I argue that cue integration, a psychophysiological mechanism from vision and multisensory perception, offers a computational linking hypothesis between psycholinguistic theory and neurobiological models of language. I propose that this mechanism, which incorporates probabilistic estimates of a cue's reliability, might function in language processing from the perception of a phoneme to the comprehension of a phrase structure. I briefly consider the implications of the cue integration hypothesis for an integrated theory of language that includes acquisition, production, dialogue and bilingualism, while grounding the hypothesis in canonical neural computation.
Introduction
Despite major advances in the last decades of language research, the linking hypothesis between ever-more plausible neurobiological models of language and ever-better empirically supported psycholinguistic models is weak, if not absent. Moreover, we are struggling to answer, and even to ask well, questions like why is language behavior the way it is? How is language processed? What is “processing difficulty?” What is the source of difficulty in psychological and neurobiological terms? What can it tell us about the computational architecture of the language system? These questions, however frustratingly difficult, speak to our persistent awe at the fact that we humans flap our articulators, we move the air, and in doing so, stimulate formally-describable complex meaning in the heads of other people. And then those people usually do it to us back. So how do we, or rather, our brains, do it?
There must be a good reason for the weak link between psycho- and neurobiological theories of language—namely that it is really hard to find a concept that would be explanatory on multiple levels of analysis in cognitive science (see Marr, 1982). Questions like what makes language the way it is probe the computational level of Marr's tri-level hypothesis, asking what the system's goal is, what computation is being performed and to what end. Questions like how does the system do it occur at the algorithmic level, asking what the nature of the mechanism that carries out the computation is. Recent debates in cognitive science have cast these two kinds of questions in opposition, or at least, in opposing theoretical camps. Bayesian modelers of perception and cognition form the statistical what camp, and non-Bayesians the mechanistic how camp (Jones and Love, 2011; Bowers and Davis, 2012). The what camp is purportedly less interested in how the mind “does it,” but is focused on reverse engineering how the natural world (or the statistics that describe it) makes cognition the way it is. The how camp purportedly wants to uncover the mechanism that the mind/brain uses, instead of a statistical approximation (Jones and Love, 2011; Bowers and Davis, 2012). I will argue that any model of language computation must answer both how and what questions, and the best model will most likely include both mechanistic and probabilistic elements. The model articulated here asserts a mechanistic psychological operation over representations derived via Bayesian inference (or an approximation there of), which are represented by neural population codes that are flexibly combined using two simple canonical neural computations: summation and normalization.
I argue that cue integration, a psychophysiological mechanism from vision and multisensory perception, offers a computational linking hypothesis between psycholinguistic theory and neurobiological models of language. I propose that this mechanism, which incorporates probabilistic estimates of a cue's reliability, might function in language processing from the perception of a phoneme to the comprehension of a phrase structure. I briefly consider the implications of the cue integration hypothesis for an integrated theory of language that includes acquisition, production, dialogue and bilingualism, while grounding the hypothesis in canonical neural computation.
Introduction
Despite major advances in the last decades of language research, the linking hypothesis between ever-more plausible neurobiological models of language and ever-better empirically supported psycholinguistic models is weak, if not absent. Moreover, we are struggling to answer, and even to ask well, questions like why is language behavior the way it is? How is language processed? What is “processing difficulty?” What is the source of difficulty in psychological and neurobiological terms? What can it tell us about the computational architecture of the language system? These questions, however frustratingly difficult, speak to our persistent awe at the fact that we humans flap our articulators, we move the air, and in doing so, stimulate formally-describable complex meaning in the heads of other people. And then those people usually do it to us back. So how do we, or rather, our brains, do it?
There must be a good reason for the weak link between psycho- and neurobiological theories of language—namely that it is really hard to find a concept that would be explanatory on multiple levels of analysis in cognitive science (see Marr, 1982). Questions like what makes language the way it is probe the computational level of Marr's tri-level hypothesis, asking what the system's goal is, what computation is being performed and to what end. Questions like how does the system do it occur at the algorithmic level, asking what the nature of the mechanism that carries out the computation is. Recent debates in cognitive science have cast these two kinds of questions in opposition, or at least, in opposing theoretical camps. Bayesian modelers of perception and cognition form the statistical what camp, and non-Bayesians the mechanistic how camp (Jones and Love, 2011; Bowers and Davis, 2012). The what camp is purportedly less interested in how the mind “does it,” but is focused on reverse engineering how the natural world (or the statistics that describe it) makes cognition the way it is. The how camp purportedly wants to uncover the mechanism that the mind/brain uses, instead of a statistical approximation (Jones and Love, 2011; Bowers and Davis, 2012). I will argue that any model of language computation must answer both how and what questions, and the best model will most likely include both mechanistic and probabilistic elements. The model articulated here asserts a mechanistic psychological operation over representations derived via Bayesian inference (or an approximation there of), which are represented by neural population codes that are flexibly combined using two simple canonical neural computations: summation and normalization.
.png "Hướng dẫn download ti liệu")
%20(1).png "Hướng dẫn download ti liệu")
%20(1)%20(1).png)
.png)
.png)
%20(1).png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
%20(1).png "Gói VIP Member EBOOKBKMT - Hỗ trợ tài liệu nhanh nhất, ưu tiên cho thành viên VIP (Update 2026)")
.png "SÁCH - Quản trị vận tải đa phương thức (Hồ Thị Thu Hòa)")
