Jinan, China
| | - Jianbiao Li
- School of Economics, Institute for Study of Brain-Like Economics, Shandong University, Jinan, China.,Department of Economic and Management, Nankai University Binhai College, Tianjin, China
| | - Shuaiqi Li
- School of Economics, Institute for Study of Brain-Like Economics, Shandong University, Jinan, China
| | - Chengkang Zhu
- School of Economics, Institute for Study of Brain-Like Economics, Shandong University, Jinan, China
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The electrophysiology correlation of the cognitive bias in anxiety under uncertainty. Sci Rep 2020; 10:11354. [PMID: 32647252 PMCID: PMC7347926 DOI: 10.1038/s41598-020-68427-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 06/24/2020] [Indexed: 11/17/2022] Open
Abstract
The intolerance of uncertainty (IU) model holds that excessive emotional response under uncertain conditions is conducive to the maintenance of anxiety, and individuals with a high anxiety level may exhibit a negative bias and experience anxiety when processing uncertain information. However, the dynamic electrophysiological correlation of this negative bias is not clear. Therefore, we used an adapted study–test paradigm to explore the changes in the electroencephalography (EEG) of subjects when processing uncertain cues and certain cues (certain neutral and certain threatening) and correlated the differences with anxiety level. The behavioral results showed that there was a significant positive correlation between the trait anxiety score and β value under the threatening condition, which indicated that individuals with high trait anxiety take a more conservative approach in the face of negative stimuli. The results of EEG showed that during the test stage, the components N1 and P2, which are related to early perception, had significant conditional main effects. Meanwhile, under uncertain conditions, the N1 peak was positively correlated with the state anxiety score. In the study stage, we found that the N400 component was significantly larger in the early study stage than in the late study stage under uncertain conditions. In sum, individuals with high anxiety levels had a negative bias in the early cue processing of the test stage, and anxiety did not affect the study stage.
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Wang G, Li J, Wang P, Zhu C, Pan J, Li S. Neural Dynamics of Processing Probability Weight and Monetary Magnitude in the Evaluation of a Risky Reward. Front Psychol 2019; 10:554. [PMID: 30984057 PMCID: PMC6448026 DOI: 10.3389/fpsyg.2019.00554] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 02/27/2019] [Indexed: 11/30/2022] Open
Abstract
Risky decision-making involves risky reward valuation, choice, and feedback processes. However, the temporal dynamics of risky reward processing are not well understood. Using event-related brain potential, we investigated the neural correlates of probability weight and money magnitude in the evaluation of a risky reward. In this study, each risky choice consisted of two risky options, which were presented serially to separate decision-making and option evaluation processes. The early P200 component reflected the process of probability weight, not money magnitude. The medial frontal negativity (MFN) reflected both probability weight and money magnitude processes. The late positive potential (LPP) only reflected the process of probability weight. These results demonstrate distinct temporal dynamics for probability weight and money magnitude processes when evaluating a risky outcome, providing a better understanding of the possible mechanism underlying risky reward processing.
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Affiliation(s)
- Guangrong Wang
- Neural Decision Science Laboratory, Weifang University, Weifang, China.,Reinhard Selten Laboratory, China Academy of Corporate Governance, Business School, Nankai University, Tianjin, China
| | - Jianbiao Li
- Reinhard Selten Laboratory, China Academy of Corporate Governance, Business School, Nankai University, Tianjin, China.,Department of Economic and Management, Nankai University Binhai College, Tianjin, China.,School of Economics, Shandong University, Jinan, China
| | - Pengcheng Wang
- Reinhard Selten Laboratory, China Academy of Corporate Governance, Business School, Nankai University, Tianjin, China.,Business School, Tianjin University of Economic and Finance, Tianjin, China
| | - Chengkang Zhu
- Reinhard Selten Laboratory, China Academy of Corporate Governance, Business School, Nankai University, Tianjin, China
| | - Jingjing Pan
- Reinhard Selten Laboratory, China Academy of Corporate Governance, Business School, Nankai University, Tianjin, China
| | - Shuaiqi Li
- Reinhard Selten Laboratory, China Academy of Corporate Governance, Business School, Nankai University, Tianjin, China
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