1
|
Shin U, Yi E, Song S. Investigating a neural language model's replicability of psycholinguistic experiments: A case study of NPI licensing. Front Psychol 2023; 14:937656. [PMID: 36910779 PMCID: PMC9995786 DOI: 10.3389/fpsyg.2023.937656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 01/27/2023] [Indexed: 02/25/2023] Open
Abstract
The recent success of deep learning neural language models such as Bidirectional Encoder Representations from Transformers (BERT) has brought innovations to computational language research. The present study explores the possibility of using a language model in investigating human language processes, based on the case study of negative polarity items (NPIs). We first conducted an experiment with BERT to examine whether the model successfully captures the hierarchical structural relationship between an NPI and its licensor and whether it may lead to an error analogous to the grammatical illusion shown in the psycholinguistic experiment (Experiment 1). We also investigated whether the language model can capture the fine-grained semantic properties of NPI licensors and discriminate their subtle differences on the scale of licensing strengths (Experiment 2). The results of the two experiments suggest that overall, the neural language model is highly sensitive to both syntactic and semantic constraints in NPI processing. The model's processing patterns and sensitivities are shown to be very close to humans, suggesting their role as a research tool or object in the study of language.
Collapse
Affiliation(s)
- Unsub Shin
- Department of Linguistics, Korea University, Seoul, Republic of Korea
| | - Eunkyung Yi
- Department of English Education, Ewha Womans University, Seoul, Republic of Korea
| | - Sanghoun Song
- Department of Linguistics, Korea University, Seoul, Republic of Korea
| |
Collapse
|
2
|
Brouwer H, Delogu F, Venhuizen NJ, Crocker MW. Neurobehavioral Correlates of Surprisal in Language Comprehension: A Neurocomputational Model. Front Psychol 2021; 12:615538. [PMID: 33643143 PMCID: PMC7905034 DOI: 10.3389/fpsyg.2021.615538] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/11/2021] [Indexed: 11/13/2022] Open
Abstract
Expectation-based theories of language comprehension, in particular Surprisal Theory, go a long way in accounting for the behavioral correlates of word-by-word processing difficulty, such as reading times. An open question, however, is in which component(s) of the Event-Related brain Potential (ERP) signal Surprisal is reflected, and how these electrophysiological correlates relate to behavioral processing indices. Here, we address this question by instantiating an explicit neurocomputational model of incremental, word-by-word language comprehension that produces estimates of the N400 and the P600-the two most salient ERP components for language processing-as well as estimates of "comprehension-centric" Surprisal for each word in a sentence. We derive model predictions for a recent experimental design that directly investigates "world-knowledge"-induced Surprisal. By relating these predictions to both empirical electrophysiological and behavioral results, we establish a close link between Surprisal, as indexed by reading times, and the P600 component of the ERP signal. The resultant model thus offers an integrated neurobehavioral account of processing difficulty in language comprehension.
Collapse
Affiliation(s)
- Harm Brouwer
- Department of Language Science and Technology, Saarland University, Saarbrücken, Germany
| | - Francesca Delogu
- Department of Language Science and Technology, Saarland University, Saarbrücken, Germany
| | - Noortje J Venhuizen
- Department of Language Science and Technology, Saarland University, Saarbrücken, Germany
| | - Matthew W Crocker
- Department of Language Science and Technology, Saarland University, Saarbrücken, Germany
| |
Collapse
|
3
|
Ouyang G, Zhou C. Characterizing the brain's dynamical response from scalp-level neural electrical signals: a review of methodology development. Cogn Neurodyn 2020; 14:731-742. [PMID: 33101527 DOI: 10.1007/s11571-020-09631-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/09/2020] [Accepted: 08/27/2020] [Indexed: 01/02/2023] Open
Abstract
The brain displays dynamical system behaviors at various levels that are functionally and cognitively relevant. Ample researches have examined how the dynamical properties of brain activity reflect the neural cognitive working mechanisms. A prevalent approach in this field is to extract the trial-averaged brain electrophysiological signals as a representation of the dynamical response of the complex neural system to external stimuli. However, the responses are intrinsically variable in latency from trial to trial. The variability compromises the accuracy of the detected dynamical response pattern based on trial-averaged approach, which may mislead subsequent modelling works. More accurate characterization of the brain's dynamical response incorporating single trial variability information is of profound significance in deepening our understanding of neural cognitive dynamics and brain's working principles. Various methods have been attempted to address the trial-to-trial asynchrony issue in order to achieve an improved representation of the dynamical response. We review the latest development of methodology in this area and the contribution of latency variability-based decomposition and reconstruction of dynamical response to neural cognitive researches.
Collapse
Affiliation(s)
- Guang Ouyang
- Faculty of Education, The University of Hong Kong, Pokfulam, Hong Kong Island Hong Kong
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| |
Collapse
|
4
|
Friston K. The Emperor's new topology: Comment on "Topodynamics of metastable brains" by Arturo Tozzi et al. Phys Life Rev 2017; 21:26-28. [PMID: 28506713 DOI: 10.1016/j.plrev.2017.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 05/08/2017] [Indexed: 10/19/2022]
Affiliation(s)
- Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, WC1N 3BG, UK.
| |
Collapse
|
5
|
Carmantini GS, Beim Graben P, Desroches M, Rodrigues S. A modular architecture for transparent computation in recurrent neural networks. Neural Netw 2016; 85:85-105. [PMID: 27814468 DOI: 10.1016/j.neunet.2016.09.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 08/30/2016] [Accepted: 09/05/2016] [Indexed: 10/20/2022]
Abstract
Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments.
Collapse
Affiliation(s)
| | - Peter Beim Graben
- Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.
| | | | - Serafim Rodrigues
- School of Computing and Mathematics, Plymouth University, Plymouth, United Kingdom.
| |
Collapse
|
6
|
Classifying four-category visual objects using multiple ERP components in single-trial ERP. Cogn Neurodyn 2016; 10:275-85. [PMID: 27468316 DOI: 10.1007/s11571-016-9378-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 01/21/2016] [Accepted: 01/28/2016] [Indexed: 10/22/2022] Open
Abstract
Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.
Collapse
|
7
|
Mizraji E, Lin J. Modeling spatial-temporal operations with context-dependent associative memories. Cogn Neurodyn 2015; 9:523-34. [PMID: 26379802 DOI: 10.1007/s11571-015-9343-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Revised: 03/31/2015] [Accepted: 05/07/2015] [Indexed: 11/25/2022] Open
Abstract
We organize our behavior and store structured information with many procedures that require the coding of spatial and temporal order in specific neural modules. In the simplest cases, spatial and temporal relations are condensed in prepositions like "below" and "above", "behind" and "in front of", or "before" and "after", etc. Neural operators lie beneath these words, sharing some similarities with logical gates that compute spatial and temporal asymmetric relations. We show how these operators can be modeled by means of neural matrix memories acting on Kronecker tensor products of vectors. The complexity of these memories is further enhanced by their ability to store episodes unfolding in space and time. How does the brain scale up from the raw plasticity of contingent episodic memories to the apparent stable connectivity of large neural networks? We clarify this transition by analyzing a model that flexibly codes episodic spatial and temporal structures into contextual markers capable of linking different memory modules.
Collapse
Affiliation(s)
- Eduardo Mizraji
- Group of Cognitive Systems Modeling, Biophysics Section, Facultad de Ciencias, Universidad de la República, Iguá 4225, 11400 Montevideo, Uruguay
| | - Juan Lin
- Department of Physics, Washington College, Chestertown, MD 21620 USA
| |
Collapse
|
8
|
Beim Graben P, Hutt A. Detecting event-related recurrences by symbolic analysis: applications to human language processing. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2015; 373:rsta.2014.0089. [PMID: 25548270 PMCID: PMC4281863 DOI: 10.1098/rsta.2014.0089] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Quasi-stationarity is ubiquitous in complex dynamical systems. In brain dynamics, there is ample evidence that event-related potentials (ERPs) reflect such quasi-stationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study, we elaborate a recent approach for detecting quasi-stationary states as recurrence domains by means of recurrence analysis and subsequent symbolization methods. We address two pertinent problems of contemporary recurrence analysis: optimizing the size of recurrence neighbourhoods and identifying symbols from different realizations for sequence alignment. As possible solutions for these problems, we suggest a maximum entropy criterion and a Hausdorff clustering algorithm. The resulting recurrence domains for single-subject ERPs are obtained as partition cells reflecting quasi-stationary brain states.
Collapse
Affiliation(s)
- Peter Beim Graben
- Department of German Studies and Linguistics, Humboldt- Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Axel Hutt
- Team Neurosys, INRIA CR Nancy, 54602 Villers-les-Nancy Cedex, France
| |
Collapse
|
9
|
Kempen G. Prolegomena to a neurocomputational architecture for human grammatical encoding and decoding. Neuroinformatics 2014; 12:111-42. [PMID: 23872869 DOI: 10.1007/s12021-013-9191-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
This study develops a neurocomputational architecture for grammatical processing in language production and language comprehension (grammatical encoding and decoding, respectively). It seeks to answer two questions. First, how is online syntactic structure formation of the complexity required by natural-language grammars possible in a fixed, preexisting neural network without the need for online creation of new connections or associations? Second, is it realistic to assume that the seemingly disparate instantiations of syntactic structure formation in grammatical encoding and grammatical decoding can run on the same neural infrastructure? This issue is prompted by accumulating experimental evidence for the hypothesis that the mechanisms for grammatical decoding overlap with those for grammatical encoding to a considerable extent, thus inviting the hypothesis of a single "grammatical coder." The paper answers both questions by providing the blueprint for a syntactic structure formation mechanism that is entirely based on prewired circuitry (except for referential processing, which relies on the rapid learning capacity of the hippocampal complex), and can subserve decoding as well as encoding tasks. The model builds on the "Unification Space" model of syntactic parsing developed by Vosse and Kempen (Cognition 75:105-143, 2000; Cognitive Neurodynamics 3:331-346, 2009a). The design includes a neurocomputational mechanism for the treatment of an important class of grammatical movement phenomena.
Collapse
Affiliation(s)
- Gerard Kempen
- Max Planck Institute for Psycholinguistics, PO Box 310, 6500 AH, Nijmegen, The Netherlands,
| |
Collapse
|
10
|
Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing. PLoS One 2013; 8:e52946. [PMID: 23383296 PMCID: PMC3562282 DOI: 10.1371/journal.pone.0052946] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2012] [Accepted: 11/26/2012] [Indexed: 11/19/2022] Open
Abstract
Sentence processing takes place in real-time. Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. Recent neurophysiological studies in humans suggest that the fronto-striatal system (frontal cortex, and striatum--the major input locus of the basal ganglia) plays a crucial role in this process. The current research provides a possible explanation of how certain aspects of this real-time processing can occur, based on the dynamics of recurrent cortical networks, and plasticity in the cortico-striatal system. We simulate prefrontal area BA47 as a recurrent network that receives on-line input about word categories during sentence processing, with plastic connections between cortex and striatum. We exploit the homology between the cortico-striatal system and reservoir computing, where recurrent frontal cortical networks are the reservoir, and plastic cortico-striatal synapses are the readout. The system is trained on sentence-meaning pairs, where meaning is coded as activation in the striatum corresponding to the roles that different nouns and verbs play in the sentences. The model learns an extended set of grammatical constructions, and demonstrates the ability to generalize to novel constructions. It demonstrates how early in the sentence, a parallel set of predictions are made concerning the meaning, which are then confirmed or updated as the processing of the input sentence proceeds. It demonstrates how on-line responses to words are influenced by previous words in the sentence, and by previous sentences in the discourse, providing new insight into the neurophysiology of the P600 ERP scalp response to grammatical complexity. This demonstrates that a recurrent neural network can decode grammatical structure from sentences in real-time in order to generate a predictive representation of the meaning of the sentences. This can provide insight into the underlying mechanisms of human cortico-striatal function in sentence processing.
Collapse
|
11
|
Wang WY, Hu L, Valentini E, Xie XB, Cui HY, Hu Y. Dynamic characteristics of multisensory facilitation and inhibition. Cogn Neurodyn 2012; 6:409-19. [PMID: 24082962 DOI: 10.1007/s11571-012-9197-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2011] [Revised: 02/14/2012] [Accepted: 03/01/2012] [Indexed: 11/29/2022] Open
Abstract
Multimodal integration, which mainly refers to multisensory facilitation and multisensory inhibition, is the process of merging multisensory information in the human brain. However, the neural mechanisms underlying the dynamic characteristics of multimodal integration are not fully understood. The objective of this study is to investigate the basic mechanisms of multimodal integration by assessing the intermodal influences of vision, audition, and somatosensory sensations (the influence of multisensory background events to the target event). We used a timed target detection task, and measured both behavioral and electroencephalographic responses to visual target events (green solid circle), auditory target events (2 kHz pure tone) and somatosensory target events (1.5 ± 0.1 mA square wave pulse) from 20 normal participants. There were significant differences in both behavior performance and ERP components when comparing the unimodal target stimuli with multimodal (bimodal and trimodal) target stimuli for all target groups. Significant correlation among reaction time and P3 latency was observed across all target conditions. The perceptual processing of auditory target events (A) was inhibited by the background events, while the perceptual processing of somatosensory target events (S) was facilitated by the background events. In contrast, the perceptual processing of visual target events (V) remained impervious to multisensory background events.
Collapse
Affiliation(s)
- W Y Wang
- Chinese Academy of Medical Science and Peking Union Medical College, Institute of Biomedical Engineering, Tianjin, China ; Department of Orthopaedics and Traumatology, Duchess of Kent Children's Hospital, The University of Hong Kong, 12 Sandy Bay Road, Hong Kong, China
| | | | | | | | | | | |
Collapse
|
12
|
Ursino M, Cuppini C, Magosso E. An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation. Cogn Neurodyn 2011; 5:183-207. [PMID: 22654990 DOI: 10.1007/s11571-011-9154-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2010] [Revised: 01/13/2011] [Accepted: 03/09/2011] [Indexed: 01/03/2023] Open
Abstract
This work presents a connectionist model of the semantic-lexical system. Model assumes that the lexical and semantic aspects of language are memorized in two distinct stores, and are then linked together on the basis of previous experience, using physiological learning mechanisms. Particular characteristics of the model are: (1) the semantic aspects of an object are described by a collection of features, whose number may vary between objects. (2) Individual features are topologically organized to implement a similarity principle. (3) Gamma-band synchronization is used to segment different objects simultaneously. (4) The model is able to simulate the formation of categories, assuming that objects belong to the same category if they share some features. (5) Homosynaptic potentiation and homosynaptic depression are used within the semantic network, to create an asymmetric pattern of synapses; this allows a different role to be assigned to shared and distinctive features during object reconstruction. (6) Features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. (7) Features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that, after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Word presentation, in turn, activates the corresponding features in the sensory-motor areas, recreating the same conditions occurred during learning, according to a grounded cognition viewpoint. Several words and their conceptual description can coexist in the lexical-semantic system exploiting gamma-band time division. Schematic exempla are shown, to illustrate the possibility to distinguish between words representing a category, and words representing individual members and to evaluate the role of gamma-band synchronization in priming. Finally, the model is used to simulate patients with focalized lesions, assuming a damage of synaptic strength in specific feature areas. Results are critically discussed in view of future model extensions and application to real objects. The model represents an original effort to incorporate many basic ideas, found in recent conceptual theories, within a single quantitative scaffold.
Collapse
Affiliation(s)
- Mauro Ursino
- Department of Electronics, Computer Science and Systems, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
| | | | | |
Collapse
|
13
|
Affiliation(s)
- Eduardo Mizraji
- Group of Cognitive Systems Modelling, Biophysical Section, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, 11400, Uruguay.
| | | |
Collapse
|
14
|
Maganioti AE, Hountala CD, Papageorgiou CC, Kyprianou MA, Rabavilas AD, Capsalis CN. Principal component analysis of the P600 waveform: RF and gender effects. Neurosci Lett 2010; 478:19-23. [DOI: 10.1016/j.neulet.2010.04.058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2009] [Revised: 04/08/2010] [Accepted: 04/25/2010] [Indexed: 10/19/2022]
|
15
|
Mizraji E, Pomi A, Valle-Lisboa JC. Dynamic searching in the brain. Cogn Neurodyn 2009; 3:401-14. [PMID: 19496023 PMCID: PMC2777191 DOI: 10.1007/s11571-009-9084-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2009] [Revised: 04/27/2009] [Accepted: 04/27/2009] [Indexed: 11/30/2022] Open
Abstract
Cognitive functions rely on the extensive use of information stored in the brain, and the searching for the relevant information for solving some problem is a very complex task. Human cognition largely uses biological search engines, and we assume that to study cognitive function we need to understand the way these brain search engines work. The approach we favor is to study multi-modular network models, able to solve particular problems that involve searching for information. The building blocks of these multimodular networks are the context dependent memory models we have been using for almost 20 years. These models work by associating an output to the Kronecker product of an input and a context. Input, context and output are vectors that represent cognitive variables. Our models constitute a natural extension of the traditional linear associator. We show that coding the information in vectors that are processed through association matrices, allows for a direct contact between these memory models and some procedures that are now classical in the Information Retrieval field. One essential feature of context-dependent models is that they are based on the thematic packing of information, whereby each context points to a particular set of related concepts. The thematic packing can be extended to multimodular networks involving input-output contexts, in order to accomplish more complex tasks. Contexts act as passwords that elicit the appropriate memory to deal with a query. We also show toy versions of several 'neuromimetic' devices that solve cognitive tasks as diverse as decision making or word sense disambiguation. The functioning of these multimodular networks can be described as dynamical systems at the level of cognitive variables.
Collapse
Affiliation(s)
- Eduardo Mizraji
- Group of Cognitive Systems Modeling, Biophysical Section, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, 11400 Uruguay
| | - Andrés Pomi
- Group of Cognitive Systems Modeling, Biophysical Section, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, 11400 Uruguay
| | - Juan C. Valle-Lisboa
- Group of Cognitive Systems Modeling, Biophysical Section, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, 11400 Uruguay
| |
Collapse
|
16
|
Tabor W. A dynamical systems perspective on the relationship between symbolic and non-symbolic computation. Cogn Neurodyn 2009; 3:415-27. [PMID: 19898957 DOI: 10.1007/s11571-009-9099-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2009] [Revised: 10/05/2009] [Accepted: 10/13/2009] [Indexed: 11/29/2022] Open
Abstract
It has been claimed that connectionist (artificial neural network) models of language processing, which do not appear to employ "rules", are doing something different in kind from classical symbol processing models, which treat "rules" as atoms (e.g., McClelland and Patterson in Trends Cogn Sci 6(11):465-472, 2002). This claim is hard to assess in the absence of careful, formal comparisons between the two approaches. This paper formally investigates the symbol-processing properties of simple dynamical systems called affine dynamical automata, which are close relatives of several recurrent connectionist models of language processing (e.g., Elman in Cogn Sci 14:179-211, 1990). In line with related work (Moore in Theor Comput Sci 201:99-136, 1998; Siegelmann in Neural networks and analog computation: beyond the Turing limit. Birkhäuser, Boston, 1999), the analysis shows that affine dynamical automata exhibit a range of symbol processing behaviors, some of which can be mirrored by various Turing machine devices, and others of which cannot be. On the assumption that the Turing machine framework is a good way to formalize the "computation" part of our understanding of classical symbol processing, this finding supports the view that there is a fundamental "incompatibility" between connectionist and classical models (see Fodor and Pylyshyn 1988; Smolensky in Behav Brain Sci 11(1):1-74, 1988; beim Graben in Mind Matter 2(2):29--51,2004b). Given the empirical successes of connectionist models, the more general, super-Turing framework is a preferable vantage point from which to consider cognitive phenomena. This vantage may give us insight into ill-formed as well as well-formed language behavior and shed light on important structural properties of learning processes.
Collapse
Affiliation(s)
- Whitney Tabor
- Department of Psychology and Program in Cognitive Science, U-1020, University of Connecticut, Storrs, CT 06269 USA
| |
Collapse
|
17
|
Lipinski J, Sandamirskaya Y, Schöner G. Swing it to the left, swing it to the right: enacting flexible spatial language using a neurodynamic framework. Cogn Neurodyn 2009; 3:373-400. [PMID: 19789993 DOI: 10.1007/s11571-009-9096-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2009] [Revised: 08/27/2009] [Accepted: 09/02/2009] [Indexed: 11/25/2022] Open
Abstract
Research is continually expanding the empirical and theoretical picture of embodiment and dynamics in language. To date, however, a formalized neural dynamic framework for embodied linguistic processes has yet to emerge. To advance embodied theories of language, the present work develops a formalized neural dynamic framework of spatial language that explicitly integrates linguistic processes and dynamic sensory-motor systems. We then implement and test our spatial language architecture on a robotic platform continuously linked to real-time camera input. In a suite of tasks using everyday objects we demonstrate the framework's capacity for both contextually-dependent behavioral flexibility and the seamless integration of spatial, non-spatial, and symbolic representations. To our knowledge this is the first unified, neurally-grounded architecture integrating these processes and behaviors.
Collapse
Affiliation(s)
- John Lipinski
- Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany
| | | | | |
Collapse
|
18
|
Gerth S, Beim Graben P. Unifying syntactic theory and sentence processing difficulty through a connectionist minimalist parser. Cogn Neurodyn 2009; 3:297-316. [PMID: 19795221 DOI: 10.1007/s11571-009-9093-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2009] [Accepted: 08/31/2009] [Indexed: 11/26/2022] Open
Abstract
Syntactic theory provides a rich array of representational assumptions about linguistic knowledge and processes. Such detailed and independently motivated constraints on grammatical knowledge ought to play a role in sentence comprehension. However most grammar-based explanations of processing difficulty in the literature have attempted to use grammatical representations and processes per se to explain processing difficulty. They did not take into account that the description of higher cognition in mind and brain encompasses two levels: on the one hand, at the macrolevel, symbolic computation is performed, and on the other hand, at the microlevel, computation is achieved through processes within a dynamical system. One critical question is therefore how linguistic theory and dynamical systems can be unified to provide an explanation for processing effects. Here, we present such a unification for a particular account to syntactic theory: namely a parser for Stabler's Minimalist Grammars, in the framework of Smolensky's Integrated Connectionist/Symbolic architectures. In simulations we demonstrate that the connectionist minimalist parser produces predictions which mirror global empirical findings from psycholinguistic research.
Collapse
|
19
|
beim Graben P, Potthast R. Inverse problems in dynamic cognitive modeling. CHAOS (WOODBURY, N.Y.) 2009; 19:015103. [PMID: 19335007 DOI: 10.1063/1.3097067] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.
Collapse
Affiliation(s)
- Peter beim Graben
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, Berkshire, United Kingdom.
| | | |
Collapse
|
20
|
Beim Graben P, Pinotsis D, Saddy D, Potthast R. Language processing with dynamic fields. Cogn Neurodyn 2008; 2:79-88. [PMID: 19003475 PMCID: PMC2427059 DOI: 10.1007/s11571-008-9042-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2008] [Accepted: 03/24/2008] [Indexed: 11/24/2022] Open
Abstract
We construct a mapping from complex recursive linguistic data structures to spherical wave functions using Smolensky's filler/role bindings and tensor product representations. Syntactic language processing is then described by the transient evolution of these spherical patterns whose amplitudes are governed by nonlinear order parameter equations. Implications of the model in terms of brain wave dynamics are indicated.
Collapse
Affiliation(s)
- Peter Beim Graben
- School of Psychology and Clinical Language Sciences, University of Reading, Whiteknights, P.O. Box 217, Reading, RG6 6AH, UK,
| | | | | | | |
Collapse
|