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。     

Prenatal auditory experience and its sequelae

Towards the end of the second trimester of gestation, a human fetus is able to register environmental sounds. This in utero auditory experience is characterized by comprising strongly low-pass-filtered versions of sounds from the external world. Here, we present computational tests of the hypothesis that this early exposure to severely degraded auditory inputs serves an adaptive purpose—it may induce the neural development of extended temporal integration. Such integration can facilitate the detection of information carried by low-frequency variations in the auditory signal, including emotional or other prosodic content. To test this prediction, we characterized the impact of several training regimens, biomimetic and otherwise, on a computational model system trained and tested on the task of emotion recognition. We find that training with an auditory trajectory recapitulating that of a neurotypical infant in the pre-to-postnatal period results in temporally extended receptive field structures and yields the best subsequent accuracy and generalization performance on the task of emotion recognition. This strongly suggests that the progression from low-pass-filtered to full-frequency inputs is likely to be an adaptive feature of our development, conferring significant benefits to later auditory processing abilities relying on temporally extended analyses. Additionally, this finding can help explain some of the auditory impairments associated with preterm births, suggests guidelines for the design of auditory environments in neonatal care units, and points to enhanced training procedures for computational models.     

Environmental and mental conditions predicting the experience of involuntary musical imagery: An experience sampling method study

An experience sampling method (ESM) study on 40 volunteers was conducted to explore the environmental factors and psychological conditions related to involuntary musical imagery (INMI) in everyday life. Participants reported 6 times per day for one week on their INMI experiences, relevant contextual information and associated environmental conditions. The resulting data was modeled with Bayesian networks and led to insights into the interplay of factors related to INMI experiences. The activity that a person is engaged was found to play an important role in the experience of mind wandering, which in turn enables the experience of INMI. INMI occurrence is independent of the time of the day while the INMI trigger affects the subjective evaluation of the INMI experience. The results are compared to findings from earlier studies based on retrospective surveys and questionnaires and highlight the advantage of ESM techniques in research on spontaneous experiences like INMI.     

Self-presentation styles and Problematic use of Internet communicative services: The role of the concerns over behavioral displays of imperfection

The current study had the dual purpose of identifying consequences associated with perfectionistic self-presentation (PSP) and evaluating these factors as mediator of the proposed link between PSP and Problematic use of Internet communicative services (GPIU). The present study hypothesized that a self-presentation style characterized by the need to avoid displaying imperfections is associated with GPIU because communicating online rather than in person affords greater control through the reduction of non-verbal cues and the greater temporal flexibility. A sample of 200 university student participants completed measures assessing PSP, GPIU, and perceived controllable aspects of behaviors associated with relying on computer mediated communications (i.e. managing nonverbal displays and buying more time before having to respond). Structural equation modeling confirmed that those who systematically try to avoid revealing their supposed “less than perfect” behaviors or performance place great emphasis on the reduction of nonverbal cues and the temporal flexibility offered by the computer mediated interactions, which, in turn predicts GPIU levels. The findings are discussed in terms of their implications for GPIU and for the perfectionism social disconnection model.     

Adaptive rewiring in weighted networks

Human connectome studies suggest that the brain has a modular small world network structure with rich-club effect. Such structure emerges spontaneously in simple model neural networks, (e.g. coupled maps), through adaptive rewiring according to the dynamic functional connectivity. The utility of adaptive rewiring has so far exclusively been demonstrated for unweighted networks; it is anything but guaranteed to work as well for weighted networks. We investigate adaptive rewiring in weighted networks, comparing various right-skewed, symmetrical, and left-skewed fixed weight distributions. We examine how network clustering, path length, modularity, and rich club coefficients develop for weakly, intermediate and strongly coupled networks. At low coupling strength, the weight distribution, as well as episodes of functional synchrony, have a significant effect on network evolution. With increased coupling strengths, all weighted networks robustly develop architectures similar to the unweighted ones. Adaptive rewiring appears relatively ineffective in networks with (biologically implausibly) extreme right-skewed weight distributions but performed most economically in biologically plausible log-normal distributions.     

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.     

Symbolically speaking: a connectionist model of sentence production

The ability to combine words into novel sentences has been used to argue that humans have symbolic language production abilities. Critiques of connectionist models of language often center on the inability of these models to generalize symbolically (Fodor & Pylyshyn, 1988; Marcus, 1998). To address these issues, a connectionist model of sentence production was developed. The model had variables (role‐concept bindings) that were inspired by spatial representations (Landau & Jackendoff, 1993). In order to take advantage of these variables, a novel dual‐pathway architecture with event semantics is proposed and shown to be better at symbolic generalization than several variants. This architecture has one pathway for mapping message content to words and a separate pathway that enforces sequencing constraints. Analysis of the model's hidden units demonstrated that the model learned different types of information in each pathway, and that the model's compositional behavior arose from the combination of these two pathways. The model's ability to balance symbolic and statistical behavior in syntax acquisition and to model aphasic double dissociations provided independent support for the dual‐pathway architecture.