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Elspeth Jajdelska 《Philosophical Psychology》2019,32(4):560-583
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. 相似文献
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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.
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David G Loughrey Ernest Mihelj Brian A Lawlor 《Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition》2021,28(1):1-25
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. 相似文献
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