Eye-Tracking Sentence Reading Task
Creators: name: Schonberg, Tom
name: Kobo, Oren
Publisher: name: OSF Registries
Year: 2020

description: We aim to compare the processing of the experimental sentences between subjects with varied levels of tendency to depression (as measured by questionnaires) to probe whether we can predict emotional state based on an accessible, objective measure. 
Any difference may indicate alternate processing strategies or allocation of attention during reading that might causes a different distribution of probabilities to the possible sentence subsequent information. 
Participants will read sentences, one at a time, while collecting Eye-Tracking data. We will use thirty-two sets of four sentences. 
All sentences are constructed such that a specific position in the beginning of the sentence (denoted as the source word) has an interaction with an upcoming negative/positive word (denoted as the target word). 
The nature of this interaction is such that target word is either expected or surprising (but plausible) given the source word. 
Sets are in 2X2 design (Agreement X Sentiment) to split to four conditions with word frequencies controlled using a Hebrew dataset (Linzen, 2009). 
Every subject sees exactly one sentence from each set, and the same number of sentences from each of the four conditions. To prevent structural priming (Bock, 1986), we added 56 fillers that are regular probable sentence taken from previous studies. 
Participants take part in 20-minute long sessions while eye-tracking data are collected throughout the experiment using Eyelink1000 plus, using 1000Hz. 
Afterwards, they will be asked to fill out three questionnaires to measure different aspect of mental conditions: PHQ-9 (depression), state and trait anxiety (STAI; Spielberger 1983) to correlate with the eye-tracking measures collected at the target word during comprehension of the sentences and to validate our implicit measures. 
We will explore the processing differences as reflected in Eye-Tracking data when reading the target word and hypothesize that tendency to depression will interact with reading time and various known eye-tracking related linguistic measures, when processing negative/positive surprise at that point. 
Meaning, we will analyze the gaze pattern when reading the target words to probe whether participants with more depression (i.e. higher PHQ) process negative stimuli differently, while altering predictability of this sentiment given prior context (surprising/expected). 
The measures that will be extracted from the raw Eye-Tracking signal and be used for analysis are: total gaze distance covered, total gaze duration on ROI, first fixation duration, second fixation duration, regression path duration, skipping probability, word has first pass regression, number of fixations, pupil diameter. 
All those measures are with regard to the defined ROI of the specific presented sentences. The ROI are the location of the target and source words, meaning the exact range of pixels on screen on which the words are presented. Additionally, we will: 
1. Explore the inter-relation between these features 2. 
2. Build the following classifiers: Predict sentence type by raw Eye-tracking signal using Deep-Learning model (Bi-LSTM) ; Predict sentence type be extracted aforementioned features (with ML model such as random forest); Predict PHQ level by raw Eye-tracking signal using Deep-Learning model (Bi-LSTM) - Predict PHQ level by extracted aforementioned features (with ML model such as random forest). 
We will perform both within-subject and between-subject evaluation.

To ensure participants will pay attention and be engaged, they will be asked simple Yes/No comprehension questions following reading the sentence (both the experimental and the filler sentences). Participants with lower than predetermined threshold of 75% success ratio in these questions will be disqualified