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We know that reading involves coordination between textual characteristics and visual attention, but research linking eye movements during reading and comprehension assessed after reading is surprisingly limited, especially for reading long connected texts. We tested two competing possibilities: (1) the weak association hypothesis: Links between eye movements and comprehension are weak and short-lived; versus (2) the strong association hypothesis: The two are robustly linked, even after a delay. Using a predictive modeling approach, we trained regression models to predict comprehension scores from global eye movement features, using participant-level cross-validation to ensure that the models generalize across participants. We used data from three studies in which readers (Ns = 104, 130, 147) answered multiple-choice comprehension questions ~30 min after reading a 6,500-word text, or after reading up to eight 1,000-word texts. The models generated accurate predictions of participants' text comprehension scores (correlations between observed and predicted comprehension: 0.384, 0.362, 0.372, ps < 0.001), in line with the strong association hypothesis. We found that making more, but shorter fixations, consistently predicted comprehension across all studies. Furthermore, models trained on one study's data could successfully predict comprehension on the others, suggesting generalizability across studies. Collectively, these findings suggest that there is a robust link between eye movements and subsequent comprehension of a long connected text, thereby connecting theories of low-level eye movements with those of higher order text processing during reading.

We propose that machine-learned computational models (MLCMs), in which the model parameters and perhaps even structure are learned from data, can complement extant approaches to the study of text and discourse. Such models are particularly useful when theoretical understanding is insufficient, when the data are rife with nonlinearities and interactivity, and when researchers aspire to take advantage of "big data." Being fully instantiated computer programs, MLCMs can also be used for autonomous assessment and real-time intervention. We illustrate these ideas in the context of an eye movement-based MLCM of textbase comprehension during reading along connected text. Using a dataset where 104 participants read a 6,500-word text, we trained Random Forests models to predict comprehension scores from six eye movement features. The models were highly accurate (area under the receiver operating characteristic curve = 0.902; r = 0.661), robust, and generalized across participants, suggesting possible use in future studies. We conclude by arguing for an increased role of MLCMs in the future of discourse research.

Reading is one of the most common everyday activities, yet research elucidating how affective influence reading processes and outcomes is sparse with inconsistent results. To investigate this question, we randomly assigned participants (N = 136) to happiness (positive affect), sadness (negative affect), and neutral video-induction conditions prior to engaging in self-paced reading of a long, complex science text. Participants completed assessments targeting multiple levels of comprehension (e.g. recognising factual information, integrating different textual components, and open-ended responses of concepts from memory) after reading and after a week-long delay. Results indicated that the Sadness (vs. Happiness) condition had higher comprehension scores, with the largest effects emerging for assessments targeting deeper levels comprehension immediately after reading. Eye-tracking analyses revealed that such benefits may be partly driven by sustained attentional focus over the 20-minute reading session. We discuss results with respect to theories on affect, cognition, and text comprehension. [ABSTRACT FROM AUTHOR]

We propose that machine-learned computational models (MLCMs), in which the model parameters and perhaps even structure are learned from data, can complement extant approaches to the study of text and discourse. Such models are particularly useful when theoretical understanding is insufficient, when the data are rife with nonlinearities and interactivity, and when researchers aspire to take advantage of "big data." Being fully instantiated computer programs, MLCMs can also be used for autonomous assessment and real-time intervention. We illustrate these ideas in the context of an eye movement–based MLCM of textbase comprehension during reading along connected text. Using a dataset where 104 participants read a 6,500-word text, we trained Random Forests models to predict comprehension scores from six eye movement features. The models were highly accurate (area under the receiver operating characteristic curve =.902; r =.661), robust, and generalized across participants, suggesting possible use in future studies. We conclude by arguing for an increased role of MLCMs in the future of discourse research. [ABSTRACT FROM AUTHOR]

Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)—computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use. [ABSTRACT FROM AUTHOR]

In this series of behavioural and electroencephalography (EEG) experiments, we investigate the extent to which repeating patterns of sounds capture attention. Work in the visual domain has revealed attentional capture by statistically predictable stimuli, consistent with predictive coding accounts which suggest that attention is drawn to sensory regularities. Here, stimuli comprised rapid sequences of tone pips, arranged in regular (REG) or random (RAND) patterns. EEG data demonstrate that the brain rapidly recognizes predictable patterns manifested as a rapid increase in responses to REG relative to RAND sequences. This increase is reminiscent of the increase in gain on neural responses to attended stimuli often seen in the neuroimaging literature, and thus consistent with the hypothesis that predictable sequences draw attention. To study potential attentional capture by auditory regularities, we used REG and RAND sequences in two different behavioural tasks designed to reveal effects of attentional capture by regularity. Overall, the pattern of results suggests that regularity does not capture attention.This article is part of the themed issue 'Auditory and visual scene analysis'., (© 2017 The Authors.)