Neuroscience

A Python Toolbox for Representational Similarity Analysis

1. Jasper JF van den Bosch
2. Tal Golan
3. Benjamin Peters
4. JohnMark Taylor
5. Mahdiyar Shahbazi
6. Baihan Lin
7. Ian Charest
8. Jörn Diedrichsen
9. Nikolaus Kriegeskorte
10. Marieke Mur
11. Heiko H Schütt

• Curated by eLife

  eLife Assessment

  This study presents a new toolbox for Representational Similarity Analysis, representing a valuable contribution to the neuroscience community. The authors offer a well-integrated platform that brings together a range of state-of-the-art methodological advances within a convincing framework, with strong potential to enable more rigorous and insightful analyses of neural data across multiple subfields.

Reviewed by eLife

Drosophila learn to prefer immobile spherical objects through repeated physical interaction

1. Kenichi Iwasaki
2. Sena Kawano
3. Ashnu Cassod
4. Charles Neuhauser
5. Aleksandr Rayshubskiy

Reviewed by PREreview

The resource elasticity of control

1. Levi Solomyak
2. Aviv Emanuel
3. Eran Eldar

• Curated by eLife

  eLife Assessment

  This study makes the valuable claim that people track, specifically, the elasticity of control (that is, the degree to which outcome depends on how many resources - such as money - are invested), and that control elasticity is impaired in certain types of psychopathologies. A novel task is introduced that provides solid evidence that this learning process occurs and that human behavior is sensitive to changes in the elasticity of control. Evidence that elasticity inference is distinct from more general learning mechanisms and is related to psychopathology remains incomplete.

Reviewed by eLife