Department of Psychology, University of Amsterdam, the Netherlands.
Psychol Rev. 2012 Jan;119(1):201-15. doi: 10.1037/a0026275. Epub 2011 Nov 21.
In their influential Psychological Review article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the DDM and accomplish optimal decision making. Here we show that these conclusions depend on how the models handle negative activation values and (for the LCA) across-trial variability in response conservativeness. Negative neural activations are undesirable for both neurophysiological and mathematical reasons. However, when negative activations are truncated to 0, the equivalence to the DDM is lost. Simulations show that this concern has practical ramifications: the DDM generally outperforms truncated versions of the LCA and the FFI, and the parameter estimates from the neural models can no longer be mapped onto those of the DDM in a simple fashion. We show that for both models, truncation may be avoided by assuming a baseline activity for each accumulator. This solution allows the LCA to approximate the DDM and the FFI to be identical to the DDM.
在他们颇具影响力的《心理学评论》文章中，Bogacz、Brown、Moehlis、Holmes 和 Cohen（2006 年）讨论了最优决策是如何通过漂移扩散模型（DDM）来完成的。作者表明，神经抑制模型，如漏失竞争累加器模型（LCA）和前馈抑制模型（FFI），可以模拟 DDM 并完成最优决策。在这里，我们表明这些结论取决于模型如何处理负激活值以及（对于 LCA）跨试验响应保守性的可变性。负神经激活由于神经生理学和数学方面的原因都是不理想的。然而，当负激活被截断为 0 时，与 DDM 的等价性就丧失了。模拟表明，这一问题具有实际影响：DDM 通常优于 LCA 和 FFI 的截断版本，并且神经模型的参数估计值不再能够以简单的方式映射到 DDM 的参数估计值上。我们表明，对于这两个模型，通过为每个累加器假设一个基线活动，可以避免截断。该解决方案允许 LCA 近似 DDM，FFI 等同于 DDM。
