Search for: All records

What can eye movements reveal about reading, a complex skill ubiquitous in everyday life? Research suggests that gaze can measure short-term comprehension for facts, but it is unknown whether it can measure long-term, deep comprehension. We tracked gaze while 147 participants read long, connected, in-formative texts and completed assessments of rote (factual) and inference (connecting ideas) comprehension while reading a text, after reading a text, after reading five texts, and after a seven-day delay. Gaze-based student-independent computa-tional models predicted both immediate and long-term rote and inference comprehension with moderate accuracies. Surprising-ly, the models were most accurate for comprehension assessed after reading all texts and predicted comprehension even after a week-long delay. This shows that eye movements can provide a lens into the cognitive processes underlying reading compre-hension, including inference formation, and the consolidation of information into long-term memory, which has implications for intelligent student interfaces that can automatically detect and repair comprehension in real-time. 

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.

 

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: (a) the weak association hypothesis: Links between eye movements and comprehension are weak and short‐lived, versus (b) 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 < .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.