Neurolinguistic Computational Models

Journal of Neurolinguistics, 2012

The cognitive neuroscience of language is an exciting interdisciplinary perspective that suffers from unresolved epistemological and methodological issues. Despite the impressive amount of neural evidence accumulated until now, the field of research results fragmented and it is quite difficult to reach a unit of analysis and consensus on the object of study. This frustrating state of the art results in a detrimental reductionism consisting in the practice of associating linguistic computation hypothesized at theoretical level with neurobiological computation. However, these two entities are at the moment ontologically incommensurable. The problem lies in the fact that a theory of language consistent with a range of neurophysiological and neuroimaging techniques of investigation and verifiable through neural data is still lacking. In this article, I focus on the main issues, questions, and concerns that prevent the integrated study of language and brain and I explore a feasible way for linguistics to pursue a theory susceptible of neuroscientific testability in the light of recent neurocognitive models and of data on the functional-anatomic organization of language in the brain. Finally, I discuss a possible interdisciplinary program in order to achieve a theory capable of predictions on the real-time neural constrains characterizing the biological bases of language.

1970

Abstract: Noting that linguistics and the neurological sciences have developed independently, this paper presents a coordinated approach to man's understanding of language, cognition, and mind. A neurological model is developed following a discussion of the rationale of such an approach. Chapters include:(1) the relation of neurological evidence to models of language,(2) a schematic model of language,(3) functional anatomy of the central language system,(4) functional anatomy of the peripheral language modalities, ...

Behavioral and Brain Sciences, 1979

We provide a threefold taxonomy of models in neurolinguistics: faculty models, which embrace the work of classical connectionists and holists and of such modern workers as Geschwind; process models, which are exemplified by the work of Luria, and which fractionate psycholinguistic tasks and ascribe the components to particular brain regions; and representational models, which use specific linguistic representations to build a psycholinguistic analysis of aphasic performance. We argue that further progress requires that neurolinguistics become more computational, using techniques from Artificial Intelligence to model the cooperative computation underlying language processing at a level of detail consonant with linguistic representations. Finally, we note that current neurolinguistics makes virtually no contact with the synapse-cell-circuit level of analysis characteristic of twentieth-century neuroscience. We suggest that the cooperative computation models we envisage provide the necessary intermediary between current neurolinguistic analysis and the utilization of the fruits of modern neuroanatomy, neurochemistry, and neurophysiology.

Neurocomputing, 2007

The computational program for theoretical neuroscience initiated by calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels -computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational -significant progress has been made in the understanding of cognition. In the past three decades, computational principles have been discovered that are common to a wide range of functions in perception (vision, hearing, olfaction) and action (motor control). More recently, these principles have been applied to the analysis of cognitive tasks that require dealing with structured information, such as visual scene understanding and analogical reasoning. Insofar as language relies on cognition-general principles and mechanisms, it should be possible to capitalize on the recent advances in the computational study of cognition by extending its methods to linguistics.

2008

Instead going straight into dealing with specific approaches, issues, and domains of computational cognitive modeling, it would be more appropriate to first take some time to explore a few general questions that lie at the very core of cognitive science and computational cognitive modeling. What is computational cognitive modeling? What exactly can it contribute to cognitive science? What has it contributed thus far? Where is it going? Answering such questions may sound overly defensive to the insiders of computational cognitive modeling, and may even seem so to some other cognitive scientists, but they are very much needed in a volume like this-because they lie at the very foundation of this field. Many insiders and outsiders alike would like to take a balanced and rational look at these questions, without indulging in excessive cheer-leading, which, as one would expect, happens sometimes amongst computational modeling enthusiasts. However, given the large number of issues involved and the complexity of these issues, only a cursory discussion is possible in this introductory chapter. One may thus view this chapter as a set of pointers to the existing literature, rather than a full-scale discussion. 1 1 What is Computational Cognitive Modeling? Research in computational cognitive modeling, or simply computational psychology, explores the essence of cognition (broadly defined, including motivation, emotion, perception, and so on) and various cognitive functionalities through developing detailed, process-based understanding by specifying corresponding computational models (in a broad sense) of representations, mechanisms, and processes. It embodies descriptions of cognition in computer algorithms and programs, based on computer science (Turing 1950). That is, it imputes computational processes (in a broad sense) onto cognitive functions, and thereby it produces runnable computational models. Detailed simulations are then conducted based on the computational models (see, e.g., Newell 1990, Rumelhart et al 1986, Sun 2002). Right from the beginning of the formal establishment of cognitive science around late 1970's, computational modeling has been a mainstay of cognitive science. 1 In general, models in cognitive science may be roughly categorized into computational, mathematical, or verbal-conceptual models (see, e.g., Bechtel and Graham 1998). Computational models (broadly defined) present process details using algorithmic descriptions. Mathematical models presents relationships between variables using mathematical equations. Verbal-conceptual models describe entities, relations, and processes in rather informal natural languages. Each model, regardless of its genre, might as well be viewed as a theory of whatever phenomena it purports to capture (as argued extensively before by, for example, Newell 1990, Sun 2005). 1 The roots of cognitive science can, of course, be traced back to much earlier times. For example, Newell and Simon's early work in the 60's and 70's has been seminal (see, e.g., Newell and Simon 1976). The work of Miller, Galanter, and Pribram (1960) has also been highly influential. See the chapter by Boden in this volume for a more complete historical perspective (see also Boden 2006).

Behavioral and Brain Sciences, 1979

We provide a threefold taxonomy of models in neurolinguistics: faculty models, which embrace the work of classical connectionists and holists and of such modern workers as Geschwind; process models, which are exemplified by the work of Luria, and which fractionate psycholinguistic tasks and ascribe the components to particular brain regions; and representational models, which use specific linguistic representations to build a psycholinguistic analysis of aphasic performance. We argue that further progress requires that neurolinguistics become more computational, using techniques from Artificial Intelligence to model the cooperative computation underlying language processing at a level of detail consonant with linguistic representations. Finally, we note that current neurolinguistics makes virtually no contact with the synapse-cell-circuit level of analysis characteristic of twentieth-century neuroscience. We suggest that the cooperative computation models we envisage provide the necessary intermediary between current neurolinguistic analysis and the utilization of the fruits of modern neuroanatomy, neurochemistry, and neurophysiology.

2015

Brain and Language, 2000

The problem of the brain is one of misconceptions. No other entity seems to be so intuitive, yet proves to be so counterintuitive. Our folk psychological notions and theoretical history contribute to this confusion by often clouding our interpretations of the neuroanatomical data, be it at the level of the single cell, oxygen-saturated hemoglobin, or mass bioelectrical firings. The misconception is that our categories (be they for language, memory, or attention) fail to map easily onto tissue. Our intuitions for how we think, process information, make decisions, and speak do not correspond to some Cartesian Theater or Central Executive pulling our neuronal strings. Thus, one scientific problem to be addressed in the next millennium must be one of categorization. We must find ways to effectively categorize and dissect our intuitions regarding language and the brain such that they are more in line with our growing mass of data. To facilitate research and our understanding, models are required to help define the relationship between language and the brain, the system along which our brains are organized from the neuroanatomical underpinnings to the level of the mind. Unfortunately, we have seen few innovations in such models throughout most of the present century.

Word Knowledge and Word Usage

Over the last decades, a growing body of evidence on the mechanisms governing lexical storage, access, acquisition and processing has questioned traditional models of language architecture and word usage based on the hypothesis of a direct correspondence between modular components of grammar competence (lexicon vs. rules), processing correlates (memory vs. computation) and neuro-anatomical localizations (prefrontal vs. temporo-parietal perisylvian areas of the left hemisphere). In the present chapter, we explore the empirical and theoretical consequences of a distributed, integrative model of the mental lexicon, whereby words are seen as emergent properties of the functional interaction between basic, language-independent processing principles and the language-specific nature and organization of the input. From this perspective, language learning appears to be inextricably related to the way language is processed and internalized by the speakers, and key to an interdisciplinary understanding of such a way, in line with Tomaso Poggio's suggestion that the development of a cognitive skill is causally and ontogenetically prior to its execution (and sits "on top of it"). In particular, we discuss conditions, potential and prospects of the epistemological continuity between psycholinguistic and computational modelling of word learning, and illustrate the yet largely untapped potential of their integration. We use David Marr's hierarchy to clarify the complementarity of the two viewpoints. Psycholinguistic models are informative about how speakers learn to use language (interfacing Marr's levels 1 and 2). When we move from the psycholinguistic analysis of the functional operations involved in language learning to an algorithmic description of how they are computed, computer simulations can help us explore the relation between speakers' behavior and general learning principles in more detail. In the end, psycho-computational