This paper shows how identical skills can emerge either from instruction or discovery when both result in an understanding of the causal structure of the task domain. The paper focuses on the discovery process, extending the skill acquisition model of Anderson et al. (2019) to address learning by discovery. The discovery process involves exploring the environment and developing associations between discontinuities in the task and events that precede them. The growth of associative strength in ACT-R serves to identify potential causal connections. The model can derive operators from these discovered causal relations just as does with the instructed causal information. Subjects were given a task of learning to play a video game either with a description of the game’s causal structure (Instruction) or not (Discovery). The Instruction subjects learned faster, but successful Discovery subjects caught up. After 20 3-minute games the behavior of the successful subjects in the two groups was largely indistinguishable. The play of these Discovery subjects jumped in the same discrete way as did the behavior of simulated subjects in the model. These results show how implicit processes (associative learning, control tuning) and explicit processes (causal inference, planning) can combine to produce human learning in complex environments.

本文展示了当两者都导致对任务域的因果结构的理解时，相同的技能如何从教学或发现中出现。该论文侧重于发现过程，扩展了 Anderson 等人的技能获取模型。(2019) 解决通过发现学习的问题。发现过程涉及探索环境并在任务中的不连续性与其之前的事件之间建立关联。ACT-R 中联想强度的增长有助于识别潜在的因果关系。该模型可以从这些发现的因果关系中推导出算子，就像使用指示的因果信息一样。受试者被赋予学习玩电子游戏的任务，要么描述游戏的因果结构（指令），要么不描述（发现）。指导科目学得更快，但成功的探索科目赶上了。经过 20 场 3 分钟的比赛后，两组中成功受试者的行为在很大程度上无法区分。这些发现对象的游戏以与模型中模拟对象的行为相同的离散方式跳跃。这些结果显示了隐性过程（关联学习、控制调整）和显性过程（因果推理、规划）如何结合以在复杂环境中产生人类学习。