<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="editorial" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.5757.2</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Editorial</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                    <subj-group>
                        <subject>Cognitive Neuroscience</subject>
                    </subj-group>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Active learning and decision making: an introduction to the collection</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 2; peer review: not peer reviewed]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Gottlieb</surname>
                        <given-names>Jacqueline</given-names>
                    </name>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Lopes</surname>
                        <given-names>Manuel</given-names>
                    </name>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Oudeyer</surname>
                        <given-names>Pierre-Yves</given-names>
                    </name>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Department of Neuroscience, The Kavli Institute for Brain Science, Columbia University, New York, NY, USA</aff>
                <aff id="a2">
                    <label>2</label>INRIA, Bordeaux Sud-Ouest, France</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:jg2141@columbia.edu">jg2141@columbia.edu</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>15</day>
                <month>1</month>
                <year>2015</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2014</year>
            </pub-date>
            <volume>3</volume>
            <elocation-id>276</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>8</day>
                    <month>1</month>
                    <year>2015</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2015 Gottlieb J et al.</copyright-statement>
                <copyright-year>2015</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/3-276/pdf"/>
            <abstract>
                <p>The importance of exploratory behaviors by which agents actively sample information has been long appreciated in a wide range of disciplines ranging from machine and robot learning to neuroscience and psychology. Given the complexity of these behaviors, progress in understanding them will require a confluence of ideas from these multiple fields. This collection of articles in 
                    <italic toggle="yes">F1000Research</italic> aims to provide a home for a broad range of studies addressing this topic, including full length research articles, brief communications, single figure studies, and review/opinion articles, and studies using computational, behavioral or neural approaches. Here, we provide an introduction to the collection which we hope will grow and become a valuable resource for the researchers exploring this topic.</p>
            </abstract>
            <funding-group>
                <funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement>
            </funding-group>
        </article-meta>
        <notes>
            <sec sec-type="version-changes">
                <label>Updated</label>
                <title>Changes from Version 1</title>
                <p>The scope of this call has been expanded to include work on curiosity and intrinsic motivation and place stronger emphasis on computational/robotics approaches to these questions.</p>
            </sec>
        </notes>
    </front>
    <body>
        <sec>
            <title>Editorial</title>
            <p>Most of our decisions are made under uncertainty, and many of our actions are geared toward reducing this uncertainty. Information seeking actions take many shapes and forms that span the gamut of cognitive function. At one end of the range are simple orienting acts whereby we use our sensory receptors to sample task-relevant information &#x2013; such as 
                <italic toggle="yes">looking</italic> at a relevant stimulus or 
                <italic toggle="yes">listening</italic> for a relevant sound. At the other end are elaborate behaviors such as scientific research, which systematically search for information over extended time scales. And at an intermediate level there are exploration/exploitation tradeoffs, whereby we may temporarily forego a valuable action in order to learn about more uncertain but potentially more lucrative paths.</p>
            <p>Understanding how the brain regulates its information seeking behaviors is significant from both basic science and applied perspectives. It is important for understanding attention, which is a crucial information selection mechanism and is implicated in a range of psychiatric disorders
                <sup>
                    <xref ref-type="bibr" rid="ref-1">1</xref>
                </sup>, for understanding the active control of learning and memory &#x2013; how a neural system selects and organizes its own learning experiences
                <sup>
                    <xref ref-type="bibr" rid="ref-4">4</xref>
                </sup> and determines which ones will leave a lasting trace
                <sup>
                    <xref ref-type="bibr" rid="ref-2">2</xref>
                </sup>, and for understanding curiosity and embodied exploration during development and adulthood
                <sup>
                    <xref ref-type="bibr" rid="ref-3">3</xref>,
                    <xref ref-type="bibr" rid="ref-6">6</xref>
                </sup>. From a practical standpoint, such an understanding can spur improvements in education and computational methods for guiding efficient robotic exploration
                <sup>
                    <xref ref-type="bibr" rid="ref-6">6</xref>,
                    <xref ref-type="bibr" rid="ref-7">7</xref>
                </sup>.</p>
            <p>Addressing these questions requires us to tackle a number of difficult questions
                <sup>
                    <xref ref-type="bibr" rid="ref-4">4</xref>
                </sup>. These questions include how rewards and information seeking shape cognitive mechanisms of learning, memory and attention
                <sup>
                    <xref ref-type="bibr" rid="ref-5">5</xref>
                </sup>, how information seeking shapes exploratory behavior in situated and embodied organisms
                <sup>
                    <xref ref-type="bibr" rid="ref-6">6</xref>
                </sup>, how subjects build explanatory models of their environment and use these models to constrain the sampling of additional information, how the brain generates the intrinsic motivation to seek information when physical rewards are absent or unknown, and how intrinsic and extrinsic rewards interact in driving behavior. The goal of this collection is to provide a home for papers on these and related topics, with the goal of spurring interest and debate in this research field.</p>
        </sec>
    </body>
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