In the current study we used the gaze-contingent moving window paradigm to directly compare the second language (L2) English perceptual span of two groups that speak languages with essentially the same lexicon and grammar but crucially with different writing directions (and scripts): Hindi (read left to right) and Urdu (read right to left). This is the first study to directly compare first language (L1) speakers of languages that differ primarily in reading direction in a common L2, English. While Urdu speakers had a slightly faster reading rate, we found no additional differences between Hindi and Urdu speakers when reading L2 English; both groups showed a perceptual span between 9 and 11 characters to the right of the fixation based on saccade length. This suggests little to no influence of L1 reading direction on L2 perceptual span, but rather that L2 perceptual span is influenced by allocation of attention during reading. Our data are in line with research by Leung et al. (2014) finding that L2 speakers have a smaller perceptual span than native speakers (L1 perceptual span is approximately 15 characters to the right of the fixation). This most likely stems from the increased demands associated with reading in a second language, which led to a reduction in the amount of attention that can be allocated outside of the current fixation.

Research has shown that suprasegmental cues in conjunction with visual context can lead to anticipatory (or predictive) eye movements. However, the impact of speech rate on anticipatory eye movements has received little empirical attention. The purpose of the current study was twofold. From a methodological perspective, we tested the impact of speech rate on anticipatory eye movements by systemically varying speech rate (3.5, 4.5, 5.5, and 6.0 syllables per second) in the processing of filler-gap dependencies. From a theoretical perspective, we examined two groups thought to show fewer anticipatory eye movements, and thus likely to be more impacted by speech rate. Experiment 1 compared anticipatory eye movements across the lifespan with younger (18–24 years old) and older adults (40–75 years old). Experiment 2 compared L1 speakers of English and L2 speakers of English with an L1 of German. Results showed that all groups made anticipatory eye movements. However, L2 speakers only made anticipatory eye movements at 3.5 syllables per second, older adults at 3.5 and 4.5 syllables per second, and younger adults at speech rates up to 5.5 syllables per second. At the fastest speech rate, all groups showed a marked decrease in anticipatory eye movements. This work highlights (1) the importance of speech rate on anticipatory eye movements, and (2) group-level performance differences in filler-gap prediction.

According to the Shallow Structure Hypothesis (SSH), second language (L2) speakers, unlike native speakers, build shallow syntactic representations during sentence processing. In order to test the SSH, this study investigated the processing of a syntactic movement in both native speakers of English and proficient late L2 speakers of English using pupillometry to measure processing cost. Of particular interest were constructions where movement resulted in an intermediate gap between clauses. Pupil diameter was recorded during auditory presentation of complex syntactic constructions. Two factors were manipulated: syntactic movement (such that some conditions contained movement while others did not), as well as syntactic movement type (either causing an intermediate gap or not). Grammaticality judgments revealed no differences between the two groups, suggesting both were capable of comprehending these constructions. Pupil change slope measurements revealed a potential sensitivity to intermediate gaps for only native speakers, however, both native and late L2 speakers showed similar facilitation during processing of the second gap site. Acoustic analysis revealed potential acoustic cues that may have facilitated the processing of these constructions. This suggests that, contrary to the predictions of the SSH, late L2 speakers are capable of constructing rich syntactic representations during the processing of intermediate gap constructions in spoken language.

While there is strong evidence that younger adults use contextual information to generate semantic predictions, findings from older adults are less clear. Age affects cognition in a variety of different ways that may impact prediction mechanisms; while the efficiency of memory systems and processing speed decrease, life experience leads to complementary increases in vocabulary size, real-world knowledge, and even inhibitory control. Using the visual world paradigm, we tested prediction in younger (n = 30, between 18 and 35 years of age) and older adults (n = 30, between 53 and 78 years of age). Importantly, we differentiated early stage predictions based on simple spreading activation from the more resource-intensive tailoring of predictions when additional constraining information is provided. We found that older adults were slower than younger adults in generating early stage predictions but then quicker than younger adults to tailor those predictions given additional information. This suggests that while age may lead to delays in first activating relevant lexical items when listening to speech, increased linguistic experience nonetheless increases the efficiency with which contextual information is used. These findings are consistent with reports of age having positive as well as negative impacts on cognition and suggest conflation of different stages of prediction as a basis for the inconsistency in the aging-related literature to date. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Research has found that both first language (L1) and second language (L2) speakers make predictions about upcoming linguistic information, with predictive behaviour being impacted by individual differences and methodological factors. However, it is not clear whether a cost is incurred when a prediction is made, but not met. L2 speakers have less experience with their L2 and parsing can be cognitively demanding, which together may lead L2 speakers to incur prediction costs differently relative to L1 speakers. In this study using the visual world paradigm, we test whether L1 and L2 speakers predict in the same way, within the same time frame, and incur the same costs if predictions are not met. We also explore the role of proficiency and speech rate. We found that both groups predict in a similar way and within a similar time frame. In addition, neither group incurred a prediction cost when the target was the most likely alternative, though L2 speakers take longer to shift their attention to the target object when predictions are not met. We argue that this reflects a slowing of lexical access rather than a specific cost of prediction. We only found prediction differences when speech rate was included in the analysis, highlighting the importance of attending to speech rate in studies using the visual world paradigm. Overall, this study supports research showing that both L1 and L2 speakers may make multiple partial predictions about upcoming information rather than predicting one specific lexical candidate while inhibiting less likely lexical candidates.

Reading is an essential method for adults to learn new languages, but difficulty reading texts in a foreign language can increase learners’ anxiety. Identifying text difficulty from the reader’s perspective can aid language learning by tailoring texts to readers’ needs. There is little research focusing on L2 speakers or using a multimodal approach, i.e., using multiple sensors, to detect subjective difficulty. In this study (N = 30) we determined L2 speakers’ subjective difficulty while reading using language proficiency and objective text difficulty, combined with physiological data. We compared machine learning classifiers combining eye, skin and heart sensor data against models using each modality separately. Additionally, we assessed the effect on model performance of shifting the data to account for delayed physiological responses. The models detected 3 levels of subjective difficulty (low, medium, high) and were evaluated using leave-one-participantout (LoPo) and leave-one-document-out (LoDo) cross-validation. The results showed acceptable levels of generalization to new participants (Acc_LoPo = 0.434) and documents (Acc_LoDo = 0.521). Combining sensor data from all modalities improved predictions in both LoDo and LoPo cross-validation, compared to each modality in isolation. Shifting the data to account for physiological response delay did not improve model performance compared to not shifting the data. These findings support refining subjective difficulty detection models and their implementation in adaptive language learning systems. Finally, this work contributes to the field of cognitive science and technology by laying the foundation for innovative approaches to cognitive state detection.