THE EFFECTS OF PROCESSING INSTRUCTION AND TRADITIONAL INSTRUCTION ON L2 ONLINE PROCESSING OF THE CAUSATIVE CONSTRUCTION IN FRENCH

2017 ◽  
Vol 40 (2) ◽  
pp. 241-268 ◽  
Author(s):  
Wynne Wong ◽  
Kiwako Ito
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Group Process ◽  
Traditional Instruction ◽  
Movement Patterns ◽  
Movement Pattern ◽  
Processing Instruction ◽  
Online Processing ◽  
Correct Picture ◽  
Causative Construction

AbstractWhile previous research has shown that processing instruction (PI) can more effectively facilitate the acquisition of target structures than traditional drill practice, the processing mechanism of PI has not been adequately examined because most assessment tasks have been offline. Using eye-tracking, this two-experiment study compared changes in processing patterns between two types of training: PI and traditional instruction (TI) on intermediate-level L2 learners’ acquisition of the French causative. Both experiments used a pretraining/posttraining design involving a dichotomous scene selection eye-tracking task to measure eye-movement patterns and accuracy in picture selection while participants processed auditory sentences. Neither group received explicit information (EI) in Experiment 1 while both experimental groups in Experiment 2 received EI before processing sentences. Results of Experiment 1 revealed the PI group had significantly higher accuracy scores than the TI group. A change in eye-movement pattern was also observed after training for the PI group but not for the TI group. In Experiment 2, when both groups received EI, PI subjects were again significantly more accurate than TI subjects, but both groups’ accuracy scores were not reliably higher than subjects in the PI and TI groups in Experiment 1 who did not receive EI. Eye-movement patterns in Experiment 2 showed that both TI and PI started to shift their gaze to the correct picture at the same point as PI subjects did in Experiment 1. This suggests that EI helped the TI group start entertaining the correct picture as a possible response sooner but the EI did not help the PI group process the target structure sooner than the TI group.

The effects of structured input and traditional instruction on the acquisition of the English causative passive forms: An eye-tracking study measuring accuracy in responses and processing patterns

2020 ◽  
pp. 136216882092857
Author(s):  
Alessandro Benati
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Traditional Instruction ◽  
Movement Patterns ◽  
Causative Factor ◽  
Tracking Task ◽  
Processing Instruction ◽  
Structured Input ◽  
Positive Effects ◽  
Online Measurements

The present study explores the effects of structured input and traditional instruction on the acquisition of English causative passive forms using online measurements (eye-tracking). Previous empirical research investigating the effects of processing instruction through offline measurements (sentence and discourse) has overall shown positive results for this pedagogical intervention. Research investigating the main factor responsible for the effectiveness of processing instruction has confirmed that it is the structured input component that is the causative factor for the positive effects of processing instruction. The main questions of this study are: (1) what are the effects of structured input and traditional instruction on accuracy when measured by an eye-tracking picture selection task? (2) would possible difference in accuracy between structured input and traditional instruction be accompanied by changes in eye-movement patterns? To provide answers to the two questions formulated in this study, one eye-tracking study was carried out. Fifty-two adult learners (aged 19–21 years) participated and were assigned to one of two groups: structured input ( n = 26) or traditional instruction ( n = 26). Neither instructional groups received explicit information. A pre and post-training design was adopted and the two groups received two different instructional treatments (structured input vs. traditional instruction). Participants were assessed through a picture selection eye-tracking task to measure accuracy and eye-movement patterns while they were processing auditory sentences. Results of the eye-tracking task indicated that the structured input group achieved significantly higher accuracy scores compared to the group receiving traditional instruction. The main findings from the present study reveal that structured input training causes a change in learners’ eye-movement patterns.

An eye-tracking study on the effects of structured input and traditional instruction on the acquisition of English passive forms

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Alessandro Benati
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Traditional Instruction ◽  
Movement Patterns ◽  
Causative Factor ◽  
Tracking Task ◽  
Processing Instruction ◽  
Structured Input ◽  
Positive Effects ◽  
Online Measurements

The present study explores the effects of structured input and traditional instruction on the acquisition of English passive forms using online measurements (eye-tracking). Previous empirical research investigating the effects of processing instruction through offline measurements (sentence and discourse-level) has overall shown that it is an effective pedagogical intervention. Research investigating the main factor responsible for the effectiveness of processing instruction has confirmed that it is the structured input component that is the causative factor for the positive effects of processing instruction. The two main questions of this study are: (1) What are the effects of structured input and traditional instruction on accuracy when measured by an eye-tracking picture selection task? (2) Would possible differences in accuracy between structured input and traditional instruction be accompanied by changes in eye-movement patterns? To provide answers to the questions formulated in this study, one eye-tracking study was carried out. Sixty-four school-age learners (15–16 years old) participated and were assigned to one of two groups: structured input (n = 32 or traditional instruction (n = 32). Neither instructional group received explicit information. A pre- and post-training design was adopted and the two groups received two different instructional treatments (structured input vs traditional instruction). Participants were assessed through a picture selection eye-tracking task to measure accuracy and eye-movement patterns while they were processing auditory sentences. Results of the eye-tracking task indicated that the structured input group achieved significantly higher accuracy scores compared with  the group receiving traditional instruction. The main findings from the present study reveal that structured input training might cause a change in learners’ eye-movement patterns.

scholarly journals Passivizability of Idioms: Has the Wrong Tree Been Barked Up?

2019 ◽  
Vol 63 (2) ◽  
pp. 404-435 ◽  
Author(s):  
Marianna Kyriacou ◽  
Kathy Conklin ◽  
Dominic Thompson
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Passive Control ◽  
Movement Patterns ◽  
The Other ◽  
Figurative Meaning ◽  
Other Hand ◽  
Figurative Interpretation

A growing number of studies support the partial compositionality of idiomatic phrases, while idioms are thought to vary in their syntactic flexibility. Some idioms, like kick the bucket, have been classified as inflexible and incapable of being passivized without losing their figurative interpretation (i.e., the bucket was kicked ≠ died). Crucially, this has never been substantiated by empirical findings. In the current study, we used eye-tracking to examine whether the passive forms of (flexible and inflexible) idioms retain or lose their figurative meaning. Active and passivized idioms ( he kicked the bucket/the bucket was kicked) and incongruous active and passive control phrases (he kicked the apple/the apple was kicked) were inserted in sentences biasing the figurative meaning of the respective idiom ( die). Active idioms served as a baseline. We hypothesized that if passivized idioms retain their figurative meaning ( the bucket was kicked = died), they should be processed more efficiently than the control phrases, since their figurative meaning would be congruous in the context. If, on the other hand, passivized idioms lose their figurative interpretation ( the bucket was kicked = the pail was kicked), then their meaning should be just as incongruous as that of both control phrases, in which case we would expect no difference in their processing. Eye movement patterns demonstrated a processing advantage for passivized idioms (flexible and inflexible) over control phrases, thus indicating that their figurative meaning was not compromised. These findings challenge classifications of idiom flexibility and highlight the creative nature of language.

scholarly journals Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

2021 ◽  
Author(s):  
Janet H. Hsiao ◽  
Hui Lan ◽  
Yueyuan Zheng ◽  
Antoni B. Chan
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Hidden Markov Models ◽  
Eye Movement ◽  
Markov Models ◽  
Hidden Markov ◽  
Movement Patterns ◽  
Movement Analysis ◽  
Background Information ◽  
Eye Movement Analysis

AbstractThe eye movement analysis with hidden Markov models (EMHMM) method provides quantitative measures of individual differences in eye-movement pattern. However, it is limited to tasks where stimuli have the same feature layout (e.g., faces). Here we proposed to combine EMHMM with the data mining technique co-clustering to discover participant groups with consistent eye-movement patterns across stimuli for tasks involving stimuli with different feature layouts. Through applying this method to eye movements in scene perception, we discovered explorative (switching between the foreground and background information or different regions of interest) and focused (mainly looking at the foreground with less switching) eye-movement patterns among Asian participants. Higher similarity to the explorative pattern predicted better foreground object recognition performance, whereas higher similarity to the focused pattern was associated with better feature integration in the flanker task. These results have important implications for using eye tracking as a window into individual differences in cognitive abilities and styles. Thus, EMHMM with co-clustering provides quantitative assessments on eye-movement patterns across stimuli and tasks. It can be applied to many other real-life visual tasks, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.

scholarly journals Tracking Decision Makers under Uncertainty

2011 ◽  
Vol 3 (4) ◽  
pp. 68-76 ◽  
Author(s):  
Amos Arieli ◽  
Yaniv Ben-Ami ◽  
Ariel Rubinstein
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Expected Utility ◽  
Movement Patterns ◽  
Decision Makers ◽  
Deliberation Process

Eye tracking is used to investigate the procedures that participants employ in choosing between two lotteries. Eye movement patterns in problems where the deliberation process is clearly identified are used to substantiate an interpretation of the results. The data provide little support for the hypothesis that decision makers rely exclusively upon an expected utility type of calculation. Instead eye patterns indicate that decision makers often compare prizes and probabilities separately. This is particularly true when the multiplication of sums and probabilities is laborious to compute. (JEL D81, D87)

scholarly journals Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4686
Author(s):  
Sangbong Yoo ◽  
Seongmin Jeong ◽  
Yun Jang
Keyword(s):  
Machine Learning ◽  
Eye Movement ◽  
Data Visualization ◽  
Movement Patterns ◽  
Movement Pattern ◽  
Eye Tracker ◽  
Gaze Behavior ◽  
Short Period ◽  
Behavior Based ◽  
Visualization Techniques

Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.

Sign in / Sign up

Export Citation Format

Share Document