Individual differences in reading aloud: a mega-study, item effects, and some models.

Normal individual differences are rarely considered in the modelling of visual word recognition--with item response time effects and neuropsychological disorders being given more emphasis--but such individual differences can inform and test accounts of the processes of reading. We thus had 100 participants read aloud words selected to assess theoretically important item response time effects on an individual basis. Using two major models of reading aloud--DRC and CDP+--we estimated numerical parameters to best model each individual's response times to see if this would allow the models to capture the effects, individual differences in them and the correlations among these individual differences. It did not. We therefore created an alternative model, the DRC-FC, which successfully captured more of the correlations among individual differences, by modifying the locus of the frequency effect. Overall, our analyses indicate that (i) even after accounting for individual differences in general speed, several other individual difference in reading remain significant; and (ii) these individual differences provide critical tests of models of reading aloud. The database thus offers a set of important constraints for future modelling of visual word recognition, and is a step towards integrating such models with other knowledge about individual differences in reading.

The unexplained nature of reading.

The effects of properties of words on their reading aloud response times (RTs) are 1 major source of evidence about the reading process. The precision with which such RTs could potentially be predicted by word properties is critical to evaluate our understanding of reading but is often underestimated due to contamination from individual differences. We estimated this precision without such contamination individually for 4 people who each read 2,820 words 50 times each. These estimates were compared to the precision achieved by a 31-variable regression model that outperforms current cognitive models on variance-explained criteria. Most (around 2/3) of the meaningful (non-first-phoneme, non-noise) word-level variance remained unexplained by this model. Considerable empirical and theoretical-computational effort has been expended on this area of psychology, but the high level of systematic variance remaining unexplained suggests doubts regarding contemporary accounts of the details of the mechanisms of reading at the level of the word. Future assessment of models can take advantage of the availability of our precise participant-level database.