Francesca Zermiani

Abstract

Select one of your emails You have multiple emails registered with Frontiers: Notify me on publication Please enter your email address: Email If you already have an account, please login You don't have a Frontiers account ? You can register here Interruptions are often pervasive and require attentional shifts from the primary task. Limited data are available on the factors influencing individuals' efficiency in resuming from interruptions during digital reading. The reported investigation -conducted using the InteRead dataset -examined whether individual differences in visuo-spatial working memory capacity (vsWMC) and prior knowledge could influence resumption lag times during interrupted reading. Participants' vsWMC capacity was assessed using the symmetry span (SSPAN) task, while a pre-test questionnaire targeted their background knowledge about the text. While reading an extract from a Sherlock Holmes story, they were interrupted six times and asked to answer an opinion question. Our analyses revealed that the interaction between vsWMC and prior knowledge significantly predicted the time needed to resume reading following an interruption. The results from our analyses are discussed in relation to theoretical frameworks of task resumption and current research in the field.

Abstract

Eye movements during reading offer a window into cognitive processes and language comprehension, but the scarcity of reading data with interruptions – which learners frequently encounter in their everyday learning environments – hampers advances in the development of intelligent learning technologies. We introduce InteRead – a novel 50-participant dataset of gaze data recorded during self-paced reading of real-world text. InteRead further offers fine-grained annotations of interruptions interspersed throughout the text as well as resumption lags incurred by these interruptions. Interruptions were triggered automatically once readers reached predefined target words. We validate our dataset by reporting interdisciplinary analyses on different measures of gaze behavior. In line with prior research, our analyses show that the interruptions as well as word length and word frequency effects significantly impact eye movements during reading. We also explore individual differences within our dataset, shedding light on the potential for tailored educational solutions. InteRead is accessible from our datasets web-page: https://www.ife.uni-stuttgart.de/en/llis/research/datasets/.

Abstract

Mind wandering (MW) is defined as a shift of attention to task-unrelated internal thoughts that is pervasive and disruptive for learning performance. Current state-of-the-art gaze-based attention-aware intelligent systems are capable of detecting MW from eye movements and delivering interventions to mitigate its negative effects. However, the beneficial functions of MW and its trait-level tendency, defined as the content of MW experience, are still largely neglected by these systems. In this pilot study, we address the questions of whether different MW trait-level tendencies can be detected through off-screen fixations’ frequency and duration and blink rate during a lecture viewing task. We focus on prospective planning and creative problem-solving as two of the main MW trait-level tendencies. Despite the non-significance, the descriptive values show a higher frequency and duration of off-screen fixations, but lower blink rate, in the creative problem-solving MW condition. Interestingly, we do find a highly significant correlation between MW level and engagement scores in the prospective planning MW group. Potential explanations for the observed results are discussed. Overall, these findings represent a preliminary step towards the development of more accurate and adaptive learning technologies, and call for further studies on MW trait-level tendency detection.

Abstract

We address the question of how to account for both forward and backward dependencies in an online processing account of human language acquisition. We focus on descriptive adjectives in English and Italian, and show that the acquisition of adjectives in these languages likely relies on tracking both forward and backward regularities. Our simulations confirm that forward-predicting models like standard Recurrent Neural Networks (RNN) cannot account for this phenomenon due to the lack of backward prediction, but the addition of a small delay (as proposed in Turek et al., 2019) endows the RNN with the ability to not only predict but also retrodict.

Abstract

We address the question of how to account for both forward and backward dependencies in an online processing account of human language acquisition. We focus on descriptive adjectives in English and Italian, and show that the acquisition of adjectives in these languages likely relies on tracking both forward and backward regularities. Our simulations confirm that forward-predicting models like standard Recurrent Neural Networks (RNN) cannot account for this phenomenon due to the lack of backward prediction, but the addition of a small delay (as proposed in Turek et al., 2019) endows the RNN with the ability to not only predict but also retrodict.