University of Kent, England, UK.
Cognition. 2011 May;119(2):179-96. doi: 10.1016/j.cognition.2011.01.005. Epub 2011 Feb 24.
The time-course of representing others' perspectives is inconclusive across the currently available models of ToM processing. We report two visual-world studies investigating how knowledge about a character's basic preferences (e.g. Tom's favourite colour is pink) and higher-order desires (his wish to keep this preference secret) compete to influence online expectations about subsequent behaviour. Participants' eye movements around a visual scene were tracked while they listened to auditory narratives. While clear differences in anticipatory visual biases emerged between conditions in Experiment 1, post-hoc analyses testing the strength of the relevant biases suggested a discrepancy in the time-course of predicting appropriate referents within the different contexts. Specifically, predictions to the target emerged very early when there was no conflict between the character's basic preferences and higher-order desires, but appeared to be relatively delayed when comprehenders were provided with conflicting information about that character's desire to keep a secret. However, a second experiment demonstrated that this apparent 'cognitive cost' in inferring behaviour based on higher-order desires was in fact driven by low-level features between the context sentence and visual scene. Taken together, these results suggest that healthy adults are able to make complex higher-order ToM inferences without the need to call on costly cognitive processes. Results are discussed relative to previous accounts of ToM and language processing.
目前关于 ToM 加工的各种模型都无法确定代表他人观点的时间进程。我们报告了两项视觉世界研究,旨在调查关于角色的基本偏好(例如汤姆最喜欢的颜色是粉色)和更高阶欲望(他希望保守这个偏好的秘密)的知识如何相互竞争,以影响对后续行为的在线预期。参与者在听音频叙事时,其眼球运动在视觉场景中被跟踪。虽然在实验 1 中,条件之间出现了明显的预期视觉偏差,但事后分析测试了相关偏差的强度,表明在不同的语境下,预测适当的参照对象的时间进程存在差异。具体来说,当角色的基本偏好和高阶欲望之间没有冲突时,对目标的预测会很早就出现,但当理解者接收到关于该角色保守秘密的欲望的冲突信息时,预测似乎会相对延迟。然而,第二项实验表明,基于高阶欲望推断行为的这种明显的“认知成本”实际上是由上下文句子和视觉场景之间的低级特征驱动的。总之,这些结果表明,健康的成年人能够进行复杂的高阶 ToM 推理,而无需调用昂贵的认知过程。结果与以前的 ToM 和语言处理的解释进行了讨论。