Adaptive Forgetting Curves for Spaced Repetition Language Learning

The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model.Cambridge Assessmen

A co-registration investigation of inter-word spacing and parafoveal preview: Eye movements and fixation-related potentials

Participants’ eye movements (EMs) and EEG signal were simultaneously recorded to examine foveal and parafoveal processing during sentence reading. All the words in the sentence were manipulated for inter-word spacing (intact spaces vs. spaces replaced by a random letter) and parafoveal preview (identical preview vs. random letter string preview). We observed disruption for unspaced text and invalid preview conditions in both EMs and fixation-related potentials (FRPs). Unspaced and invalid preview conditions received longer reading times than spaced and valid preview conditions. In addition, the FRP data showed that unspaced previews disrupted reading in earlier time windows of analysis, compared to string preview conditions. Moreover, the effect of parafoveal preview was greater for spaced relative to unspaced conditions, in both EMs and FRPs. These findings replicate well-established preview effects, provide novel insight into the neural correlates of reading with and without inter-word spacing and suggest that spatial selection precedes lexical processing

The ERP signature of the contextual diversity effect in visual word recognition

Behavioral experiments have revealed that words appearing in many different contexts are responded to faster than words that appear in few contexts. Although this contextual diversity (CD) effect has been found to be stronger than the word-frequency (WF) effect, it is a matter of debate whether the facilitative effects of CD and WF reflect the same underlying mechanisms. The analysis of the electrophysiological correlates of CD may shed some light on this issue. This experiment is the first to examine the ERPs to high- and low-CD words when WF is controlled for. Results revealed that while high-CD words produced faster responses than low-CD words, their ERPs showed larger negativities (225-325 ms) than low-CD words. This result goes in the opposite direction of the ERP WF effect (high-frequency words elicit smaller N400 amplitudes than low-frequency words). The direction and scalp distribution of the CD effect resembled the ERP effects associated with "semantic richness." Thus, while apparently related, CD and WF originate from different sources during the access of lexical-semantic representations.The research reported in this article has been partially funded by Grants PSI2011-26924 (Spanish Ministry of Economy and Competitiveness) and GV/2014/067 (Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana).info:eu-repo/semantics/publishedVersio

A backwards glance at words: Using reversed-interior masked primes to test models of visual word identification

The experiments reported here used "Reversed-Interior" (RI) primes (e.g., cetupmor-COMPUTER) in three different masked priming paradigms in order to test between different models of orthographic coding/visual word recognition. The results of Experiment 1, using a standard masked priming methodology, showed no evidence of priming from RI primes, in contrast to the predictions of the Bayesian Reader and LTRS models. By contrast, Experiment 2, using a sandwich priming methodology, showed significant priming from RI primes, in contrast to the predictions of open bigram models, which predict that there should be no orthographic similarity between these primes and their targets. Similar results were obtained in Experiment 3, using a masked prime same-different task. The results of all three experiments are most consistent with the predictions derived from simulations of the Spatial-coding model