Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well as investigations into the structure of scientific and ordinary information, I maintain neither argument establishes its architectural conclusion. Similar considerations also undermine Fodor’s decades-long diagnosis of artificial intelligence research as confounded by an inability to circumscribe the amount of information relevant to inferential processes. This diagnosis is particularly inapposite with respect to Bayes nets, since one of their strengths as machine learning systems has been their capacity to reason probabilistically about large data sets whose size overwhelms the capacities of individual human reasoners. A general moral follows from these criticisms: Insights into artificial and human cognitive systems are likely to be cultivated by focusing greater attention on the structure and density of connections among items of information that are available to them.

中文翻译：

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认知架构、整体推理和贝叶斯网络

认知科学中两个长期存在的论点援引了整体推理在计算上不可行的假设。第一个是福多对普通归纳推理的计算建模的怀疑论点。第二种主张采用 Cosmides、Tooby 和 Sperber 提出的那种模块化计算机制。基于与贝叶斯网络相关的机器学习的进步，以及对科学和普通信息结构的调查，我认为这两个论点都没有建立其架构结论。类似的考虑也破坏了福多对人工智能研究长达数十年的诊断，因为无法限制与推理过程相关的信息量。这种诊断特别不适合贝叶斯网，因为它们作为机器学习系统的优势之一是它们能够对大数据集进行概率推理，这些数据集的大小超过了单个人类推理者的能力。从这些批评中得出一个普遍的道德：通过更加关注可用信息项之间的连接结构和密度，可能会培养对人工和人类认知系统的洞察力。