Working memory (WM) is a cognitive system allowing short-term maintenance and processing of information. Maintaining information in WM consists, classically, in rehearsing or refreshing it. Chunking could also be considered as a maintenance mechanism. However, in the literature, it is more often used to explain performance than explicitly investigated within WM paradigms. Hence, the aim of the present paper was (1) to strengthen the experimental dialogue between WM and chunking, by studying the effect of acronyms in a computer-paced complex span task paradigm and (2) to formalize explicitly this dialogue within a computational model. Young adults performed a WM complex span task in which they had to maintain series of 7 letters for further recall while performing a concurrent location judgment task. The series to be remembered were either random strings of letters or strings containing a 3-letter acronym that appeared in position 1, 3, or 5 in the series. Together, the data and simulations provide a better understanding of the maintenance mechanisms taking place in WM and its interplay with long-term memory. Indeed, the behavioral WM performance lends evidence to the functional characteristics of chunking that seems to be, especially in a WM complex span task, an attentional time-based mechanism that certainly enhances WM performance but also competes with other processes at hand in WM. Computational simulations support and delineate such a conception by showing that searching for a chunk in long-term memory involves attentionally demanding subprocesses that essentially take place during the encoding phases of the task.

International audienceShort-term memorization of items while performing a concurrent distracting task requires maintenance processes. The time-based resource-sharing model of working memory (Barrouillet et al. in Psychol Rev 118:175–192, 2011) and its computational version TBRS* (Oberauer and Lewandowsky in Psychon Bull Rev 18:10–45, 2011) proposed that items are refreshed when attention is not captured by the distracting activity. However, these models are unable to account for human performance on the last items when temporal constraints are substantial. The present study presents an analytic approach and computational simulations showing that the sequentiality of the domain-general attentional refreshing mechanism is responsible for the discrepancy between humans and model. It is suggested that the focus of attention could be flexible. The implementation of a computational model based on this solution provides a much better fit to human data. Outcomes are discussed in reference to contemporary works on the phonological loop as well as in reference to other computational models of short-term memory

One way of maintaining information in working memory is through attentional refreshing, a process that was recently shown to be independent from verbal rehearsal. In the classical working memory complex span task, the usual assumption is that memoranda are refreshed in a cumulative fashion, starting from the first item, going in a forward order until the latest one, and cycling until there is no time to continue the process. However, there is no evidence that refreshing operates in that way. The present study proposes a computational modelling study, which constitutes a powerful method to investigate alternative hypotheses. Different refreshing schedules are investigated within computational implementation of the time-based resource sharing model. Their ability to fit three sets of behavioral data and to reproduce the major time-based resource sharing predictions were evaluated using standard model selection criteria. Besides an already published schedule in which the attentional focus is expanded, it appeared that one schedule, the least-activated-first, outperforms the classical cumulative one. The memory trace refreshed at a given time is the one that is the least activated in working memory at that time. These findings characterized the time course of attentional refreshing in working memory and specified the contribution of refreshing to primacy and recency effects. Moreover, in the light of various fields of cognitive psychology, we propose that such refreshing schedules can operate without a homunculus within a general framework including cognitive control and strategic considerations.

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Previously reported simulations using the E-Z Reader model of eye-movement control suggest that the patterns of eye movements observed with children versus adult readers reflect differences in lexical processing proficiency (Reichle et al., 2013). However, these simulations fail to specify precisely what aspect(s) of lexical processing (e.g., orthographic processing) account for the concurrent changes in eye movements and reading skill. To examine this issue, the E-Z Reader model was first used to simulate the aggregate eye-movement data from 15 adults and 75 children to replicate the finding that gross differences in reading skill can be accounted for by differences in lexical processing proficiency. The model was then used to simulate the eye-movement data of individual children so that the best-fitting lexical-processing parameters could be correlated to measures of orthographic knowledge, phonological-processing skill, sentence comprehension, and general intelligence. These analyses suggest that orthographic knowledge accounts for variance in the eye-movement measures that is observed with between-individual differences in reading skill. The theoretical implications of this conclusion will be discussed in relation to computational models of reading and our understanding of reading skill development.

This article presents Apex, a system that can automatically assess a student essay based on its content. It relies on Latent Semantic Analysis, a tool which represents the meaning of words as vectors in a high-dimensional space. By comparing an essay and the text of a given course on a semantic basis, our system can measure how well the essay matches the text. Various assessments are presented to the student regarding the topic, the outline and the coherence of the essay. Our experiments yield promising results.

Reading on a web page is known to be not linear and people need to make fast decisions about whether they have to stop or not reading. In such context, reading, and decision-making processes are intertwined and this experiment attempts to separate them through electrophysiological patterns provided by the Eye-Fixation-Related Potentials technique (EFRPs). We conducted an experiment in which EFRPs were recorded while participants read blocks of text that were semantically highly related, moderately related, and unrelated to a given goal. Participants had to decide as fast as possible whether the text was related or not to the semantic goal given at a prior stage. Decision making (stopping information search) may occur when the paragraph is highly related to the goal (positive decision) or when it is unrelated to the goal (negative decision). EFRPs were analyzed on and around typical eye fixations: either on words belonging to the goal (target), subjected to a high rate of positive decisions, or on low frequency unrelated words (incongruent), subjected to a high rate of negative decisions. In both cases, we found EFRPs specific patterns (amplitude peaking between 51 to 120 ms after fixation onset) spreading out on the next words following the goal word and the second fixation after an incongruent word, in parietal and occipital areas. We interpreted these results as delayed late components (P3b and N400), reflecting the decision to stop information searching. Indeed, we show a clear spill-over effect showing that the effect on word N spread out on word N + 1 and N + 2.