Reproducible analysis reports with eye-tracking reading time data
  • D. Palleschi
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  1. Background
  2. 2  Eye-tracking during reading
  • Course Intro
  • Background
    • 1  Reproducible Analayses
    • 2  Eye-tracking during reading
    • 3  Working with eye-tracking reading data in R
  • Exploratory Data Analysis
    • 4  Data wrangling
    • 5  Data Visualisation with ggplot2
  • Modelling

Table of contents

  • 3 Eye-tracking
    • 3.1 Eye movements
    • 3.2 The eye-tracker
  • 4 Eye-tracking during reading
    • 4.1 Eye-tracking reading measures
    • 4.2 Region of interest (ROI)
    • 4.3 Measures (dependent variables)
    • 4.4 Independent variables
    • 4.5 What do these measures tell us?

2  Eye-tracking during reading

What can we learn from the measures?

Author
Affiliation

Daniela Palleschi

Humboldt-Universität zu Berlin

Published

July 9, 2023

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3 Eye-tracking

  • in (psycho)linguistics
    • during reading
    • visual world paradigm
  • in psychology
    • pupillometry
    • visual search
  • but also
    • market research
    • diagnostic tool

3.1 Eye movements

  • saccades: eye movements (e.g., from one word to another)
    • average saccade legnth: 7-9 letters (in alphabetic writing systems)
  • fixations: ‘looking at’ something, e.g., a word (little movement)
    • when information is taken in
    • average duration: 225-250ms (ranging 50-600ms)
  • regressions: saccades to earlier text
    • occurance: 10-15% of saccades in skilled readers

(rayner_eye_2009-1?)

3.2 The eye-tracker

  • eye-tracker
    • camera \(+\) infrared illuminator
  • screen
  • chin/head rest
  • in our lab: desk-mounted

Image source: SR Research (all rights reserved)

4 Eye-tracking during reading

4.1 Eye-tracking reading measures

  • inform theories of language processing via linking hypotheses
    • linking visual attention to processing
  • typically, we compare reading times as a function of some manipulation
    • e.g., Sally went/goed to the store.
  • longer reading times are taken to reflect processing costs, associated with e.g., sentence complexity or anomalies

4.2 Region of interest (ROI)

  • can be anything on-screen
    • sentence-level
    • word/region-level
    • a certain part of the screen

Video

4.3 Measures (dependent variables)

  • what we measure = dependent variables (usually…)
    • their value depends on some predictor (e.g., word frequency)
  • measures of duration (time spent on a region)
    • first fixation
    • first-pass reading time
    • regression path duration
    • total reading time
  • data type: continuous
  • measures of revisits
    • number of fixations
    • number of regressions in/out
    • regression in/out (yes or no)
    • probability of regressions in/out (0:1)
  • data type: binary (0,1) or count

4.4 Independent variables

  • what can influence reading measures? (juhasz_lexical_2011?; rayner_linguistic_2011?; warren_influence_2011?; clifton_syntactic_2011?)
    • some examples:
  • Word properties
    • word frequency
    • word length
  • Sentence-level influences
    • context (i.e., prediction)
    • semantic or grammatical manipulations
  • Inter- and intra-individual
    • domain-specific expertise
    • reading skill level

4.5 What do these measures tell us?

  • eye-tracking during reading can tell use when and where processing costs are incurred
  • early measures involve “first contact with a word” or region: first-fixation, first-pass reading time (vasishth_what_2013?)
  • late measures involve regressions to a region: e.g., total reading time
    • may also include ‘spillover’ effects from early processing
  • eye-tracking during reading measures can therefore tell us about stages of processing

References

1  Reproducible Analayses
3  Working with eye-tracking reading data in R
Source Code
---
title: "Eye-tracking during reading"
subtitle: "What can we learn from the measures?"
author: "Daniela Palleschi"
date: "`r Sys.Date()`"
institute: Humboldt-Universität zu Berlin
lang: en
bibliography: references/references.json
csl: references/apa.csl
---

```{r, eval = T, echo = F}
knitr::opts_chunk$set(eval = T, # evaluate = T for REPRODUCIBLE analyses
                      echo = F, # 'print code chunk?'
                      message = F, # print messages?
                      error = T, # render even if errors encountered?
                      warning = F) # print warnings?
```

```{r, eval = T, cache = F, echo = F}
# Create references.json file based on the citations in this script
# make sure you have 'bibliography: references.json' in the YAML
rbbt::bbt_update_bib("_et_reading.qmd")
```

# Eye-tracking

- in (psycho)linguistics
  + during reading
  + visual world paradigm
- in psychology
  + pupillometry
  + visual search
- but also
  + market research
  + diagnostic tool

## Eye movements

- **saccades**: eye *movements* (e.g., from one word to another)
  + average saccade legnth: 7-9 letters (in alphabetic writing systems)
- **fixations**: 'looking at' something, e.g., a word (little movement)
  + when information is taken in
  + average duration: 225-250ms (ranging 50-600ms)
- **regressions**: saccades to earlier text
  + occurance: 10-15% of saccades in skilled readers

::: {.footer}
@rayner_eye_2009-1
:::

## The eye-tracker

:::: {.columns}

::: {.column width="50%"}
- eye-tracker
  + camera $+$ infrared illuminator
- screen
- chin/head rest
- in our lab: desk-mounted

:::

::: {.column width="50%"}
```{r eval = T, fig.env = "figure",out.width="100%", fig.align = "center", set.cap.width=T, fig.cap="Image source: [SR Research](https://www.sr-research.com/about-eye-tracking/) (all rights reserved)"}
knitr::include_graphics(here::here("media/SR_Research.png"))
```

:::

::::

# Eye-tracking during reading

## Eye-tracking reading measures

- inform theories of language processing via linking hypotheses
  + linking visual attention to processing
- typically, we compare reading times as a function of some manipulation
  + e.g., Sally *went*/*goed* to the store.
- longer reading times are taken to reflect processing costs, associated with e.g., sentence complexity or anomalies

## Region of interest (ROI)

- can be anything on-screen
  + sentence-level
  + word/region-level
  + a certain part of the screen
  
::: {.content-visible when-format="revealjs" when-format="html"}
:::: {.columns}

::: {.column width="60%"}
![](media/crit_trial_px03.mp4){fig-align="center" width="100%"}
:::

::::
:::

## Measures (dependent variables)

- what we measure = *dependent* variables (usually...)
  + their value *depends* on some *predictor* (e.g., word frequency)

:::: {.columns}

::: {.column width="50%"}

- measures of duration (time spent on a region)
  + first fixation
  + first-pass reading time
  + regression path duration
  + total reading time
- data type: *continuous*
  
:::
 
::: {.column width="50%"}

- measures of revisits
  + number of fixations
  + number of regressions in/out
  + regression in/out (yes or no)
  + probability of regressions in/out (0:1)
- data type: *binary* (0,1) or *count*
  
:::

::::

## Independent variables

- what can influence reading measures? [@juhasz_lexical_2011; @rayner_linguistic_2011; @warren_influence_2011; @clifton_syntactic_2011]
  + some examples:

:::: {.columns}

::: {.column width="26%"}
- Word properties
  - word frequency
  - word length

:::

::: {.column width="37%"}
- Sentence-level influences
  - context (i.e., prediction)
  - semantic or grammatical manipulations

:::

::: {.column width="37%"}
- Inter- and intra-individual
  - domain-specific expertise
  - reading skill level

:::

::::


## What do these measures tell us?

- eye-tracking during reading can tell use *when* and *where* processing costs are incurred
- *early measures* involve "first contact with a word" or region: first-fixation, first-pass reading time [@vasishth_what_2013, p.126]
- *late measures* involve regressions to a region: e.g., total reading time
  + may also include 'spillover' effects from early processing
- eye-tracking during reading measures can therefore tell us about stages of processing

# References {.unlisted .unnumbered visibility="uncounted"}

::: {#refs custom-style="Bibliography"}
:::