Code and data from: Representational geometry explains puzzling error distributions in behavioral tasks

Measuring and interpreting errors in behavioral tasks is critical for understanding cognition. Conventional wisdom assumes that encoding/decoding errors for continuous variables in behavioral tasks should naturally have Gaussian distributions, so that deviations from normality in the empirical data indicate the presence of more complex sources of noise. This line of reasoning has been central for prior research on working memory. Here we re-assess this assumption, and find that even in ideal observer models with Gaussian encoding noise, the error distribution is generally non-Gaussian, contrary to the commonly held belief. Critically, we find that the shape of the error distribution is determined by the geometrical structure of the encoding manifold via a simple rule. In the case of a high-dimensional geometry, the error distributions naturally exhibit flat tails. Using this novel insight, we apply our theory to visual short-term memory tasks, and find that it can account for a large ar..., No new data were collected in this study. The analyses in this study were based on several previously published datasets collected from other labs. For convenience, we have included these published datasets here. Please refer to the original publications for detailed experiment procedures for the data collection. , , # **Code and data from: Representational geometry explains puzzling error distributions in behavioral tasks**

[https://doi.org/10.5061/dryad.hx3ffbgq9](https://doi.org/10.5061/dryad.hx3ffbgq9)

**Description of the data and file structure**

This file contains information for using the MATLAB code associated with the paper entitled “Representational geometry explains puzzling error distributions in behavioral tasks” by Wei & Woodford, PNAS, 2025.

Section 1: Explanations of the Datasets used in this paper

No new experimental data were collected in this study. The analyses in the present study were based on several previously published datasets (from Refs 1,2,3,4,5; see the reference list below) collected in other labs. 

The data in Ref 1  can be downloaded from [https://osf.io/j2h65/?view_only=fdd51dd775a945508c7cbbf25b662692](https://osf.io/j2h65/?view_only=fdd51dd775a945508c7cbbf25b662692)

The data in Refs 2,3,4,5  can also be downloaded from [https://github.com/WeiJiMaLab/del...