社会交流中人际神经同步的认知机制

近年来,许多研究者开始关注社会交流中的人际神经同步机制,并将人际神经同步作为研究社会交流的一个神经指标,这对于揭示社会交流的本质和规律具有重要意义。本文从心理理论和镜像神经系统的角度,分析社会交流中神经同步的认知机制及其影响因素。未来的研究应关注这两套机制是否因交流目的、对象、形式或内容的不同,而在不同的脑区表现出神经同步,进而引发了不同认知机制的争议;以及这两套机制各自或协同工作适用的情景和任务。     

Is it worth generating rules from neural network ensembles?

Although many authors generated comprehensible models from individual networks, much less work has been done in the explanation of ensembles. DIMLP is a special neural network model from which rules are generated at the level of a single network and also at the level of an ensemble of networks. We applied ensembles of 25 DIMLP networks to several datasets of the public domain and a classification problem related to post-translational modifications of proteins. For the classification problems of the public domain, the average predictive accuracy of rulesets extracted from ensembles of neural networks was significantly better than the average predictive accuracy of rulesets generated from ensembles of decision trees. By varying the architectures of DIMLP networks we found that the average predictive accuracy of rules, as well as their complexity were quite stable. The comparison to other rule extraction techniques applied to neural networks showed that rules generated from DIMLP ensembles gave very good results. In the last problem related to bioinformatics, the best result obtained by ensembles of DIMLP networks was also significantly better than the best result obtained by ensembles of decision trees. Thus, although neural networks take much longer to train than decision trees and also rules are generated at a greater computational cost (however, still polynomial), at least for several classification problems it was worth using neural network ensembles, as extracted rules were more accurate, on average. The DIMLP software is available for PC-Linux under http://us.expasy.org/people/Guido.Bologna.html.     

The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness

How can we explain consciousness? This question has become a vibrant topic of neuroscience research in recent decades. A large body of empirical results has been accumulated, and many theories have been proposed. Certain theories suggest that consciousness should be explained in terms of brain functions, such as accessing information in a global workspace, applying higher order to lower order representations, or predictive coding. These functions could be realized by a variety of patterns of brain connectivity. Other theories, such as Information Integration Theory (IIT) and Recurrent Processing Theory (RPT), identify causal structure with consciousness. For example, according to these theories, feedforward systems are never conscious, and feedback systems always are. Here, using theorems from the theory of computation, we show that causal structure theories are either false or outside the realm of science.     

The role of levels of processing in disentangling the ERP signatures of conscious visual processing

We aimed to distinguish electrophysiological signatures of visual awareness from other task-related processes through manipulating the level of processing of visual stimuli. During an event-related EEG experiment, 36 subjects performed either color (low-level condition) or magnitude (high-level condition) evaluations of masked digits. Participants also assessed subjective visibility of each stimulus using the Perceptual Awareness Scale (PAS). Mean amplitude of the components of interest was analyzed (VAN − 140–240 ms; LP − 380–480 ms) with weighted regression mixed model. In the VAN component time window the mean amplitude correlated with PAS rating in both conditions. Mean amplitude in the LP time window correlated with PAS ratings in the high-level condition, but not in the low-level condition. Our results support the temporal unfolding of ERP makers of conscious processing, with an early component reflecting the initial perceptual experience and a late component being a correlate of the conscious experience of non-perceptual information.     

I’ll just watch: Do the pro-social effects of coordination really generalize to non-actors?

ABSTRACT

Moving in time together has been shown to cultivate pro-social effects in co-actors, such as cooperation and helping. But less is known about who these effects apply to – whether they are restricted only to co-actors, or whether they generalize to those not involved in the coordination. One difference between past work finding generalized vs. restricted effects is whether these “outsiders” were present for the coordination or not. The present study explores whether the pro-social effects of coordination are seen towards observers as well as co-actors, and whether the absence or presence of observers during the coordination is a determining factor. Results show that greater cooperation following coordination is only seen amongst co- actors, regardless of whether the observers were present during the task or not. Findings are discussed in the context of the literature and alternative explanations for research showing generalized effects are suggested.     

Rhythms of the body,rhythms of the brain: Respiration,neural oscillations,and embodied cognition

In spite of its importance as a life-defining rhythmic movement and its constant rhythmic contraction and relaxation of the body, respiration has not received attention in Embodied Cognition (EC) literature. Our paper aims to show that (1) respiration exerts significant and unexpected influence on cognitive processes, and (2) it does so by modulating neural synchronization that underlies specific cognitive processes. Then, (3) we suggest that the particular example of respiration may function as a model for a general mechanism through which the body influences cognitive functioning. Finally, (4) we work out the implications for EC, draw a parallel to the role of gesture, and argue that respiration sometimes plays a double, pragmatic and epistemic, role, which reduces the cognitive load. In such cases, consistent with EC, the overall cognitive activity includes a loop-like interaction between neural and non-neural elements.     

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.     

Face recognition algorithms and the other‐race effect: computational mechanisms for a developmental contact hypothesis

People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other‐race effect” occurs as a result of the greater experience we have with own‐ versus other‐race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other‐race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other‐race effect. Consistent with the predictions of a developmental contact hypothesis, “experience‐based models” demonstrated an other‐race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model's experience with faces of different races. When the model's representation relied on a feature set optimized to encode the information in the learned faces, experience‐based algorithms recognized minority‐race faces more accurately than majority‐race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own‐race faces. This feature space limits the quality of face representations for other‐race faces.     

The temporally-integrated causality landscape: A theoretical framework for consciousness and meaning

Theoretical approaches to understanding consciousness have begun to converge upon areas of general agreement, yet substantive differences remain. Here, I introduce a new theoretical framework for the emergence of consciousness from the functional integration of the thalamocortical system: the Temporally-Integrated Causality Landscape (TICL). TICL presents a novel perspective which addresses important phenomenological characteristics of consciousness that other frameworks, such as IIT, do not. First, the TICL is based upon the observation that conscious experiences are temporally continuous, not discrete. Secondly, the TICL establishes a thalamocortical basis for the point-of-view. According to TICL, consciousness is composed of contents that arise from neuronal subsystems that have meaning from the point-of-view of the larger, integrated system in which they are nested. Meaningful contents emerge from the subsystems because they exhibit a level of temporally-integrated causality (TIC) that is distinguishable from that of the larger system.