Correction to "Evaluating categories from experience: The simple averaging heuristic" by Woiczyk and Le Mens (2021).

Reports an error in "Evaluating categories from experience: The simple averaging heuristic" by Thomas K. A. Woiczyk and Gaël Le Mens (, 2021[Oct], Vol 121[4], 747-773).

Frequent winners explain apparent skewness preferences in experience-based decisions.

Do people's attitudes toward the (a)symmetry of an outcome distribution affect their choices? Financial investors seek return distributions with frequent small returns but few large ones, consistent with leading models of choice in economics and finance that assume right-skewed preferences. In contrast, many experiments in which decision-makers learn about choice options through experience find the opposite choice tendency, in favor of left-skewed options. To reconcile these seemingly contradicting findings, the present work investigates the effect of skewness on choices in experience-based decisions.

Uncovering the semantics of concepts using GPT-4.

The ability of recent Large Language Models (LLMs) such as GPT-3.5 and GPT-4 to generate human-like texts suggests that social scientists could use these LLMs to construct measures of semantic similarity that match human judgment. In this article, we provide an empirical test of this intuition.

Social media feedback and extreme opinion expression.

On popular social media platforms such as Twitter, Facebook, Instagram, or Tiktok, the quantitative feedback received by content producers is asymmetric: counts of positive reactions such as 'likes,' or 'retweets,' are easily observed but similar counts of negative reactions are not directly available. We study how this design feature of social media platforms affects the expression of extreme opinions. Using simulations of a learning model, we compare two feedback environments that differ in terms of the availability of negative reaction counts.

Evaluating categories from experience: The simple averaging heuristic.

We analyze how people form evaluative judgments about categories based on their experiences with category members. Prior research suggests that such evaluative judgments depend on some experience average but is unclear about the specific kind of average. We hypothesized that evaluations of categories could be driven either by the simple average of experiences with the category or by the member average (the average of the evaluations of the category members, where the evaluation of a category member is the average of experiences with this particular member).