Reading in a deep orthography: neuromagnetic evidence for dual-mechanisms

Abstract

Despite substantial efforts to connect cognitive-linguistic models with appropriate anatomical correlates, the question of which cognitive model best accounts for the neuropsychological and functional neuroimaging evidence remains open. The two most popular models are grounded in conceptually different bases and thus make quasi-distinct predictions in regard to the patterns of activation that should be observed in imaging investigations of linguistic processing. Dual-mechanism models propose that high-frequency regular and irregular words are processed through a lexicon-based word code, which facilitates their processing and pronunciation latencies relative to pseudowords. In contrast, single-mechanism models suggest the same behavioral effects can be explained through semantic mediation without the existence of a lexicon. In most previous studies, words and pronounceable pseudowords were presented in lexical-decision or word reading paradigms, and hemodynamic techniques were utilized to distinguish involved anatomical areas. The results typically indicated that both word classes activated largely congruent tissues, with a magnitude advantage for pseudowords in most or all activated regions. However, since the dual-mechanism model predicts both word types utilize the entire linguistic network, but that certain operations are merely obligatorily involved, these results do not sharply refute nor clearly support the model's main tenets. In the current study, we approach the dual- versus single-mechanism question differently by focusing on the temporal dynamics of MEG imaged neuronal activity, during performance of an oddball version of continuous lexical-decision, to determine whether the onset latency of any cortical language region shows effects of word class that are indicative of preferential versus obligatory processing pathways. The most remarkable aspect of our results indicated that both words and pseudowords initially activate the left posterior fusiform region, but that the spatiotemporal dynamics clearly distinguish the two word classes thereafter. For words, this left fusiform activation was followed by engagement of the left posterior inferior temporal, and subsequently activation reached the left posterior superior temporal region. For pseudowords, this sequential order of left temporal area activations was reversed, as activity proceeded from the left fusiform to the left superior temporal and then the left inferior temporal region. For both classes, this dynamic sequential spread manifested within the first 300 ms of stimulus processing. We contend these results provide strong support for the existence of dual-mechanisms underlying reading in a deep orthographic language (i.e., English).

Figure 1

Grand average waveforms for each condition in the four ROIs. The average time course of the noise-normalized current-density estimates for each ROI in the word (solid black line) and pseudoword (dotted gray line) conditions are displayed above. The white circles on the standardized 3D rendition indicate the cortical area corresponding to each activation time course plot. Time is displayed on the abscissa in ms units, whereas the activity estimates are displayed on the ordinate in SD units. In each plot, a reference line marks the p < 0.001 activation threshold for the given ROI. As shown, the sequential order of activated temporal lobe regions was reversed for the two word classes, as words initially activated ITG-Sp and then the STG-Sp region; the opposite was observed to pseudowords. Note that the scale of the ordinate axis differs between ROIs.

Figure 2

Latency periods of maximal contrast between word classes in the FUSIp region. The four latency bins of interest are graphed separately on the abscissa with words represented in black and pseudowords in gray. The estimated marginal means of the activity amplitude estimates are displayed on the ordinate. Error bars depict one standard error of the mean. Significant differences between words and pseudowords within a latency bin are denoted with asterisk(s). * = p < 0.05; ** = p < 0.01

Figure 3

Latency periods of maximal contrast between word classes in the ITG-Sp area. The abscissa and ordinate are the same as Figure 2, and words are again represented in black with pseudowords in gray. Error bars depict one standard error of the mean, and asterisk(s) denote significant differences. * = p < 0.05; ** = p < 0.01

Figure 4

Latency periods of maximal contrast between word classes in the STG-Sp region. Words are shown in black and pseudowords in gray; the abscissa and ordinate are also the same as in Figure 2–Figure 3. Error bars depict one standard error of the mean. Significant differences between the conditions within each time bin are denoted with asterisk(s). * = p < 0.05; ** = p < 0.01

Figure 5

Latency periods of maximal contrast between word classes in IFG cortices. The abscissa and ordinate are the same as Figure 2 – Figure 4, with words represented in black with pseudowords in gray. The error bars depict one standard error of the mean, and asterisk(s) denote significant differences between words and pseudowords within each latency period. * = p < 0.05; ** = p < 0.01