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jIAM: Interactive Activation Models in JavaScript
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jIAM: Interactive Activation Model in JavaScript
Version: 1.91
jIAM was developed by Walter van Heuven
jIAM makes it possible to run simulations with a number of Interactive Activation models in a web browser. Currently, jIAM implements the following models:
Interactive Activation (IA) model (McCelland & Rumelhart, 1981, 1988; Rumelhart & McClelland, 1982). Two versions of the IA model can be created. The default version of the IA model is identical to the original IA model, with feature, letter and word layers. The other version of the IA model has no feature layer, just letter and word layers. The output of both models is similar when the input contains only letters. Unlike the original implementation of the IA model, the model can handle words of any length, although parameters need to be adjusted when the words are not 4 letters long.Bilingual Interactive Activation (BIA) model (Dijkstra & van Heuven, 1998; van Heuven et al., 1998). The BIA model can have two or more lexicons and the feature layer can be omitted.BIA+ model (orthography only and without task/decision system) (Dijkstra & van Heuven, 2002). The BIA+ model can have two or more lexicons and the feature layer can be omitted.The core network engine IANE and the models are written in JavaScript. For the interface I used a combination of JavaScript, HTML5, and Tumult Hype.
jIAM runs in modern web browsers on macOS, Windows, Linux, iOS, and Android. For best preformance use Safari or Firefox.
Please note that the speed of jIAM and the ability to use large and multiple lexicons depends on the capabilities of your device and the JavaScript engine of your browser. Memory available for JavaScript in web browsers is limited and therefore it is unfortunately not possible to use very large lexicons (e.g., more than 20,000 words). To improve the simulation speed when using a batch input file, you can turn off the drawing of activation graphs (see settings).
ReferencesDijkstra, T., & van Heuven, W.J.B. (1998). The BIA-model and bilingual word recognition. In J. Grainger, & A. M. Jacobs (Eds.), Localist connectionist approaches to human cognition (pp. 189-225). Mahwah, NJ: Erlbaum (Scientific psychology series. Edited volumes).
Dijkstra, T., & van Heuven, W.J.B. (2002). The architecture of the bilingual word recognition system: From identification to decision. Bilingualism: Language and Cognition, 5, 175-197.
McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception, Part 1: An account of basic findings. Psychological Review, 88, 375-405.
McClelland, J. L., & Rumelhart, D. E. (1988). Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises. Cambridge, MA: MIT Press.
Rumelhart, D. E. & McClelland, J. L. (1982). An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some test ans extensions of the model. Psychological Review, 89, 60-94.
van Heuven, W.J.B., Dijkstra, T., & Grainger, J. (1998). Orthographic neighborhood effects in bilingual word recognition. Journal of Memory and Language, 39, 458-483.
About jIAM