How Do Adults with Dyslexia Recognize Spoken Words? Evidence from Behavioral and EEG Data

ABSTRACT

Purpose

In adults with dyslexia (DYS), the persistent influence of phonological deficits on spoken language processing has mainly been examined in either perceptual tasks or those tapping complex cognitive operations. Much less attention is devoted to spoken word recognition per se. Our study aimed to fill this gap.

Method

Adults with and without dyslexia (for both groups: N = 30, mean age = 21 years, 50% female, 100% white European) performed an auditory lexical decision task. Performance and ERP were recorded.

Results

Reaction times showed a lexicality effect in both groups although they differed in ERP responses to stimulus lexicality. Skilled readers showed the typical amplitude enhancement for pseudowords compared to words in a late phase of N400 (414-581msec) whereas DYS showed the opposite pattern in an earlier phase of N400 (246-413msec). Both groups showed a stronger negativity during pseudowords processing in the late post-lexical stage (582-800msec).

Conclusions

ERP data showed subtle differences between the two populations during the lexical stage of word recognition despite their comparable behavioral outcomes. We hypothesized that a stronger reliance on intact semantic knowledge might contribute to the general enhanced and sustained ERP responses to words in DYS across different phases of lexical processing, although confirmation is needed.

Keywords:

• Adults with dyslexia
• spoken word recognition
• auditory lexical decision
• EEG
• lexicality effect
• N400

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10888438.2023.2218503.

Notes

1. The same sample of participants was included in (Denis-Noël et al., 2020).

2. The Alouette test is a 265-words text composed of meaningless but grammatically and syntactically correct sentences. The final score is an efficiency score considering both accuracy and reading time (Cavalli et al., 2018).

3. The uniqueness point refers to the earliest phoneme position in a word at which the word can be distinguished from all other words in the language. The deviation point refers to the earliest phoneme position from which no real word is compatible with the phonological input. These two values were computed based on the lexique.org database (New et al., 2001).

4. We opted for a high-pass filter with this cutoff frequency (0.4 Hz) as our data suffered from a substantial amount of slow drift due to perspiration; a large proportion of our EEG recording was carried out during the summer months. While a lower cutoff frequency is preferable, reducing the slow drift was necessary to avoid rejecting a large number of participants and also because we wished to carry out Independent Components Analysis (ICA) to correct ocular artifacts and low-frequency drift has been found to adversely affect the performance of ICA (Winkler et al., 2015).

5. The idea behind microstate analysis is that the ongoing EEG is composed of a limited number of prototypical EEG topographies that remain stable for a duration between 80 ms and 120 ms (Lehmann et al., 1987) and these discrete, quasi-stable spatial configurations are known as microstates.

6. The temporal electrodes are not included in the analyses since they are not part of the regions that show strong neural responses in Figure 1

Additional information

Funding

This work was supported by the French Ministry of Research: ANR- 13-JSH2-0002 (to C.P.), ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI) and the Excellence Initiative of Aix-Marseille University (A*MIDEX).