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Strategy Generalization Across Orientation Tasks: Testing a Computational Cognitive Model

机译：跨定向任务的策略概括：测试计算认知模型

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摘要

Humans use their spatial information processing abilities flexibly to facilitate problem solving and decision making in a variety of tasks. This article explores the question of whether a general strategy can be adapted for performing two different spatial orientation tasks by testing the predictions of a computational cognitive model. Human performance was measured on an orientation task requiring participants to identify the location of a target either on a map (find-on-map) or within an egocentric view of a space (find-in-scene). A general strategy instantiated in a computational cognitive model of the find-on-map task, based on the results from Gunzelmann and Anderson (2006), was adapted to perform both tasks and used to generate performance predictions for a new study. The qualitative fit of the model to the human data supports the view that participants were able to tailor a general strategy to the requirements of particular spatial tasks. The quantitative differences between the predictions of the model and the performance of human participants in the new experiment expose individual differences in sample populations. The model provides a means of accounting for those differences and a framework for understanding how human spatial abilities are applied to naturalistic spatial tasks that involve reasoning with maps.
机译：人类可以灵活地利用其空间信息处理能力，以促进解决各种任务中的问题和做出决策。本文探讨了通过测试计算认知模型的预测，是否可以将通用策略调整为执行两个不同的空间定向任务的问题。人类绩效是在一项定向任务上测量的，该任务要求参与者在地图上（在地图上查找）或在空间的以自我为中心的视图内（场景查找）识别目标的位置。根据Gunzelmann和Anderson（2006）的结果，在“按图查找”任务的计算认知模型中实例化的通用策略适用于执行这两项任务，并用于为新研究生成性能预测。该模型对人类数据的定性拟合支持以下观点：参与者能够针对特定空间任务的需求量身定制总体策略。该模型的预测与新实验中人类参与者的表现之间的数量差异暴露了样本群体中的个体差异。该模型为解决这些差异提供了一种方法，并为理解人类空间能力如何应用于涉及地图推理的自然空间任务提供了框架。

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