PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus

Kernekoncepter

The author presents the PoTeC corpus, a naturalistic eye-tracking-while-reading dataset with a unique design to analyze reading strategies in experts and novices. The approach allows for comprehensive studies in various fields.

Resumé

The PoTeC corpus contains data from 75 participants reading 12 scientific texts, offering insights into expert and non-expert reading strategies. It includes annotations for linguistic features and aims to facilitate diverse research studies.
The content discusses the importance of naturalistic reading corpora compared to controlled experiments, highlighting the benefits of studying language processing in ecologically valid settings. It emphasizes the value of exploring complex phenomena in naturally occurring text for theoretical relevance.
Furthermore, it explores how eye-tracking data can be leveraged for Natural Language Processing tasks and computational language models. The article also introduces a new standard for data publication following FAIR principles to enhance transparency and reusability.
Overall, the PoTeC corpus provides a valuable resource for studying cognitive processes involved in everyday reading across different disciplines and levels of expertise.

Statistik

PoTeC contains data from 75 participants reading 12 scientific texts.
The corpus includes annotations for linguistic features at different levels.
Surprisal values were added to all words in the texts obtained from different language models.

Citater

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PoTeC

by Debo... kl. arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00506.pdf

Dybere Forespørgsler

How does the PoTeC corpus contribute to advancing research in eye-tracking studies?

The PoTeC corpus contributes significantly to advancing research in eye-tracking studies by providing a naturalistic dataset that includes data from participants reading scientific texts. This corpus is unique as it contains eye movements from both domain experts and novices, allowing for a within-participant manipulation based on factors like level of study and discipline. By incorporating a 2x2x2 fully-crossed factorial design, PoTeC enables researchers to analyze reading strategies used by experts and non-experts across different text domains.
Furthermore, the availability of comprehensive linguistic annotations at different levels provides valuable insights into various linguistic features during reading. The inclusion of text comprehension questions and background knowledge assessments adds depth to the dataset, making it suitable for a wide range of studies beyond traditional eye-tracking experiments.
Overall, PoTeC enhances the understanding of cognitive processes involved in language comprehension through naturalistic stimuli, paving the way for more ecologically valid research in psycholinguistics and related fields.

What are the potential implications of using naturalistic reading corpora in various research fields?

Using naturalistic reading corpora can have several implications across different research fields.

Linguistics: Naturalistic corpora allow researchers to study language processing in ecologically valid settings using real-world stimuli. This approach helps capture a broader range of linguistic phenomena present in everyday reading contexts compared to controlled experimental designs with minimal pairs.

Psychology: Naturalistic corpora provide insights into cognitive processes involved in language comprehension under more realistic conditions. Researchers can explore how individuals process information while engaging with authentic texts, leading to a better understanding of human cognition.

Computer Science/NLP: Naturalistic datasets offer opportunities for improving computational models by leveraging human gaze data obtained during real-world reading tasks. These datasets can enhance language models' cognitive plausibility and performance by incorporating insights from how humans process text naturally.

Education: Studying naturalistic reading behaviors can inform educational practices by identifying effective strategies for enhancing literacy skills among learners based on how they interact with authentic textual materials.

How can the findings from the PoTeC corpus be applied to improve computational language models?

The findings from the PoTeC corpus can be applied to improve computational language models in several ways:

Enhancing Cognitive Plausibility: By analyzing human gaze data collected during naturalistic reading tasks, researchers can validate or refine existing computational models' cognitive plausibility.

Training Language Models: Incorporating insights from expert and novice readers' strategies identified in PoTeC could help train machine learning algorithms for better text processing capabilities.

3Improving Text Comprehension Algorithms: Understanding how individuals comprehend complex scientific texts could lead to advancements in developing algorithms that mimic human-like comprehension abilities.
4Optimizing NLP Tasks: Leveraging gaze behavior data from PoTeC may optimize various NLP tasks such as sentiment analysis or part-of-speech tagging by aligning model predictions with actual human attention patterns during text processing.