Model evaluation is commonly performed by relying on aggregated data as well as relative metrics for model comparison and selection. In light of recent criticism about the prevailing perspectives on cognitive modeling, we investigate models for human syllogistic reasoning in terms of predictive accuracy on individual responses. By contrasting cognitive models with statistical baselines such as random guessing or the most frequently selected response option as well as data‐driven neural networks, we obtain information about the progress cognitive modeling could achieve for syllogistic reasoning to date, its remaining potential, and upper bounds of performance future models should strive to exceed. The methods presented in this article are not restricted to the domains of reasoning but generalize to other fields of behavioral research and can serve as useful additions to the modern modeler's toolbox.

中文翻译：

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个体人类认知的预测模型：上限和绩效的新视角。

通常通过依赖聚合数据以及相对度量进行模型比较和选择来执行模型评估。鉴于最近对认知建模的主流观点的批评，我们根据对个人反应的预测准确性来研究人类三段论推理的模型。通过将认知模型与统计基线（例如随机猜测或最常选择的响应选项以及数据驱动的神经网络）进行对比，我们获得了有关认知建模迄今为止可以进行三段论推理的进展信息，其剩余潜力和上限未来模型的性能应该努力超越。