PatchLSD: Patch Foraging task

Isla Barnard; Mihaela Lyutskanova; Marco Wittmann; Tom Gilbertson

doi:10.5281/zenodo.15006580

PatchLSD: Patch Foraging task

Isla Barnard (Lead / Corresponding author)
, Mihaela Lyutskanova
, Marco Wittmann
, Tom Gilbertson

Research output: Non-textual form › Software

Abstract

A reinforcement learning foraging task developed to elucidate switching behaviour (Leave Stay Decisions) as a function of time linked reward rates. Includes an EEG stream via LSL on device builds.

Intended to be a portable-ish OS/ device agnostic version of this task; to allow diverse teams (clinical, academic) to collect data easily. Builds tested on for webGL, Mac, Windows & Linux (the EEG LSL stream has only been tested on Windows, and as of this release, iOS/android not tested).

Original language	English
Publisher	Zenodo
Media of output	Other
Size	32.3 MB
DOIs	https://doi.org/10.5281/zenodo.15006580
Publication status	Published - 11 Mar 2025

ASJC Scopus subject areas

Neuroscience(all)

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10.5281/zenodo.15006580Licence: Other

Cite this

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title = "PatchLSD: Patch Foraging task",

abstract = "A reinforcement learning foraging task developed to elucidate switching behaviour (Leave Stay Decisions) as a function of time linked reward rates. Includes an EEG stream via LSL on device builds. Intended to be a portable-ish OS/ device agnostic version of this task; to allow diverse teams (clinical, academic) to collect data easily. Builds tested on for webGL, Mac, Windows \& Linux (the EEG LSL stream has only been tested on Windows, and as of this release, iOS/android not tested).",

author = "Isla Barnard and Mihaela Lyutskanova and Marco Wittmann and Tom Gilbertson",

note = "Copyright {\textcopyright} 2025 Isla Barnard. Licensed under the MIT License.",

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AU - Barnard, Isla

AU - Lyutskanova, Mihaela

AU - Wittmann, Marco

AU - Gilbertson, Tom

PY - 2025/3/11

Y1 - 2025/3/11

N2 - A reinforcement learning foraging task developed to elucidate switching behaviour (Leave Stay Decisions) as a function of time linked reward rates. Includes an EEG stream via LSL on device builds. Intended to be a portable-ish OS/ device agnostic version of this task; to allow diverse teams (clinical, academic) to collect data easily. Builds tested on for webGL, Mac, Windows & Linux (the EEG LSL stream has only been tested on Windows, and as of this release, iOS/android not tested).

AB - A reinforcement learning foraging task developed to elucidate switching behaviour (Leave Stay Decisions) as a function of time linked reward rates. Includes an EEG stream via LSL on device builds. Intended to be a portable-ish OS/ device agnostic version of this task; to allow diverse teams (clinical, academic) to collect data easily. Builds tested on for webGL, Mac, Windows & Linux (the EEG LSL stream has only been tested on Windows, and as of this release, iOS/android not tested).

UR - https://github.com/tom-gilbertsons-lab/patchLSD/

UR - https://dx.doi.org/10.5281/zenodo.14977005

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DO - 10.5281/zenodo.15006580

M3 - Software

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PatchLSD: Patch Foraging task

Abstract

ASJC Scopus subject areas

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