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A theory of interactional systems: semantic connections and relational contextics
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In: ISSN: 1751-0589 ; International Journal of Applied Systemic Studies ; https://hal.univ-rennes2.fr/hal-02103100 ; International Journal of Applied Systemic Studies, 2018, 8 (1), pp.32. ⟨10.1504/IJASS.2018.091845⟩ ; https://www.inderscienceonline.com/doi/abs/10.1504/IJASS.2018.091845 (2018)
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Vanilla Sequence-to-Sequence Neural Nets cannot Model Reduplication
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In: OWP Linguistics (2017)
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Inteligencia conectiva para la educación matemática infantil
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In: Comunicar: Revista científica iberoamericana de comunicación y educación, ISSN 1134-3478, Nº 52, 2017, pags. 29-39 (2017)
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Content-Based Instruction Understood in Terms of Connectionism and Constructivism
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In: Lain, Stephanie. (2016). Content-Based Instruction Understood in Terms of Connectionism and Constructivism. L2 Journal, 8(1). doi:10.5070/L28128902. Retrieved from: http://www.escholarship.org/uc/item/2cg5m3vs (2016)
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Connectionism coming of age: legacy and future challenges
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In: ISSN: 1664-1078 ; Frontiers in Psychology, Vol. 5, No 187 (2014) (2014)
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Where do I know that? A distributed multimodal model of semantic knowledge
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In: Undergraduate Honors Theses (2014)
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Simulating the N400 ERP Component as Semantic Network Error: Insights from a Feature-Based Connectionist Attractor Model of Word Meaning
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In: Psychology Publications (2014)
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Abstract:
The N400 ERP component is widely used in research on language and semantic memory. Although the component’s relation to semantic processing is well-established, the computational mechanisms underlying N400 generation are currently unclear (Kutas & Federmeier, 2011). We explored the mechanisms underlying the N400 by examining how a connectionist model’s performance measures covary with N400 amplitudes. We simulated seven N400 effects obtained in human empirical research. Network error was consistently in the same direction as N400 amplitudes, namely larger for low frequency words, larger for words with many features, larger for words with many orthographic neighbors, and smaller for semantically related target words as well as repeated words. Furthermore, the repetition-induced decrease was stronger for low frequency words, and for words with many semantic features. In contrast, semantic activation corresponded less well with the N400. Our results suggest an interesting relation between N400 amplitudes and semantic network error. In psychological terms, error values in connectionist models have been conceptualized as implicit prediction error, and we interpret our results as support for the idea that N400 amplitudes reflect implicit prediction error in semantic memory (McClelland, 1994).
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Keyword:
connectionism; ERP; N400; network model; prediction error; Psychology; semantic system
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URL: https://ir.lib.uwo.ca/psychologypub/126
https://www.sciencedirect.com/science/article/pii/S0010027714000493?via%3Dihub#!
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