Effects of phonological and orthographic neighbourhood density interact in visual word recognition

The present study investigated the role of phonological and orthographic neighbourhood density in visual word recognition. Three mechanisms were identified that predict distinct facilitatory or inhibitory effects of each variable. The lexical competition account predicts overall inhibitory effects of neighbourhood density. The global activation (familiarity) account predicts overall facilitatory effects of neighbourhood density. Finally, the cross-code consistency account predicts an interaction, with inhibition of phonological neighbours in sparse orthographic regions and facilitation of phonological neighbours in dense orthographic regions. In Experiment 1 (lexical decision), a cross-over interaction was indeed found, supporting the prediction of the cross-code consistency account. In Experiment 2, this cross-over interaction was exaggerated by adding pseudohomo-phone stimuli (e.g., brane) among the nonword targets. Finally, in Experiment 3 (progressive demasking), we tried to shift the balance between inhibitory and facilitatory mechanisms by using a perceptual identification task. As predicted, the inhibitory effects of phonological neighbourhood were amplified, whereas the facilitatory effects disappeared. We conclude that the level of compatibility across co-activated orthographic and phonological representations is a major causal factor underlying this pattern of effects.     

A Computational and Empirical Investigation of Graphemes in Reading

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full‐blown model of multi‐syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading.     

Two routes to perspective: Simulation and rule‐use as approaches to mentalizing

We review evidence relating to children's ability to acknowledge false beliefs within a simulation account according to which our focus is set by default to the world as we know it: hence, our current beliefs assume salience over beliefs that do not fall into this category. The model proposes that the ease with which we imaginatively shift from this default depends on the salience of our current belief, relative to the salience of the belief that is being simulated. However, children do use a rule‐based approach for mentalizing in some contexts, which has the advantage of protecting them from the salience of their own belief. Rule‐based mentalizing judgements might be faster, cognitively easier and less prone to error, relative to simulation‐based judgements that are much influenced by salience. We propose that although simulation is primary, rule‐based approaches develop as a shortcut; we thus grow from individuals capable of using only simulation into individuals capable of both techniques.