Month: January 2023

The Bias–Variance Tradeoff in Cognitive Science

Abstract The bias–variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias–variance tradeoff is a general concept that can be applied to human cognition as well, and […]

Published on January 19, 2023

Readers and Tweeters Diagnose Greed and Chronic Pain Within US Health Care System

Letters to the Editor is a periodic feature. We welcome all comments and will publish a selection. We edit for length and clarity and require full names. U.S. Health Care Is Harmful to One’s Health Thank you for publishing this research (“Hundreds of Hospitals Sue Patients or Threaten Their Credit, a KHN Investigation Finds. Does Yours?” Dec. 21). […]

Published on January 19, 2023

Hierarchical Learning in Binary Sequences through Fibonacci Grammar

Imagine you’re trying to solve a puzzle. You’re given a sequence of codes, like a secret message, but the catch is that the codes are organized in a hierarchical structure. In this study, researchers wanted to see if people could learn and anticipate the patterns in these sequences, even with minimal information. They used a […]

Published on January 19, 2023