Ness Tal, Meltzer-Asscher Aya
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Linguistics Department, Tel Aviv University, Tel Aviv, Israel.
Front Psychol. 2021 Apr 14;12:622873. doi: 10.3389/fpsyg.2021.622873. eCollection 2021.
Recent studies indicate that the processing of an unexpected word is costly when the initial, disconfirmed prediction was strong. This penalty was suggested to stem from commitment to the strongly predicted word, requiring its inhibition when disconfirmed. Additional studies show that comprehenders rationally adapt their predictions in different situations. In the current study, we hypothesized that since the disconfirmation of strong predictions incurs costs, it would also trigger adaptation mechanisms influencing the processing of subsequent (potentially) strong predictions. In two experiments (in Hebrew and English), participants made speeded congruency judgments on two-word phrases in which the first word was either highly constraining (e.g., "climate," which strongly predicts "change") or not (e.g., "vegetable," which does not have any highly probable completion). We manipulated the proportion of disconfirmed predictions in highly constraining contexts between participants. The results provide additional evidence of the costs associated with the disconfirmation of strong predictions. Moreover, they show a reduction in these costs when participants experience a high proportion of disconfirmed strong predictions throughout the experiment, indicating that participants adjust the strength of their predictions when strong prediction is discouraged. We formulate a Bayesian adaptation model whereby prediction failure cost is weighted by the participant's belief (updated on each trial) about the likelihood of encountering the expected word, and show that it accounts for the trial-by-trial data.
最近的研究表明,当最初被否定的预测很强时,处理一个意外的词成本很高。这种代价被认为源于对强烈预测词的执着,当被否定时需要抑制它。更多研究表明,理解者会在不同情况下合理调整他们的预测。在当前的研究中,我们假设,由于强烈预测的否定会产生成本,它也会触发影响后续(潜在)强烈预测处理的适应机制。在两个实验(希伯来语和英语)中,参与者对两个词的短语进行快速一致性判断,其中第一个词要么具有高度约束性(例如,“气候”,强烈预测“变化”),要么没有(例如,“蔬菜”,没有任何高度可能的后续词)。我们在参与者之间操纵了高度约束性语境中被否定预测的比例。结果为与强烈预测的否定相关的成本提供了更多证据。此外,当参与者在整个实验中经历了高比例的被否定的强烈预测时,这些成本有所降低,这表明当强烈预测不被鼓励时,参与者会调整他们预测的强度。我们构建了一个贝叶斯适应模型,其中预测失败成本由参与者(在每次试验中更新)对遇到预期词可能性的信念加权,并表明它能够解释逐次试验的数据。