Aberrant reward learning, but not negative reinforcement learning, is related to depressive symptoms: an attentional perspective

被引:3
|
作者
Hertz-Palmor, Nimrod [1 ,2 ]
Rozenblit, Danielle [1 ]
Lavi, Shani [1 ]
Zeltser, Jonathan [1 ]
Kviatek, Yonatan [1 ]
Lazarov, Amit [1 ,3 ]
机构
[1] Tel Aviv Univ, Sch Psychol Sci, Tel Aviv, Israel
[2] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England
[3] Columbia Univ, Irving Med Ctr, Dept Psychiat, New York, NY 10027 USA
基金
以色列科学基金会;
关键词
anhedonia; attention allocation; depression symptoms; negative reinforcement; positive reinforcement; reward learning; selection history; SOCIAL ANXIETY DISORDER; VALUE-DRIVEN ATTENTION; EYE-TRACKING; BIAS MODIFICATION; INDIVIDUALS; ANHEDONIA; INFORMATION; RESPONSES; STIMULI; STRESS;
D O I
10.1017/S0033291723002519
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Background Aberrant reward functioning is implicated in depression. While attention precedes behavior and guides higher-order cognitive processes, reward learning from an attentional perspective - the effects of prior reward-learning on subsequent attention allocation - has been mainly overlooked.Methods The present study explored the effects of reward-based attentional learning in depression using two separate, yet complimentary, studies. In study 1, participants with high (HD) and low (LD) levels of depression symptoms were trained to divert their gaze toward one type of stimuli over another using a novel gaze-contingent music reward paradigm - music played when fixating the desired stimulus type and stopped when gazing the alternate one. Attention allocation was assessed before, during, and following training. In study 2, using negative reinforcement, the same attention allocation pattern was trained while substituting the appetitive music reward for gazing the desired stimulus type with the removal of an aversive sound (i.e. white noise).Results In study 1 both groups showed the intended shift in attention allocation during training (online reward learning), while generalization of learning at post-training was only evident among LD participants. Conversely, in study 2 both groups showed post-training generalization. Results were maintained when introducing anxiety as a covariate, and when using a more powerful sensitivity analysis. Finally, HD participants showed higher learning speed than LD participants during initial online learning, but only when using negative, not positive, reinforcement.Conclusions Deficient generalization of learning characterizes the attentional system of HD individuals, but only when using reward-based positive reinforcement, not negative reinforcement.
引用
收藏
页码:794 / 807
页数:14
相关论文
共 50 条
  • [41] Feasible Q-Learning for Average Reward Reinforcement Learning
    Jin, Ying
    Blanchet, Jose
    Gummadi, Ramki
    Zhou, Zhengyuan
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 238, 2024, 238
  • [42] Immunity for counterproductive attentional capture by reward signals among individuals with depressive symptoms
    Zhao, Xiaoning
    Hu, Jinsheng
    Liu, Meng
    Li, Qi
    Yang, Qingshuo
    BEHAVIOUR RESEARCH AND THERAPY, 2025, 184
  • [43] Participation in Learning and Depressive Symptoms
    Jenkins, Andrew
    EDUCATIONAL GERONTOLOGY, 2012, 38 (09) : 595 - 603
  • [44] Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
    Icarte R.T.
    Klassen T.Q.
    Valenzano R.
    McIlraith S.A.
    Journal of Artificial Intelligence Research, 2022, 73 : 173 - 208
  • [45] Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
    Icarte, Rodrigo Toro
    Klassen, Toryn Q.
    Valenzano, Richard
    Mcllraith, Sheila A.
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2022, 73 : 173 - 208
  • [46] Multi-Agent Deep Reinforcement Learning With Progressive Negative Reward for Cryptocurrency Trading
    Kumlungmak, Kittiwin
    Vateekul, Peerapon
    IEEE ACCESS, 2023, 11 : 66440 - 66455
  • [47] Reinforcement learning and the reward positivity with aversive outcomes
    Bauer, Elizabeth A.
    Watanabe, Brandon K.
    Macnamara, Annmarie
    PSYCHOPHYSIOLOGY, 2024, 61 (04)
  • [48] Reward Certification for Policy Smoothed Reinforcement Learning
    Mu, Ronghui
    Marcolino, Leandro Soriano
    Zhang, Yanghao
    Zhang, Tianle
    Huang, Xiaowei
    Ruan, Wenjie
    THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 19, 2024, : 21429 - 21437
  • [49] Reinforcement Learning in Reward-Mixing MDPs
    Kwon, Jeongyeol
    Efroni, Yonathan
    Caramanis, Constantine
    Mannor, Shie
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
  • [50] Explicable Reward Design for Reinforcement Learning Agents
    Devidze, Rati
    Radanovic, Goran
    Kamalaruban, Parameswaran
    Singla, Adish
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34