Cochrane Aaron, Sims Chris R, Bejjanki Vikranth R, Green C Shawn, Bavelier Daphne
University of Geneva, Geneva, Switzerland.
Campus Biotech, Geneva, Switzerland.
NPJ Sci Learn. 2023 Jun 8;8(1):19. doi: 10.1038/s41539-023-00168-9.
Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants' threshold of when enough evidence is present to act.
证据积累模型极大地推动了我们对决策的理解，但它们在研究学习方面的应用并不常见。我们使用参与者在四天内完成动态随机点运动方向辨别任务的数据，对感知决策的两个组成部分（漂移扩散模型的漂移率和反应边界）的变化进行了表征。应用连续时间学习模型来表征性能变化的轨迹，不同的模型允许不同的动态变化。拟合效果最佳的模型表明，漂移率作为累积试验次数的连续指数函数而变化。相比之下，反应边界在每个日常会话中都会发生变化，但在不同的日常会话之间是独立变化的。我们的研究结果突出了整个学习轨迹中观察到的行为模式背后的两个不同过程，一个涉及感知敏感性的持续调整，另一个则是更具变化性的过程，描述了参与者在有足够证据采取行动时的阈值。
