Using Monte-Carlo simulation to test predictions about the time-course of semantic and lexical access in reading

Abstract

One of the main theoretical distinctions between reading models is how and when they predict semantic processing occurs. Some models assume semantic activation occurs after word-form is retrieved. Other models assume there is no-word form, and that what people think of as word-form is actually just semantics. These models thus predict semantic effects should occur early in reading. Results showing words with inconsistent spelling-sound correspondences are faster to read aloud if they are imageable/concrete compared to if they are abstract have been used as evidence supporting this prediction, although null-effects have also been reported. To investigate this, I used Monte-Carlo simulation to create a large set of simulated experiments from RTs taken from different databases. The results showed significant main effects of concreteness and spelling-sound consistency, as well as age-of-acquisition, a variable that can potentially confound the results. Alternatively, simulations showing a significant interaction between spelling-sound consistency and concreteness did not occur above chance, even without controlling for age-of-acquisition. These results support models that use lexical form. In addition, they suggest significant interactions from previous experiments may have occurred due to idiosyncratic items affecting the results and random noise causing the occasional statistical error.

Fig 1

The CDP++ model (left) and the Triangle model (right).

Fig 2. Summary of the way the words are chosen in the different experiments.

Fig 3

RTs and flipped-p values from the simulations of the Spelling-sound Consistency by Concreteness ANOVA for the simulations with AOA matched stimuli (top) and without AOA matched stimuli (bottom). The blue dots are results from the individual simulations. With the interaction term, positive values on the Y axis represent one minus the p value (1 –p). Thus, the closer to 1 the smaller the p value. The negative values represent represent–(1 –p). Thus, the closer to -1 the smaller the p value. The dots above and below the dotted blue line are significant at p < .05. The whiskers in both panels represent 1.5 +/- the interquartile range or the maximum/minimum value in the graph. Note: LDT = Lexical Decision.

Fig 4. Number of significant results from 1000 simulations that produced a significant concreteness × consistency interaction or main effect of consistency or concreteness as a function of response type (reaction times and error rates), database used, and which model generated the results.

The orange line is the criterion for overall significance (i.e., 150 individual significant simulations).