亚临床自闭症和分裂型人格特征语义预测的时间稳定性。

IF 4.1 Q2 PSYCHIATRY
Elisabeth F Sterner, Andrea Greve, Franziska Knolle
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引用次数: 0

摘要

语言障碍是精神分裂症和自闭症谱系障碍的核心症状,与预测性语言处理的缺陷有关。虽然在这两种情况下都报道了语义预测的改变,但很少有人知道语义预测是否会随着时间的推移而稳定。因此,本研究的目的是探讨语义先验信念的时间稳定性,重点关注具有分裂型和自闭症特征的个体。115名参与者,评估为亚临床分裂型(SPQ5ls;平均值= 77.99,SD = 39.31)和自闭症特征(AQ;mean = 15.67, SD = 6.01),在两个时间点完成听觉稳定性范式,以研究语义预测的时间稳定性。在时间点1,由一个会话组成,参与者听了240个句子开头,这些句子开头的可预测性各不相同(例如,高的:“游泳者跳进了……”;低:“孩子把玩具藏在……下面”),并为每个句子提供预测——最后一个单词。时间点二,由两部分组成,每部分由120个旧句和120个新句组成。除了最后的单词预测,句子回忆被评估来检查记忆对预测稳定性的影响。广义线性混合模型表明,更高的可预测性导致语义预测的时间稳定性更好。重要的是,分裂型和自闭症特征的增加与稳定性的降低有关,特别是在高度可预测的环境中,稳定的预测通常有助于有效的语言处理。虽然较差的句子回忆与更大的不稳定性有关,特别是在中等和低可预测性的环境中,但这并不能解释与分裂型和自闭症特征相关的稳定性降低。这些发现表明,具有较高分裂型和自闭症特征的个体很难形成稳定、持久的语义预测,这可能会导致高效语言处理的困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temporal stability of semantic predictions in subclinical autistic and schizotypal personality traits.

Language impairments are core symptoms of both schizophrenia and autism spectrum disorders and have been linked to deficits in predictive language processing. While altered use of semantic predictions have been reported in both conditions, little is known whether semantic predictions are stable over time. The goal of this study was therefore to investigate the temporal stability of semantic prior beliefs focusing on individuals with schizotypal and autistic traits. 115 participants, assessed for subclinical schizotypal (SPQ5ls; mean = 77.99, SD = 39.31) and autistic traits (AQ; mean = 15.67, SD = 6.01), completed an auditory stability paradigm at two timepoints to investigate the temporal stability of semantic predictions. At timepoint one, consisting of one session, participants listened to 240 sentence beginnings varying in predictability (e.g., high: "The swimmer jumped into the…"; low: "The child hid the toy under the…") and provided a prediction for each sentence-final word. Timepoint two, consisting of two sessions, each session comprising of 120 old and 120 new sentences. In addition to final-word predictions, sentence recall was assessed to examine the influence of memory on prediction stability. Generalized linear mixed models revealed that higher predictability led to greater temporal stability of semantic predictions. Importantly, increasing schizotypal and autistic traits were associated with reduced stability, particularly in highly predictable contexts where stable predictions typically facilitate efficient language processing. While poorer sentence recall was linked to greater instability, especially in medium- and low-predictability contexts, it did not account for the reduced stability observed in relation to schizotypal and autistic traits. These findings suggest that individuals with higher schizotypal and autistic traits struggle to form stable, lasting semantic predictions, which may contribute to difficulties in efficient language processing.

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