Abstract

Reaching is one of the central experimental paradigms in the field of motor control, and many computational models of reaching have been published. While most of these models try to explain subject data (such as movement kinematics, reaching performance, forces, etc.) from only a single experiment, distinct experiments often share experimental conditions and record similar kinematics. This suggests that reaching models could be applied to (and falsified by) multiple experiments. However, using multiple datasets is difficult because experimental data formats vary widely. Standardizing data formats promises to enable scientists to test model predictions against many experiments and to compare experimental results across labs. Here we report on the development of a new resource available to scientists: a database of reaching called the Database for Reaching Experiments And Models (DREAM). DREAM collects both experimental datasets and models and facilitates their comparison by standardizing formats. The DREAM project promises to be useful for experimentalists who want to understand how their data relates to models, for modelers who want to test their theories, and for educators who want to help students better understand reaching experiments, models, and data analysis.

Highlights

  • Reaching is one of the popular paradigms used to study movement

  • Using Database for Reaching Experiments And Models (DREAM) Tools to look at data One of the tools in DREAM allows a user to plot the preferred direction of neurons

  • Data from two such publications have been included in DREAM: [15] and [16], with DREAM IDs of ‘‘Flint_2012_e1’’ and ‘‘Stevenson_2011_e1’’ respectively. (The ‘e1’ at the end means the data is the first experiment associated with the publication, as defined on the download page of the dataset.)

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Summary

Introduction

Reaching is one of the popular paradigms used to study movement. Reaching is used to ask psychological, computational, behavioral, and clinical questions. It is used to ask how people determine ownership of their hand [1], how the brain deals with uncertainty [2], how people minimize their variability [3], and how recovery of motor function after stroke can be accelerated [4]. In all these domains, the contributions of reaching experiments are important, both conceptually and practically, as their ramifications extend well beyond the domain of reaching. Sharing of data and models would stimulate productive scientific communication and facilitate new scientific inquiries

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