Grossberg Stephen, Pilly Praveen K
Department of Cognitive and Neural Systems, Center for Adaptive Systems, Center of Excellence for Learning in Education, Science, and Technology, Boston University, 677 Beacon Street, Boston, MA 02215, USA.
Vision Res. 2008 Jun;48(12):1345-73. doi: 10.1016/j.visres.2008.02.019. Epub 2008 May 2.
How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons". A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probabilistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.
大脑是如何做出决策的？感知决策的速度和准确性与输入的确定性相关，并与顶叶和额叶皮质“决策神经元”中的证据积累速率相关。一个由基底神经节控制的、关于视网膜/外侧膝状体与皮质区域V1、MT、MST和LIP内部及之间相互作用的生物物理现实模型，模拟了对纽瑟姆、沙德伦及其同事在神经生理学实验中使用的模糊视觉运动刺激做出决策的动态特性。该模型阐明了解决孔径问题的脑回路如何与具有自归一化选择特性的循环竞争网络相互作用，以实时进行概率决策。一些科学家声称，感知和决策可以用贝叶斯推理或相关的一般统计思想来描述，这些思想在先验和似然性的基础上估计刺激的最优解释。然而，这些概念并没有提出实现感知和做出决策的新皮质机制。本模型无需借助贝叶斯概念就能解释行为和神经生理学决策数据，并且与这些数据的其他现有模型不同，它能响应实验性视觉刺激生成感知表征和选择动态。定量模型模拟包括在固定持续时间和反应时间任务中，不同输入模糊度水平下正确和错误试验期间LIP神经元动态的时间进程，以及行为准确性和反应时间特性。模型MT/MST相互作用计算随机点运动刺激的全局方向，而模型LIP计算导致眼球扫视运动的随机感知决策。
