发展性阅读障碍与小脑异常：小脑的功能和两者的因果关系

发展性阅读障碍(下文简称为"阅读障碍")不仅会影响个人的终身发展,还会对社会造成沉重的经济负担,深入探讨相关的神经机制,对实现阅读障碍的早期预测和干预十分重要。以往关于阅读障碍神经机制的模型多集中于大脑,近些年的研究发现,阅读障碍也与小脑异常有关,但到目前为止我们仍不清楚两者的关系。通过总结最新的研究进展,我们发现小脑在阅读障碍中可能发挥着多种功能,且小脑异常与阅读障碍可能互为因果。在此基础之上,我们提出了"阅读中小脑与大脑的功能映射假说",旨在从一个全新的角度揭示小脑与阅读的关系,以及两者与大脑的关系。相关内容对全面揭示阅读障碍的神经机制,以及小脑在高级认知加工中的作用,具有重要的启示意义。     

How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.     

The flow of narrative in the mind unmoored: An account of narrative processing

Verbal narratives provide incomplete information and can be very long, yet readers and hearers often effortlessly fill in the gaps and make connections across long stretches of text, sometimes even finding this immersive. How is this done? In the last few decades, event-indexing situation modeling and complementary accounts of narrative emotion have suggested answers. Despite this progress, comparisons between real-life perception and narrative experience might underplay the way narrative processing modifies our world model, as well as the role of the emotions that do not relate to characters. I reframe narrative experience in predictive processing and neural networks, capturing continuity between fiction, perception, and states like dreaming and imagination, enabled by the flexible instantiation of concepts. In this framework, narrative experience is more clearly revealed as a creative experience that can share some of the phenomenology of dreams.     

Explainability of neural networks for child language: Agent-First strategy in comprehension of Korean active transitive construction

This study investigates how neural networks address the properties of children's linguistic knowledge, with a focus on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop various neural-network models and measure their classification performance on the test stimuli used in a behavioural experiment involving scrambling and omission of sentential components at varying degrees. Results show that, despite some compatibility of these models’ performance with the children's response patterns, their performance does not fully approximate the children's utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study's findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language.

Research Highlights

• This study investigates how neural networks address properties of child language.
• We focus on the Agent-First strategy in comprehension of Korean active transitive.
• Results show by-model/condition asymmetries against children's response patterns.
• This implies some limits of neural networks on revealing properties of child language.

     

Age-related hearing loss associated with altered response efficiency and variability on a visual sustained attention task

ABSTRACT

This study investigated the association between age-related hearing loss (ARHL) and differences in response efficiency and variability on a sustained attention task. The study population comprised 32 participants in a hearing loss group (HLG) and 34 controls without hearing loss (CG). Mean reaction time (RT) and accuracy were recorded to assess response efficiency. RT variability was decomposed to examine temporal aspects of variability associated with neural arousal and top-down executive control of vigilant attention. The HLG had a significantly longer mean RT, possibly reflecting a strategic approach to maintain accuracy. The HLG also demonstrated altered variability (indicative of greater decline in neural arousal) but maintained executive control that was significantly predictive of poorer response efficiency. Adults with ARHL may rely on higher-order attention networks to compensate for decline in both peripheral sensory function and in subcortical arousal systems which mediate lower-order automatic neurocognitive processes.