Colas Jaron T
Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA, United States of America.
PLoS One. 2017 Oct 27;12(10):e0186822. doi: 10.1371/journal.pone.0186822. eCollection 2017.
In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.
原则上，决策的形式动力学模型除了能够表示感知决策外，还具有表示基于价值（即偏好）决策背后的基本计算的潜力。诸如竞赛模型和漂移扩散模型等顺序抽样模型，因其简单性、分析易处理性和最优性而一直很受欢迎，但它们的一些最新同类模型则是以更具可行性为目标设计的，作为可由实际神经系统实现的架构。本文在连接心理现象和潜在神经生理机制的中间分析层面提出了联结主义模型。从既定的竞赛、漂移扩散、前馈抑制、归一化除法和竞争累加器模型中提取元素的几个此类模型，针对拟合人类参与者基于享乐价值而非传统感知属性在食物之间进行选择的实证数据进行了测试。即使仅考虑模拟行为的表现，尽管更具神经合理性的模型保持简约，但在数量和质量上都与更规范的竞赛或漂移扩散模型有所不同。为了最好地捕捉该范式，制定了一个新颖的六参数计算模型，其特征包括通过相互抑制的分层竞争水平以及注意力调制的静态近似，这促进了“赢家通吃”处理。此外，一项涵盖多个相关实验的元分析验证了模型预测的人类基于价值选择趋势和伴随反应时间的稳健性。这些发现对于根据计算模型分析神经生理数据还有进一步的意义，本文也将从这个新角度进行讨论。
