A neuroconstructivist model of past tense development and processing

We present a neural network model of learning and processing the English past tense that is based on the notion that experience-dependent cortical development is a core aspect of cognitive development. During learning the model adds and removes units and connections to develop a task-specific final architecture. The model provides an integrated account of characteristic errors during learning the past tense, adult generalization to pseudoverbs, and dissociations between verbs observed after brain damage in aphasic patients. We put forward a theory of verb inflection in which a functional processing architecture develops through interactions between experience-dependent brain development and the structure of the environment, in this case, the statistical properties of verbs in the language. The outcome of this process is a structured processing system giving rise to graded dissociations between verbs that are easy and verbs that are hard to learn and process. In contrast to dual-mechanism accounts of inflection, we argue that describing dissociations as a dichotomy between regular and irregular verbs is a post hoc abstraction and is not linked to underlying processing mechanisms. We extend current single-mechanism accounts of inflection by highlighting the role of structural adaptation in development and in the formation of the adult processing system. In contrast to some single-mechanism accounts, we argue that the link between irregular inflection and verb semantics is not causal and that existing data can be explained on the basis of phonological representations alone. This work highlights the benefit of taking brain development seriously in theories of cognitive development.     

Introduction to the special section on theory and data in categorization: Integrating computational, behavioral, and cognitive neuroscience approaches

This special section brings together behavioral, computational, mathematical, and neuroimaging approaches to understand the processes underlying category learning. Over the past decade, there has been growing convergence in research on categorization, with computational-mathematical models influencing the interpretation of brain imaging and neuropsychological data, and with cognitive neuroscience findings influencing the development and refinement of models. Classic debates between single-system and multiple-memory-system theories have become more nuanced and focused. Multiple brain areas and cognitive processes contribute to categorization, but theories differ markedly in whether and when those neurocognitive components are recruited for different aspects of categorization. The articles in this special section approach this issue from several diverse angles.     

Mechanisms of developmental change in infant categorization

Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in capturing a range of developmental phenomena, in particular on-line within-task category learning by young infants. Here we describe two new models. One demonstrates how age dependent changes in neural receptive field sizes can explain observed changes in on-line category learning between 3 and 10 months of age. The other aims to reconcile two conflicting views of infant categorization by focusing on the different task requirements of preferential looking and manual exploration studies. A dual-memory hypothesis posits that within-task category learning that drives looking time behaviors is based on a fast-learning memory system, whereas categorization based on background experience and assessed by paradigms requiring complex motor behavior relies on a second, slow-learning system. The models demonstrate how emphasizing the mechanistic causes of behaviors leads to discovery of deeper, more explanatory accounts of learning and development.     

Neuroconstructivism

Neuroconstructivism is a theoretical framework focusing on the construction of representations in the developing brain. Cognitive development is explained as emerging from the experience-dependent development of neural structures supporting mental representations. Neural development occurs in the context of multiple interacting constraints acting on different levels, from the individual cell to the external environment of the developing child. Cognitive development can thus be understood as a trajectory originating from the constraints on the underlying neural structures. This perspective offers an integrated view of normal and abnormal development as well as of development and adult processing, and it stands apart from traditional cognitive approaches in taking seriously the constraints on cognition inherent to the substrate that delivers it.     

Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract?

Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal cognitive models can decompose observed behavioral data into multiple latent cognitive processes, allowing brain measurements to be associated with a particular cognitive process more precisely and more confidently. Second, cognitive neuroscience can provide additional data to inform the development of formal cognitive models, providing greater constraint than behavioral data alone. We argue that these fields are mutually dependent; not only can models guide neuroscientific endeavors, but understanding neural mechanisms can provide key insights into formal models of cognition.     

LEARNING THEORY APPROACHES TO PERSUASION

This review synthesizes relevant research dealing with the processes of learning and suggests its applications to compliance gaining. The two major issues addressed are: (1) to what degree can learning theories explain the acquisition of new attitudes and behaviors, and (2) to what degree are attitudinal and behavioral changes governed by learning theory principles? The learning theories discussed are grouped into three categories: stimulus-response or connectionist approaches; cognitive approaches; and stochastic, mathematical, and cybernetic approaches. The stimulus-response models, which encompass most of the research examined in this paper, are further broken down into four types: (1) classical conditioning, (2) contiguity models, (3) instrumental (or operant) conditioning and (4) models including drive and drive reduction. Principles and major research evidence from numerous learning theories are reviewed and analyzed, and suggestions are made as to how this evidence may aid in the construction of more complete theories of persuasion and attitude change.     

Cognitive developmental biology: history, process and fortune's wheel

Biological contributions to cognitive development continue to be conceived predominantly along deterministic lines, with proponents of different positions arguing about the preponderance of gene-based versus experience-based influences that organize brain circuits irreversibly during prenatal or early postnatal life, and evolutionary influences acting through selection on small numbers of genes. This article discusses evolutionary, mechanistic and probabilistic aspects of developmental processes that cognitive scientists need to better integrate. Developmental processes inseparably fuse experience-dependent and experience-independent components, have important stochastic contributions, and exhibit a greater degree of mechanistic continuity between developing and adult nervous systems than previously thought. Their balanced integration leads to new models for "critical or sensitive" period phenomena and behavioral biases. A general understanding of behavioral development - cognitive developmental biology--will require better coordination between comparative animal and human developmental research programs.     

Neural reuse: a fundamental organizational principle of the brain

An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role of neural plasticity (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design.     

类别学习的神经心理学研究

类别学习的神经心理学研究是检验类别学习理论的一种有效研究范式,它有助于我们对类别学习理论的深入理解,以及对认知活动过程和脑机制的认识。作者对基底神经节功能障碍患者、遗忘症患者和其他类型的神经症患者的类别学习研究进行了回顾,对这些研究可能存在的单系统理论和多系统理论解释进行了讨论,并指出了研究中可能存在的取样偏差问题和未来研究中需要在研究手段、研究设计、被试取样等方面加以改进的建议     

Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.     

文盲文字加工的行为和脑机制

文盲作为一个特殊的被试群体,对其的研究一直备受关注。文盲研究最初集中在文盲的口语加工和语音意识等方面。其后比较关注文盲字词的语音和语义加工。随着脑成像技术的发展,对文盲的文字加工脑认知机制进行了研究,探讨脑功能组织是如何受到早期学习的调制,左侧梭状回在字词识别中的作用及其文盲脑功能偏侧化问题。以后此类研究应关注文盲字词识别学习过程中的脑功能变化和文化认知对认知老化的影响等科学问题。     

Enriched learning: behavior,brain, and computation

The presence of complementary information across multiple sensory or motor modalities during learning, referred to as multimodal enrichment, can markedly benefit learning outcomes. Why is this? Here, we integrate cognitive, neuroscientific, and computational approaches to understanding the effectiveness of enrichment and discuss recent neuroscience findings indicating that crossmodal responses in sensory and motor brain regions causally contribute to the behavioral benefits of enrichment. The findings provide novel evidence for multimodal theories of enriched learning, challenge assumptions of longstanding cognitive theories, and provide counterevidence to unimodal neurobiologically inspired theories. Enriched educational methods are likely effective not only because they may engage greater levels of attention or deeper levels of processing, but also because multimodal interactions in the brain can enhance learning and memory.     

The Newell Test for a theory of cognition

Newell (1980; 1990) proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories by a broad set of criteria such as these and attended to the weaknesses such evaluations revealed. To illustrate how theories can be evaluated we apply these criteria to both classical connectionism (McClelland & Rumelhart 1986; Rumelhart & McClelland 1986b) and the ACT-R theory (Anderson & Lebiere 1998). The strengths of classical connectionism on this test derive from its intense effort in addressing empirical phenomena in such domains as language and cognitive development. Its weaknesses derive from its failure to acknowledge a symbolic level to thought. In contrast, ACT-R includes both symbolic and sub-symbolic components. The strengths of the ACT-R theory derive from its tight integration of the symbolic component with the sub-symbolic component. Its weaknesses largely derive from its failure, as yet, to adequately engage in intensive analyses of issues related to certain criteria on Newell's list.     

第二语言学习与脑可塑性

脑可塑性指人脑会因为环境刺激、认知需求和行为经验而产生功能或结构改变。近10年来的单双语者对比和语言训练研究结果表明, 不论儿童、青年或老年人, 第二语言学习和使用都能改变其脑运行模式并带来相应结构变化, 包括灰质(GM)体积和白质(WM)密度增加, 且长期持续的双语经验还能形成认知优势, 帮助抵制由老化导致的负面认知影响。基于脑可塑性概念及其研究证据, 从双语经验与语言训练两方面, 对比分析了长期和短期第二语言学习引起脑功能或结构变化及其内在机制, 并对未来相关研究进行了展望。     

Representations gone mental

Many philosophers and psychologists have attempted to elucidate the nature of mental representation by appealing to notions like isomorphism or abstract structural resemblance. The ‘structural representations’ that these theorists champion are said to count as representations by virtue of functioning as internal models of distal systems. In his 2007 book, Representation Reconsidered, William Ramsey endorses the structural conception of mental representation, but uses it to develop a novel argument against representationalism, the widespread view that cognition essentially involves the manipulation of mental representations. Ramsey argues that although theories within the ‘classical’ tradition of cognitive science once posited structural representations, these theories are being superseded by newer theories, within the tradition of connectionism and cognitive neuroscience, which rarely if ever appeal to structural representations. Instead, these theories seem to be explaining cognition by invoking so-called ‘receptor representations’, which, Ramsey claims, aren’t genuine representations at all—despite being called representations, these mechanisms function more as triggers or causal relays than as genuine stand-ins for distal systems. I argue that when the notions of structural and receptor representation are properly explicated, there turns out to be no distinction between them. There only appears to be a distinction between receptor and structural representations because the latter are tacitly conflated with the ‘mental models’ ostensibly involved in offline cognitive processes such as episodic memory and mental imagery. While structural representations might count as genuine representations, they aren’t distinctively mental representations, for they can be found in all sorts of non-intentional systems such as plants. Thus to explain the kinds of offline cognitive capacities that have motivated talk of mental models, we must develop richer conceptions of mental representation than those provided by the notions of structural and receptor representation.     

Structural Imaging Measures of Brain Aging

During the course of normal aging, biological changes occur in the brain that are associated with changes in cognitive ability. This review presents data from neuroimaging studies of primarily “normal” or healthy brain aging. As such, we focus on research in unimpaired or nondemented older adults, but also include findings from lifespan studies that include younger and middle aged individuals as well as from populations with prodromal or clinically symptomatic disease such as cerebrovascular or Alzheimer’s disease. This review predominantly addresses structural MRI biomarkers, such as volumetric or thickness measures from anatomical images, and measures of white matter injury and integrity respectively from FLAIR or DTI, and includes complementary data from PET and cognitive or clinical testing as appropriate. The findings reveal highly consistent age-related differences in brain structure, particularly frontal lobe and medial temporal regions that are also accompanied by age-related differences in frontal and medial temporal lobe mediated cognitive abilities. Newer findings also suggest that degeneration of specific white matter tracts such as those passing through the genu and splenium of the corpus callosum may also be related to age-related differences in cognitive performance. Interpretation of these findings, however, must be tempered by the fact that comorbid diseases such as cerebrovascular and Alzheimer’s disease also increase in prevalence with advancing age. As such, this review discusses challenges related to interpretation of current theories of cognitive aging in light of the common occurrence of these later-life diseases. Understanding the differences between “Normal” and “Healthy” brain aging and identifying potential modifiable risk factors for brain aging is critical to inform potential treatments to stall or reverse the effects of brain aging and possibly extend cognitive health for our aging society.     

Neurowissenschaften und Psychotherapie

The rapid development of the cognitive neurosciences is based on interdisciplinary efforts and on a broad repertoire of research methods ranging from molecular biological to functional neuroimaging procedures suitable for the study of complex, ongoing intrapsychic processes. Selected recent results that are relevant for psychotherapy are discussed. They are based on genetics, on animal studies about the impact of early relational experience on brain development, further on memory research, on functional neuroimaging (PET, fMRI), and on first studies about the impact of psychotherapy on the brain. Neuroscience studies confirm some major psychoanalytic hypotheses (crucial role of early experience, unconscious processing) and question others (e. g. structural models). They help to overcome the split between “somatic” and “psychological” theories and treatments and open new, interdisciplinary research perspectives for psychotherapy.     

Does learning behavior augment cognitive ability as an indicator of academic achievement?

Measures of cognitive ability have a rich history of accounting for meaningful levels of achievement variance. In contrast to other student characteristics, however, they are somewhat limited in terms of their intervention relevance and treatment validity. Alternatively, children's observable learning behaviors are believed to enhance both treatment validity and the predictions afforded by cognitive ability. This study examined student learning behaviors in the context of cognitive ability and academic achievement. Three structural models were evaluated on a sample of 1304 students ranging in age from 6 to 17 years. Results supported the unique relationship between learning behavior and academic achievement, beyond cognitive ability. Multi-group structural equation modeling (SEM) analysis revealed that these findings were invariant across groups differing by gender and ethnicity. These findings are consistent with previous observations that children's behavioral features in learning situations will supplement the outcome-oriented standardized tests of cognitive ability.