Walter van Heuven

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Interactive Activation Models in JavaScript (jIAM)

jIAM makes it possible to run in a web browser simulations with a number of Interactive Activation models of visual word recognition. Currently, jIAM contains the following models:

jIAM uses the Interactive Activate Network Engine (IANE) JavaScript library that I have developed to create Interactive Activation models. For the graphical interface, I used a combination of JavaScript, HTML5, and Tumult Hype. jIAM runs in web browsers on macOS, Windows, Linux, iOS, and Android.

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). Running simulations with the IA model of all 1,179 words in word.lis takes about 21 seconds in Safari on an M1 MacBook Pro (macOS 11.5.1). Simulation speed can be further improved by turning off drawing the activation graph (see settings).

Click here to start jIAM

How to run a simulation

To create the standard IA model click first on 'Create Model'. Next, enter a string of 4 characters in the input field, then press the 'Start' button to run the simulation. A graph will be presented after the simulation with the activations of the active nodes. Detailed information about the active nodes and the connections in the network are presented when you click on the 'Nodes' button. A default word recognition threshold is set at 0.70. This can be changed in the settings. To switch to a different model or to adjust parameters and other model settings click on 'Settings'.

Special input characters

'_' Absence and presence (letter feature) units off.
'.' Absence units on and presence units off.
'*' Letter feature units of letters K and R on (ambiguous R-K character).
'#' Mask character; Features of the letters X and O are turned on.

Lexicons and Parameters

The default lexicon for the IA model in jIAM is an English 4-letter word lexicon (word.lis, which is the default lexicon provided with the PDP handbook). However, you can use your own lexicon in jIAM as well. Prepare a text file with two columns separated by a space or tab. The first column should contain words (letter strings) and the second column their word frequencies. When the text file contains resting-level activations use ".lis" or ".rla" in the filename. Words can either have all the same length or they can have different lengths. jIAM will add spaces to words to align all words in the lexicon, just as in the DRC model (Coltheart et al., 2001). Bottom-up letter-to-word excitation and inhibition parameters should be changed when words are longer or shorter than 4 letters, see for example, Grainger and Jacobs (1998) and Loncke et al. (2009) for how these parameters could be adjusted.

A word identification threshold is set at the word level (default value is 0.70) so that the simulation will stop when a word node reaches this threshold. Furthermore, to increase the temporal resolution of the model you can change the integration rate/stepsize in the model from 1.0 to 0.1 as proposed by Davis (2003) and Davis and Lupker (2006).

A detailed description of the IA model can be found in McClelland and Rumelhart (1981), Rumelhart and McClelland (1982), and in the PDP handbook (McClelland & Rumelhart, 1988; Chapter 7).

BIA and BIA+

The lexicons included with jIAM for the BIA model and the BIA+ (dutch4.fpm and english4.fpm) are the same as used in the simulations with BIA model presented in Dijkstra and van Heuven (1998), van Heuven et al. (1998) and Dijkstra et al. (1998). Please note that the BIA+ model in jIAM is only a partly implemented version of BIA+ (no phonology, semantics and task/decision system). The two models in jIAM are basically the same except that BIA+ does not have the option for top-down inhibition from language nodes to words.


Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204–256.

Davis, C. J. (2003). Factors underlying masked priming effects in competitive network models of visual word recognition. In S. Kinoshita & S. J. Lupker (Eds.), Masked priming: The state of the art (pp. 121–170), Hove, England: Psychology Press.

Davis, C.J., & Lupker, S.J. (2006). Masked inhibitory priming in English: Evidence for lexical inhibition. Journal of Experimental Psychology: Human Perception and Performance, 32, 668-687.

Dijkstra, 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.

Dijkstra, T., van Heuven, W.J.B., & Grainger, J. (1998). Simulating cross-language competition with the bilingual interactive activation model. Psychologica Belgica, 38, 177-196.

Grainger, J., & Dijkstra, T. (1992). On the representation and use of language information in bilinguals. In R. J. Harris (Ed.), Cognitive processing in bilinguals (pp. 207-220). Amsterdam: Elsevier.

Grainger, J., & Jacobs, A. M. (1996). Orthographic processing in visual word recognition: A multiple read- out model. Psychological Review, 103, 518–565.

Loncke, M., Martensen, H., van Heuven, W.J.B., & Sandra, D. (2009). Who is dominating the Dutch neighborhood? On the role of subsyllabic units in Dutch nonword reading. The Quarterly Journal of Experimental Psychology, 62, 140-154.

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.