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dataset | valenced real-world reading and lexical decision

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readAloud-valence Dataset

Project Goal

This dataset provides behavioral results on a naturalistic reading task involving short passages that are heavily valenced (positive/negative) with a surprise, valence "shift" in the middle of each passage, as well as a replication of a valenced lexical decision task (Kousta et al., 2009).

Background & Design

Broadly: how does the processing of emotional inputs impact our behavior? Specifically: in what ways does valence (including known effects in semantic integration and priming) impact performance when reading a text aloud?

Recent research is throwing away the old notion of "emotional" and "non-emotional" experiences and redefining valence and arousal as central to cognition. In this way, a person's mood might be viewed as a "filter" through which they experience the world. When faced with conflicting emotional stimuli, an individual might be required to deploy inhibitory control in order to ignore their own mood to optimally process external stimuli. One would be expected to perform better (faster, more accurately) on any task when they do not have to expend resources on such inhibitory control. Alternatively, there may be a "priming effect" that facilitates reading valenced words that either match one's mood or are congruent with the valence of recently read words. Likewise, switching between ends of the valence spectrum may operate similarly to switching between tasks, with emotional switches serving as stumbling blocks to the smooth execution of a task. Finally, the valence of the text being read may impact the reader's ability to detect and respond to mistakes made while reading. That is, insofar as errors are implicitly negative events, the ability to detect and react to errors while reading aloud may be directly influenced by the valence of the text being read.

Participants completed three online behavioral tasks:

  • reading aloud of valenced passages, coded for speed and accuracy
  • a valenced lexical decision task
  • a dimension change card sort (DCCS) task

In addition, participants answered questions on:

  • detailed demographics
  • mood
  • emotion regulation
  • anxiety
  • depression

Data was collected at Florida International University from January-June 2022. Participants received course credit via the SONA system.

Data Releases

2022-11-22: osf.io/pn2hu

Also, see the associated analysis repo for the initial poster and paper from this dataset.

How to Cite

If you use the data posted to OSF, kindly cite as:

Alexander, J. M., & Buzzell, G. A. (2023). Emotional context and predictability in naturalistic reading aloud. Emotion, 10.1037/emo0001298. Advance online publication.

Contributors

Name Role
Jessica M. Alexander conceptualization, resources, project administration, data curation
Dr. George A. Buzzell supervision
Ana García Morazzani, Ana Lopez-Nuñez, Anfernee Duncombe, Brittney Rodriguez, Laura Gallardo, Lucas Sahar, Maria Rodriguez, Sarah Malykke data coding

Learn more about us here.

Contributing

If you are interested in contributing, please read our CONTRIBUTING.md file.

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