Physical mechanism of mind changes and tradeoffs among speed, accuracy, and energy cost in brain decision making:Landscape, flux, and path perspectives

• 作者: 张坤 汪劲 闫晗
• 来源期刊: 中国物理B（英文版）
• 关键词: decision making non-equilibrium landscape and flux speed-accuracy tradeoff energy cost
• 年，卷（期）: 2016,（7）
• 页码范围: 078702-1-078702-20
• 页数: 1页
• 语种: 英文
• DOI: 10.1088/1674-1056/25/7/078702

摘要

Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and mem-ory, have been successfully described by attractor dynamics. For decision making in the brain, a quantitative description of global attractor landscapes has not yet been completely given. Here, we developed a theoretical framework to quantify the landscape associated with the steady state probability distributions and associated steady state curl flux, measuring the degree of non-equilibrium through the degree of detailed balance breaking for decision making. We quantified the decision-making processes with optimal paths from the undecided attractor states to the decided attractor states, which are identified as basins of attractions, on the landscape. Both landscape and flux determine the kinetic paths and speed. The kinetics and global stability of decision making are explored by quantifying the landscape topography through the barrier heights and the mean first passage time. Our theoretical predictions are in agreement with experimental observations:more errors occur under time pressure. We quantitatively explored two mechanisms of the speed-accuracy tradeoff with speed emphasis and further uncovered the tradeoffs among speed, accuracy, and energy cost. Our results imply that there is an optimal balance among speed, accuracy, and the energy cost in decision making. We uncovered the possible mechanisms of changes of mind and how mind changes improve performance in decision processes. Our landscape approach can help facilitate an understanding of the underlying physical mechanisms of cognitive processes and identify the key factors in the corresponding neural networks.