In two experiments, we examine English monolinguals' and Spanish-English bilinguals' ability to predict an upcoming pronoun referent based on the (IC) bias of the verb. In an eye-tracking experiment, the monolingual data show anticipation of the upcoming referent for NP1-bias verbs. For bilinguals, the same effect is found, showing that bilinguals are not slower than monolinguals at processing the information associated with the IC of the verb. In an off-line experiment, both groups showed knowledge of IC bias information for the verbs used in the eye-tracking experiment. Based on the findings of the two experiments, we show that highly proficient bilinguals have similar online and off-line predictions based on IC verb information than monolingual speakers.
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http://dx.doi.org/10.3389/fpsyg.2019.00956 | DOI Listing |
Sci Rep
January 2025
Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
Assessing cognitive load using pupillography frequency features presents a persistent challenge due to the lack of consensus on optimal frequency limits. This study aims to address this challenge by exploring pupillography frequency bands and seeking clarity in defining the most effective ranges for cognitive load assessment. From a controlled experiment involving 21 programmers performing software bug inspection, our study pinpoints the optimal low-frequency (0.
View Article and Find Full Text PDFBehav Res Methods
January 2025
Experimental Psychology, Helmholtz Institute, Utrecht, The Netherlands.
In this article, we discuss operationalizations and examples of experimental design in eye-tracking research. First, we distinguish direct operationalization for entities like saccades, which are closely aligned with their original concepts, and indirect operationalization for concepts not directly measurable, such as attention or mind-wandering. The latter relies on selecting a measurable proxy.
View Article and Find Full Text PDFAtten Percept Psychophys
January 2025
U.S. DEVCOM Army Research Laboratory, Humans in Complex Systems, Aberdeen Proving Ground, MD, USA.
Historically, electrophysiological correlates of scene processing have been studied with experiments using static stimuli presented for discrete timescales where participants maintain a fixed eye position. Gaps remain in generalizing these findings to real-world conditions where eye movements are made to select new visual information and where the environment remains stable but changes with our position and orientation in space, driving dynamic visual stimulation. Co-recording of eye movements and electroencephalography (EEG) is an approach to leverage fixations as time-locking events in the EEG recording under free-viewing conditions to create fixation-related potentials (FRPs), providing a neural snapshot in which to study visual processing under naturalistic conditions.
View Article and Find Full Text PDFBehav Res Methods
January 2025
Department of Linguistics, University of Massachusetts, 650 North Pleasant Street, Amherst, MA, 01003, USA.
Eye tracking has been a popular methodology used to study the visual, cognitive, and linguistic processes underlying word recognition and sentence parsing during reading for several decades. However, the successful use of eye tracking requires researchers to make deliberate choices about how they apply this technique, and there is wide variability across labs and fields with respect to which choices are "standard." We aim to provide an easy-to-reference guideline that can help new researchers with their entrée into eye-tracking-while-reading research.
View Article and Find Full Text PDFSci Data
January 2025
Laboratory of Experimental Psychology, Tomsk State University, Tomsk, Russia.
Eye tracking data are highly promising in revealing novel and valuable evidence on human behavior and decision making. Data descripted in this article were collected in fourteen experiments with SMI eye tracking glasses in individual and social decision making conditions. The dataset is available on Harvard Dataverse and include data of 14 subjects with 4,180 visual behavior metrics summary and 3,744 eye moment records in decision-related areas of attention.
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