Butz Martin V
Neuro-Cognitive Modeling Group, Department of Computer Science, University of Tübingen, Tubingen, Germany.
Department of Psychology, Faculty of Science, University of Tübingen, Tubingen, Germany.
Front Psychol. 2022 Jun 30;13:867328. doi: 10.3389/fpsyg.2022.867328. eCollection 2022.
Pursuing a precise, focused train of thought requires cognitive effort. Even more effort is necessary when more alternatives need to be considered or when the imagined situation becomes more complex. Cognitive resources available to us limit the cognitive effort we can spend. In line with previous work, an information-theoretic, Bayesian brain approach to cognitive effort is pursued: to solve tasks in our environment, our brain needs to invest information, that is, negative entropy, to impose structure, or focus, away from a uniform structure or other task-incompatible, latent structures. To get a more complete formalization of cognitive effort, a resourceful event-predictive inference model (REPI) is introduced, which offers computational and algorithmic explanations about the latent structure of our generative models, the active inference dynamics that unfold within, and the cognitive effort required to steer the dynamics-to, for example, purposefully process sensory signals, decide on responses, and invoke their execution. REPI suggests that we invest cognitive resources to infer preparatory priors, activate responses, and anticipate action consequences. Due to our limited resources, though, the inference dynamics are prone to task-irrelevant distractions. For example, the task-irrelevant side of the imperative stimulus causes the Simon effect and, due to similar reasons, we fail to optimally switch between tasks. An actual model implementation simulates such task interactions and offers first estimates of the involved cognitive effort. The approach may be further studied and promises to offer deeper explanations about why we get quickly exhausted from multitasking, how we are influenced by irrelevant stimulus modalities, why we exhibit magnitude interference, and, during social interactions, why we often fail to take the perspective of others into account.
追求精确、专注的思维过程需要认知努力。当需要考虑更多的选择或者想象的情境变得更加复杂时，就需要付出更多的努力。我们可用的认知资源限制了我们能够投入的认知努力。与之前的研究一致，本文采用了一种信息论的、贝叶斯大脑的认知努力方法：为了解决我们环境中的任务，我们的大脑需要投入信息，即负熵，以从均匀结构或其他与任务不兼容的潜在结构中施加结构或焦点。为了更完整地形式化认知努力，引入了一个资源丰富的事件预测推理模型（REPI），该模型提供了关于我们生成模型的潜在结构、其中展开的主动推理动力学以及引导动力学所需的认知努力的计算和算法解释——例如，有目的地处理感官信号、决定反应并调用其执行。REPI表明，我们投入认知资源来推断预备先验、激活反应并预测行动后果。然而，由于我们的资源有限，推理动力学容易受到与任务无关的干扰。例如，命令性刺激中与任务无关的方面会导致西蒙效应，并且由于类似的原因，我们无法在任务之间进行最佳切换。一个实际的模型实现模拟了这种任务交互，并提供了所涉及的认知努力的初步估计。该方法可能会得到进一步研究，并有望对我们为什么会因多任务处理而迅速疲惫、我们如何受到无关刺激模态的影响、我们为什么会表现出大小干扰以及在社交互动中我们为什么经常未能考虑他人的观点等问题提供更深入的解释。
