Modeling Interactions between Speech Production and Perception: Speech ErrorDetection at Semantic and Phonological Levels and the Inner Speech Loop
Production and comprehension of speech are closely interwoven. For example, the ability todetect an error in one's own speech, halt speech production, and finally correct the error can beexplained by assuming an inner speech loop which continuously compares the word representationsinduced by pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-05-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2016.00051/full |
| _version_ | 1818450933436121088 |
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| author | Bernd eKröger Eric eCrawford Trevor eBekolay Chris eEliasmith |
| author_facet | Bernd eKröger Eric eCrawford Trevor eBekolay Chris eEliasmith |
| author_sort | Bernd eKröger |
| collection | DOAJ |
| description | Production and comprehension of speech are closely interwoven. For example, the ability todetect an error in one's own speech, halt speech production, and finally correct the error can beexplained by assuming an inner speech loop which continuously compares the word representationsinduced by production to those induced by perception at various cognitive levels (e.g. conceptual, word,or phonological levels). Because spontaneous speech errors are relatively rare, a picture naming and haltparadigm can be used to evoke them. In this paradigm, picture presentation (target word initiation) isfollowed by an auditory stop signal (distractor word) for halting speech production. The current studyseeks to understand the neural mechanisms governing self-detection of speech errors by developing abiologically inspired neural model of the inner speech loop. The neural model is based on the NeuralEngineering Framework (NEF) and consists of a network of about 500,000 spiking neurons. In the firstexperiment we induce simulated speech errors semantically and phonologically. In the secondexperiment, we simulate a picture naming and halt task. Target-distractor word pairs were balanced withrespect to variation of phonological and semantic similarity. The results of the first experiment show thatspeech errors are successfully detected by a monitoring component in the inner speech loop. The resultsof the second experiment show that the model correctly reproduces human behavioral data on thepicture naming and halt task. In particular, the halting rate in the production of target words was lowerfor phonologically similar words than for semantically similar or fully dissimilar distractor words. We thusconclude that the neural architecture proposed here to model the inner speech loop reflects importantinteractions in production and perception at phonological and semantic levels. |
| first_indexed | 2024-12-14T20:59:10Z |
| format | Article |
| id | doaj.art-bf110a12bfd247519eacd9f6bb45c5fb |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-14T20:59:10Z |
| publishDate | 2016-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-bf110a12bfd247519eacd9f6bb45c5fb2022-12-21T22:47:37ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882016-05-011010.3389/fncom.2016.00051202235Modeling Interactions between Speech Production and Perception: Speech ErrorDetection at Semantic and Phonological Levels and the Inner Speech LoopBernd eKröger0Eric eCrawford1Trevor eBekolay2Chris eEliasmith3RWTH Aachen UniversityMcGill UniversityUniversity of WaterlooUniversity of WaterlooProduction and comprehension of speech are closely interwoven. For example, the ability todetect an error in one's own speech, halt speech production, and finally correct the error can beexplained by assuming an inner speech loop which continuously compares the word representationsinduced by production to those induced by perception at various cognitive levels (e.g. conceptual, word,or phonological levels). Because spontaneous speech errors are relatively rare, a picture naming and haltparadigm can be used to evoke them. In this paradigm, picture presentation (target word initiation) isfollowed by an auditory stop signal (distractor word) for halting speech production. The current studyseeks to understand the neural mechanisms governing self-detection of speech errors by developing abiologically inspired neural model of the inner speech loop. The neural model is based on the NeuralEngineering Framework (NEF) and consists of a network of about 500,000 spiking neurons. In the firstexperiment we induce simulated speech errors semantically and phonologically. In the secondexperiment, we simulate a picture naming and halt task. Target-distractor word pairs were balanced withrespect to variation of phonological and semantic similarity. The results of the first experiment show thatspeech errors are successfully detected by a monitoring component in the inner speech loop. The resultsof the second experiment show that the model correctly reproduces human behavioral data on thepicture naming and halt task. In particular, the halting rate in the production of target words was lowerfor phonologically similar words than for semantically similar or fully dissimilar distractor words. We thusconclude that the neural architecture proposed here to model the inner speech loop reflects importantinteractions in production and perception at phonological and semantic levels.http://journal.frontiersin.org/Journal/10.3389/fncom.2016.00051/fullspeech production and perceptioninner speechspiking neural networksspeech errorsneurocomputational model |
| spellingShingle | Bernd eKröger Eric eCrawford Trevor eBekolay Chris eEliasmith Modeling Interactions between Speech Production and Perception: Speech ErrorDetection at Semantic and Phonological Levels and the Inner Speech Loop Frontiers in Computational Neuroscience speech production and perception inner speech spiking neural networks speech errors neurocomputational model |
| title | Modeling Interactions between Speech Production and Perception: Speech ErrorDetection at Semantic and Phonological Levels and the Inner Speech Loop |
| title_full | Modeling Interactions between Speech Production and Perception: Speech ErrorDetection at Semantic and Phonological Levels and the Inner Speech Loop |
| title_fullStr | Modeling Interactions between Speech Production and Perception: Speech ErrorDetection at Semantic and Phonological Levels and the Inner Speech Loop |
| title_full_unstemmed | Modeling Interactions between Speech Production and Perception: Speech ErrorDetection at Semantic and Phonological Levels and the Inner Speech Loop |
| title_short | Modeling Interactions between Speech Production and Perception: Speech ErrorDetection at Semantic and Phonological Levels and the Inner Speech Loop |
| title_sort | modeling interactions between speech production and perception speech errordetection at semantic and phonological levels and the inner speech loop |
| topic | speech production and perception inner speech spiking neural networks speech errors neurocomputational model |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2016.00051/full |
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