Jaffe-Dax Sagi, Raviv Ofri, Jacoby Nori, Loewenstein Yonatan, Ahissar Merav
Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem 9190401, Israel.
Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem 9190401, Israel, Neurobiology Department, Cognitive Sciences Department and the Federmann Center for the Study of Rationality, Hebrew University of Jerusalem, Jerusalem 9190401, Israel, and.
J Neurosci. 2015 Sep 2;35(35):12116-26. doi: 10.1523/JNEUROSCI.1302-15.2015.
Dyslexics are diagnosed for their poor reading skills, yet they characteristically also suffer from poor verbal memory and often from poor auditory skills. To date, this combined profile has been accounted for in broad cognitive terms. Here we hypothesize that the perceptual deficits associated with dyslexia can be understood computationally as a deficit in integrating prior information with noisy observations. To test this hypothesis we analyzed the performance of human participants in an auditory discrimination task using a two-parameter computational model. One parameter captures the internal noise in representing the current event, and the other captures the impact of recently acquired prior information. Our findings show that dyslexics' perceptual deficit can be accounted for by inadequate adjustment of these components; namely, low weighting of their implicit memory of past trials relative to their internal noise. Underweighting the stimulus statistics decreased dyslexics' ability to compensate for noisy observations. ERP measurements (P2 component) while participants watched a silent movie indicated that dyslexics' perceptual deficiency may stem from poor automatic integration of stimulus statistics. This study provides the first description of a specific computational deficit associated with dyslexia.
This study presents the first attempt to specify the mechanisms underlying dyslexics' perceptual difficulties computationally by applying a specific model, inspired by the Bayesian framework. This model dissociates between the contribution of sensory noise and that of the prior statistics in an auditory perceptual decision task. We show that dyslexics cannot compensate for their perceptual noise by incorporating prior information. By contrast, adequately reading controls' usage of previous information is often close to optimal. We used ERP measurements to assess the neuronal stage of this deficit. We found that unlike their peers, dyslexics' ERP responses are not sensitive to the relations between the current observation and the prior observation, indicating that they cannot establish a reliable prior.
诵读困难症患者因其阅读能力差而被诊断出来,但他们通常还存在言语记忆差的问题,并且常常伴有听觉技能不佳的情况。迄今为止,这种综合特征已从广义的认知角度进行了解释。在此,我们假设与诵读困难症相关的感知缺陷在计算上可理解为将先验信息与噪声观测进行整合时的缺陷。为了验证这一假设,我们使用双参数计算模型分析了人类参与者在听觉辨别任务中的表现。一个参数捕捉表示当前事件时的内部噪声,另一个参数捕捉最近获取的先验信息的影响。我们的研究结果表明,诵读困难症患者的感知缺陷可通过这些成分的调整不足来解释;也就是说,相对于内部噪声,他们对过去试验的内隐记忆权重较低。对刺激统计信息的权重过低降低了诵读困难症患者补偿噪声观测的能力。参与者观看无声电影时的ERP测量(P2成分)表明,诵读困难症患者的感知缺陷可能源于对刺激统计信息的自动整合不佳。本研究首次描述了与诵读困难症相关的特定计算缺陷。
本研究首次尝试通过应用受贝叶斯框架启发的特定模型,从计算角度明确诵读困难症患者感知困难背后的机制。该模型在听觉感知决策任务中区分了感官噪声和先验统计信息的贡献。我们表明,诵读困难症患者无法通过纳入先验信息来补偿其感知噪声。相比之下,阅读能力正常的对照组对先前信息的使用通常接近最优。我们使用ERP测量来评估这种缺陷的神经元阶段。我们发现,与同龄人不同,诵读困难症患者的ERP反应对当前观测与先前观测之间的关系不敏感,这表明他们无法建立可靠的先验。