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<pre class="moz-quote-pre" wrap=""><font face="Times New Roman">The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2019) will be held in Montréal, Canada, from July 7-10, 2019.
We invite extended abstracts for contributed poster presentations and oral presentations.
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<font face="Times New
Roman">We welcome submissions of original research related to “learning and decision making over time to achieve a goal”, coming from any discipline or disciplines, describing empirical results from human, animal or animal experiments,
and/or theoretical work, simulations and modeling. Contributions should be aimed at an interdisciplinary audience, but not at the expense of technical excellence. This is an abstract-based meeting, with no published conference proceedings. As such, work that
is intended for, or has been submitted to, other conferences or journals is also welcome, provided that the intent of communication to other disciplines is clear. The abstract deadline is 15 February 2019, 11:59PM PST.<br>
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<font face="Times New
Roman">Submissions should consist of a summary (max 2000 characters; text only), and an extended abstract of between one and four pages (including figures and references). Note: Only the summary will be made available in the (electronic)
abstract booklets. The extended abstract will be used for reviewing, and will be available online only pending on authors’ separate explicit permission. Online availability will have no bearing on the review process and authors are encouraged to include new,
unpublished, findings which they do not want to make publicly available.</font></div>
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<font face="Times New
Roman" color="#000000">For more information and to access the abstract submission system, please visit:
<a moz-do-not-send="true" href="www.rldm.org">www.rldm.org</a></font>
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<pre class="moz-signature" cols="72">--
Ross Otto
Assistant Professor
Department of Psychology
McGill University
<a class="moz-txt-link-freetext" href="http://otto.lab.mcgill.ca/">http://otto.lab.mcgill.ca/</a>
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