The importance of situation-specific encodings: analysis of a simple connectionist model of letter transposition effects.
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| Title: | The importance of situation-specific encodings: analysis of a simple connectionist model of letter transposition effects. |
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| Authors: | Fang, Shin-Yi, Smith, Garrett, Tabor, Whitney |
| Source: | Connection Science. Jun2018, Vol. 30 Issue 2, p135-159. 25p. |
| Subjects: | Connectionism, Encoding, Cognitive science, Problem solving, Philosophy of mind, Signs & symbols |
| Abstract: | This paper analyses a three-layer connectionist network that solves a translation-invariance problem, offering a novel explanation for transposed letter effects in word reading. Analysis of the hidden unit encodings provides insight into two central issues in cognitive science: (1) What is the novelty of claims of “modality-specific” encodings? and (2) How can a learning system establish a complex internal structure needed to solve a problem? Although these topics (embodied cognition and learnability) are often treated separately, we find a close relationship between them: modality-specific features help the network discover an abstract encoding by causing it to break the initial symmetries of the hidden units in an effective way. While this neural model is extremely simple compared to the human brain, our results suggest that neural networks need not be black boxes and that carefully examining their encoding behaviours may reveal how they differ from classical ideas about the mind-world relationship. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | This paper analyses a three-layer connectionist network that solves a translation-invariance problem, offering a novel explanation for transposed letter effects in word reading. Analysis of the hidden unit encodings provides insight into two central issues in cognitive science: (1) What is the novelty of claims of “modality-specific” encodings? and (2) How can a learning system establish a complex internal structure needed to solve a problem? Although these topics (embodied cognition and learnability) are often treated separately, we find a close relationship between them: modality-specific features help the network discover an abstract encoding by causing it to break the initial symmetries of the hidden units in an effective way. While this neural model is extremely simple compared to the human brain, our results suggest that neural networks need not be black boxes and that carefully examining their encoding behaviours may reveal how they differ from classical ideas about the mind-world relationship. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09540091 |
| DOI: | 10.1080/09540091.2016.1272097 |
