Orthographic neighbours in the spatial coding model

Another way to visualise orthographic neighbourhoods in the model is to create a match plot. To do this, switch to the plot settings tab, and click on the green add button. This will bring up a dialog that asks you to specify a plot name (anything you like) and a plot type. For the latter, click the Browse button and choose the file called match_map.R.

Click Finish on this dialog. The next step is to specify the dataframe (df) that contains the values we want to plot. For this type of plot we want the one labelled ((words default_input_observer 0) default_dataframe):

You can now click the Redraw button. This should result in a plot similar to the one below (yours won't look exactly the same, as the angle of each word node is randomly selected. Try playing with the parameters in the Plot Settings tab to vary the appearance):

This way of plotting the input to word nodes allows us to see how close different words are to the input stimulus. The distance from the centre reflects the dissimilarity between each word node and the input stimulus. In this case, the stimulus is itself a word, and so the corresponding word node is at the centre of the plot. It is important to note that the distance of the points from each other is not meaningful in this type of plot – only their distance from the origin. Here we can see that this input stimulus is a better match to from than it is to neighbours that involve substitution of the first or last letter (fork, worm) or to double substitution neighbours that preserve the exterior letters (film). Note also the presence of addition neighbours like forum and forms. The deletion neighbour for would also be shown on this plot if it were contained in this vocabulary.

The presence of transposition, addition and deletion neighbours in the orthographic neighbourhood clearly differentiates spatial coding model from the interactive activation model. A number of published papers have tested the spatial coding model's predictions regarding these non-standard neighbours (e.g., Davis & Bowers, 2006; Davis, Perea, & Acha, 2009; Lupker et al, in press). These empirical tests have provided evidence against both slot coding and also newer orthographic input coding schemes like open bigram coding.

Next: Simulating TL priming with the spatial coding model