df_jaeger14 {pangoling}R Documentation

Self-Paced Reading Dataset on Chinese Relative Clauses

Description

This dataset contains data from a self-paced reading experiment on Chinese relative clause comprehension. It is structured to support analysis of reaction times, comprehension accuracy, and surprisal values across various experimental conditions in a 2x2 fully crossed factorial design:

Usage

data(df_jaeger14)

Format

A tibble with 8,624 rows and 15 variables:

subject

Participant identifier, a character vector.

item

Trial item number, an integer.

cond

Experimental condition, a character vector indicating variations in sentence structure (e.g., "a", "b", "c", "d").

word

Chinese word presented in each trial, a character vector.

wordn

Position of the word within the sentence, an integer.

rt

Reaction time in milliseconds for reading each word, an integer.

region

Sentence region or phrase type (e.g., "hd1", "Det+CL"), a character vector.

question

Comprehension question associated with the trial, a character vector.

accuracy

Binary accuracy score for the comprehension question (1 = correct, 0 = incorrect).

correct_answer

Expected correct answer for the comprehension question, a character vector ("Y" or "N").

question_type

Type of comprehension question, a character vector.

experiment

Name of the experiment, indicating self-paced reading, a character vector.

list

Experimental list number, for counterbalancing item presentation, an integer.

sentence

Full sentence used in the trial with words marked for analysis, a character vector.

surprisal

Model-derived surprisal values for each word, a numeric vector.

Region codes in the dataset (column region):

Notes on reading times (column rt):

Details

Condition labels:

Source

Jäger, L., Chen, Z., Li, Q., Lin, C.-J. C., & Vasishth, S. (2015). The subject-relative advantage in Chinese: Evidence for expectation-based processing. Journal of Memory and Language, 79–80, 97-120. doi:10.1016/j.jml.2014.10.005

See Also

Other datasets: df_sent

Examples

# Basic exploration
head(df_jaeger14)

# Summarize reaction times by region
 library(tidytable)
df_jaeger14 |>
  group_by(region) |>
  summarize(mean_rt = mean(rt, na.rm = TRUE))

[Package pangoling version 1.0.3 Index]