发散思维的序列位置效应：创新想法动态产生机制的新视角

发散思维的序列位置效应是指随着时间的推移,单位时间内想法生成的数量逐渐降低,但想法的独创性逐渐升高的现象。联想理论和执行控制假说分别侧重对序列位置效应的个体差异和认知加工过程进行解释。神经影像学研究表明,在想法产生的整个阶段序列位置效应依赖于默认网络后部和突显网络的持续增强激活和协同合作;在想法产生的后期阶段,颞-顶区域alpha能量值以及执行控制网络与默认网络的协同模式增强。未来研究需要结合语义和神经动态分析等技术,对序列位置效应的特异性机制、动态加工机制以及影响因素进行深入探究,从而为解构创新思维的动态过程提供新的视角。     

Trauma-Informed Child Welfare: From Training to Practice and Policy Change

ABSTRACT

Multiple child welfare systems and agencies have embraced efforts to become more trauma-informed in recent years. However, the field of trauma-informed care is still somewhat in its infancy and only recently has the focus shifted from theoretical concepts into concrete practices that can be identified, measured, evaluated, and sustained over time. One of the key resources designed to help support the development of a trauma-informed child welfare system is the Child Welfare Trauma Training Toolkit (CWTTT). The CWTTT was developed in 2008 as a collaborative process that included members of the Child Welfare Committee of the National Child Traumatic Stress Network (NCTSN). Originally, the CWTTT was developed as a training resource to support child welfare professionals in increasing their understanding regarding trauma and its impact on children. Over time, the CWTTT has undergone multiple iterations and has been involved in numerous state and county initiatives to support their efforts in becoming more trauma-informed. This article will provide historical context on the development of the CWTTT, detailing its evolution since 2007, followed by highlighting the current efforts and future directions of the CWTTT.     

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.

     

"易卦"的序结构数学模型在对策理论中的应用

通过分析人类的基本行为元素,并借助于"卦象"序结构数学模型的建立,从而初步讨论了一般谋略方法的产生和起源,以及谋略对策的系统核心元素结构;最后讨论了它们在应用中的初步分类.     

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.