Biologically Plausible,Human‐Scale Knowledge Representation

Several approaches to implementing symbol‐like representations in neurally plausible models have been proposed. These approaches include binding through synchrony (Shastri & Ajjanagadde, 1993 ), “mesh” binding (van der Velde & de Kamps, 2006 ), and conjunctive binding (Smolensky, 1990 ). Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate that the biologically plausible structured representations employed in the Semantic Pointer Architecture (SPA) approach to modeling cognition (Eliasmith, 2013 ) do scale appropriately. Specifically, we construct a spiking neural network of about 2.5 million neurons that employs semantic pointers to successfully encode and decode the main lexical relations in WordNet, which has over 100,000 terms. In addition, we show that the same representations can be employed to construct recursively structured sentences consisting of arbitrary WordNet concepts, while preserving the original lexical structure. We argue that these results suggest that semantic pointers are uniquely well‐suited to providing a biologically plausible account of the structured representations that underwrite human cognition.     

Pure word deafness and the bilateral processing of the speech code

The analysis of pure word deafness (PWD) suggests that speech perception, construed as the integration of acoustic information to yield representations that enter into the linguistic computational system, (i) is separable in a modular sense from other aspects of auditory cognition and (ii) is mediated by the posterior superior temporal cortex in both hemispheres. PWD data are consistent with neuropsychological and neuroimaging evidence in a manner that suggests that the speech code is analyzed bilaterally. The typical lateralization associated with language processing is a property of the computational system that acts beyond the analysis of the input signal. The hypothesis of the bilateral mediation of the speech code does not imply that both sides execute the same computation. It is proposed that the speech signal is asymmetrically analyzed in the time domain, with left‐hemisphere mechanisms preferentially extracting information over shorter (25–50 ms) temporal integration windows and right mechanisms over longer (150–250 ms) windows.     

Understanding the emergence of modularity in neural systems

Modularity in the human brain remains a controversial issue, with disagreement over the nature of the modules that exist, and why, when, and how they emerge. It is a natural assumption that modularity offers some form of computational advantage, and hence evolution by natural selection has translated those advantages into the kind of modular neural structures familiar to cognitive scientists. However, simulations of the evolution of simplified neural systems have shown that, in many cases, it is actually non-modular architectures that are most efficient. In this paper, the relevant issues are discussed and a series of simulations are presented that reveal crucial dependencies on the details of the learning algorithms and tasks that are being modelled, and the importance of taking into account known physical brain constraints, such as the degree of neural connectivity. A pattern is established which provides one explanation of why modularity should emerge reliably across a range of neural processing tasks.     

The neural network model of organizational identification

This paper proposes the Neural Network Model of Organizational Identification; the model depicts organizational identification as an associative link within an organization member’s social knowledge structure of self as it relates to a focal organization. Within this knowledge structure, organization identification connects self to organization via an attribute sub-network that includes self-concept and organization identity and via a valance sub-network that includes organization based self-esteem and attitudinal commitment. This model draws on the principles of balance-congruity, imbalance dissonance, and differentiation [Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, self-esteem, and self-concept. Psychological Review, 109, 3–25.] to predict relationships between these organizational constructs. The Neural Network Model of Organizational Identification is parsimonious yet it effectively integrates and synthesizes the burgeoning literature on organizational identification. By operating at a neural network level of analysis, the model departs substantially from existing organization models by (1) specifying unique construct definitions; (2) offering an alternative perspective of the affective/cognitive dimensions and interrelationships; (3) introducing the concept of implicit cognition to the literature on organizational identification, which makes apparent problems with current measures; and (4) explaining phenomena not explained in existing models. This perspective adds precision and reveals that organizational identification is interconnected within a reciprocal network of mutual causality.     

The self-regulating brain: Cortical-subcortical feedback and the development of intelligent action

To speak of cognitive regulation versus emotion regulation may be misleading. However, some forms of regulation are carried out by executive processes, subject to voluntary control, while others are carried out by “automatic” processes that are far more primitive. Both sets of processes are in constant interaction, and that interaction gives rise to a stream of activity that is both cognitive and emotional. Studying the brain helps us understand these reciprocal regulatory influences in some detail. Cortical activities regulate subcortical activities through executive modulation of prepotent appraisals and emotional responses. Subcortical systems regulate the cortex by tuning its activities to the demands or opportunities provided by the environment. Cortical controls buy us time, as needed for planning and intelligent action. Subcortical controls provide energy, focus, and direction, as needed for relevant emotion-guided behaviour. We review the neural processes at work in both directions of regulatory activity, looking at the anterior cingulate cortex (ACC) as a hub of cortical systems mediating downward control, and discussing limbic, hypothalamic, and brainstem systems that mediate upward control. A macrosystem that displays both directions of control includes the ACC and the amygdala within a feedback circuit whose features vary with clinical-personality differences. Developmental changes in ACC-mediated self-regulation support advances in directed attention, response inhibition, and self-monitoring. Developmental changes in amygdala-mediated self-regulation involve the compilation of meanings that direct thought and behaviour, thus consolidating individual differences over the lifespan. In this way, the capacity to exert voluntary control develops alongside the accumulation of associations that trigger the responses that demand control. The balance between these developmental progressions has implications for personality formation and mental health.     

Neural network processing of natural language: II. Towards a unified model of corticostriatal function in learning sentence comprehension and non-linguistic sequencing

A central issue in cognitive neuroscience today concerns how distributed neural networks in the brain that are used in language learning and processing can be involved in non-linguistic cognitive sequence learning. This issue is informed by a wealth of functional neurophysiology studies of sentence comprehension, along with a number of recent studies that examined the brain processes involved in learning non-linguistic sequences, or artificial grammar learning (AGL). The current research attempts to reconcile these data with several current neurophysiologically based models of sentence processing, through the specification of a neural network model whose architecture is constrained by the known cortico-striato-thalamo-cortical (CSTC) neuroanatomy of the human language system. The challenge is to develop simulation models that take into account constraints both from neuranatomical connectivity, and from functional imaging data, and that can actually learn and perform the same kind of language and artificial syntax tasks. In our proposed model, structural cues encoded in a recurrent cortical network in BA47 activate a CSTC circuit to modulate the flow of lexical semantic information from BA45 to an integrated representation of meaning at the sentence level in BA44/6. During language acquisition, corticostriatal plasticity is employed to allow closed class structure to drive thematic role assignment. From the AGL perspective, repetitive internal structure in the AGL strings is encoded in BA47, and activates the CSTC circuit to predict the next element in the sequence. Simulation results from Caplan's [Caplan, D., Baker, C., & Dehaut, F. (1985). Syntactic determinants of sentence comprehension in aphasia. Cognition, 21, 117-175] test of syntactic comprehension, and from Gomez and Schvaneveldts' [Gomez, R. L., & Schvaneveldt, R. W. (1994). What is learned from artificial grammars?. Transfer tests of simple association. Journal of Experimental Psychology: Learning, Memory and Cognition, 20, 396-410] artificial grammar learning experiments are presented. These results are discussed in the context of a brain architecture for learning grammatical structure for multiple natural languages, and non-linguistic sequences.     

Lexical organization and competition in first and second languages: computational and neural mechanisms

How does a child rapidly acquire and develop a structured mental organization for the vast number of words in the first years of life? How does a bilingual individual deal with the even more complicated task of learning and organizing two lexicons? It is only until recently have we started to examine the lexicon as a dynamical system with regard to its acquisition, representation, and organization. In this article, I outline a proposal based on our research that takes the dynamical approach to the lexicon, and I discuss how this proposal can be applied to account for lexical organization, structural representation, and competition within and between languages. In particular, I provide computational evidence based on the DevLex model, a self-organizing neural network model, and neuroimaging evidence based on functional magnetic resonance imaging (fMRI) studies, to illustrate how children and adults learn and represent the lexicon in their first and second languages. In the computational research, our goal has been to identify, through linguistically and developmentally realistic models, detailed cognitive mechanisms underlying the dynamic self-organizing processes in monolingual and bilingual lexical development; in the neuroimaging research, our goal has been to identify the neural substrates that subserve lexical organization and competition in the monolingual and the bilingual brain. In both cases, our findings lead to a better understanding of the interactive dynamics involved in the acquisition and representation of one or multiple languages.     

考试抄袭识别的心理测量学研究回顾

考试抄袭是最难识别的作弊方式。抄袭统计量（ACS）和人员拟合统计量（PFS）是识别抄袭的两类主要统计方法。ACS是根据被怀疑抄袭者与被抄袭者实际得分模式相似的概率来识别抄袭者。PFS 则把一个观察的项目得分模式与一定的测量模型相对比,来检验被试得分模式是否与测量模型预测的模式相吻合。其中,PFS由于在识别异常得分模式时存在一些干扰因素,所以对结果的解释存在多样性,应用较少。ACS是专门用于识别抄袭的统计方法,研究表明其识别率更高。目前ACS指标在美国的SAT和一些资格认证考试中已经得到广泛应用     

The neural correlates of consciousness: New experimental approaches needed?

It appears that consciousness science is progressing soundly, in particular in its search for the neural correlates of consciousness. There are two main approaches to this search, one is content-based (focusing on the contrast between conscious perception of, e.g., faces vs. houses), the other is state-based (focusing on overall conscious states, e.g., the contrast between dreamless sleep vs. the awake state). Methodological and conceptual considerations of a number of concrete studies show that both approaches are problematic: the content-based approach seems to set aside crucial aspects of consciousness; and the state-based approach seems over-inclusive in a way that is hard to rectify without losing sight of the crucial conscious–unconscious contrast. Consequently, the search for the neural correlates of consciousness is in need of new experimental paradigms.     

Multiple realization and methodological pluralism

Multiple realization was once taken to be a challenge to reductionist visions, especially within cognitive science, and a foundation of the “antireductionist consensus.” More recently, multiple realization has come to be challenged on naturalistic grounds, as well as on more “metaphysical” grounds. Within cognitive science, one focal issue concerns the role of neural plasticity for addressing these issues. If reorganization maintains the same cognitive functions, that supports claims for multiple realization. I take up the reorganization involved in language dysfunctions to deal with questions concerned with multiple realization and neural plasticity. Beginning with Broca’s case for localization and the nineteenth century discussion of “reorganization,” and returning to more recent evidence for neural plasticity, I argue that, in the end, there is substantial support for multiple realization in cognitive systems; I further argue that this is wholly consistent with a recognition of methodological pluralism in cognitive science.