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<!DOCTYPE html> <html lang="en" dir="ltr" xmlns="http://www.w3.org/1999/xhtml" xmlns:mml="http://www.w3.org/1998/Math/MathML"> <head prefix="og: http://ogp.me/ns# article: http://ogp.me/ns/article# book: http://ogp.me/ns/book#" >  <link rel="dns-prefetch" href="//d33xdlntwy0kbs.cloudfront.net" /> <link rel="dns-prefetch" href="//www.google.com" /> <link rel="dns-prefetch" href="//scholar.google.com" /> <link rel="dns-prefetch" href="//www.googletagmanager.com" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="shortcut icon" href="https://www.biorxiv.org/sites/default/files/images/favicon.ico" type="image/vnd.microsoft.icon" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <link rel="alternate" type="application/pdf" title="Full Text (PDF)" href="/content/10.1101/626374v2.full.pdf" /> <link rel="alternate" type="text/plain" title="Full Text (Plain)" href="/content/10.1101/626374v2.full.txt" /> <meta name="article_thumbnail" content="https://www.biorxiv.org/content/biorxiv/early/2019/05/22/626374/embed/graphic-2.gif" /> <meta name="type" content="article" /> <meta name="category" content="article" /> <meta name="HW.identifier" content="/biorxiv/early/2019/05/22/626374.atom" /> <meta name="HW.pisa" content="biorxiv;626374v2" /> <meta name="DC.Format" content="text/html" /> <meta name="DC.Language" content="en" /> <meta name="DC.Title" content="Levels of Representation in a Deep Learning Model of Categorization" /> <meta name="DC.Identifier" content="10.1101/626374" /> <meta name="DC.Date" content="2019-05-22" /> <meta name="DC.Publisher" content="Cold Spring Harbor Laboratory" /> <meta name="DC.Rights" content="© 2019, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/" /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="Deep convolutional neural networks (DCNNs) rival humans in object recognition. The layers (or levels of representation) in DCNNs have been successfully aligned with processing stages along the ventral stream for visual processing. Here, we propose a model of concept learning that uses visual representations from these networks to build memory representations of novel categories, which may rely on the medial temporal lobe (MTL) and medial prefrontal cortex (mPFC). Our approach opens up two possibilities: a ) formal investigations can involve photographic stimuli as opposed to stimuli handcrafted and coded by the experimenter; b ) model comparison can determine which level of representation within a DCNN a learner is using during categorization decisions. Pursuing the latter point, DCNNs suggest that the shape bias in children relies on representations at more advanced network layers whereas a learner that relied on lower network layers would display a color bias. These results confirm the role of natural statistics in the shape bias (i.e., shape is predictive of category membership) while highlighting that the type of statistics matter, i.e., those from lower or higher levels of representation. We use the same approach to provide evidence that pigeons performing seemingly sophisticated categorization of complex imagery may in fact be relying on representations that are very low-level (i.e., retinotopic). Although complex features, such as shape, relatively predominate at more advanced network layers, even simple features, such as spatial frequency and orientation, are better represented at the more advanced layers, contrary to a standard hierarchical view." /> <meta name="DC.Contributor" content="Olivia Guest" /> <meta name="DC.Contributor" content="Bradley C. Love" /> <meta name="article:published_time" content="2019-05-22" /> <meta name="article:section" content="New Results" /> <meta name="citation_title" content="Levels of Representation in a Deep Learning Model of Categorization" /> <meta name="citation_abstract" lang="en" content="<h3>Abstract</h3> <p>Deep convolutional neural networks (DCNNs) rival humans in object recognition. The layers (or levels of representation) in DCNNs have been successfully aligned with processing stages along the ventral stream for visual processing. Here, we propose a model of concept learning that uses visual representations from these networks to build memory representations of novel categories, which may rely on the medial temporal lobe (MTL) and medial prefrontal cortex (mPFC). Our approach opens up two possibilities: <i>a</i>) formal investigations can involve photographic stimuli as opposed to stimuli handcrafted and coded by the experimenter; <i>b</i>) model comparison can determine which level of representation within a DCNN a learner is using during categorization decisions. Pursuing the latter point, DCNNs suggest that the shape bias in children relies on representations at more advanced network layers whereas a learner that relied on lower network layers would display a color bias. These results confirm the role of natural statistics in the shape bias (i.e., shape is predictive of category membership) while highlighting that the type of statistics matter, i.e., those from lower or higher levels of representation. We use the same approach to provide evidence that pigeons performing seemingly sophisticated categorization of complex imagery may in fact be relying on representations that are very low-level (i.e., retinotopic). Although complex features, such as shape, relatively predominate at more advanced network layers, even simple features, such as spatial frequency and orientation, are better represented at the more advanced layers, contrary to a standard hierarchical view.</p>" /> <meta name="citation_journal_title" content="bioRxiv" /> <meta name="citation_publisher" content="Cold Spring Harbor Laboratory" /> <meta name="citation_publication_date" content="2019/01/01" /> <meta name="citation_mjid" content="biorxiv;626374v2" /> <meta name="citation_id" content="626374v2" /> <meta name="citation_public_url" content="https://www.biorxiv.org/content/10.1101/626374v2" /> <meta name="citation_abstract_html_url" content="https://www.biorxiv.org/content/10.1101/626374v2.abstract" /> <meta name="citation_full_html_url" content="https://www.biorxiv.org/content/10.1101/626374v2.full" /> <meta name="citation_pdf_url" content="https://www.biorxiv.org/content/biorxiv/early/2019/05/22/626374.full.pdf" /> <meta name="citation_doi" content="10.1101/626374" /> <meta name="citation_num_pages" content="34" /> <meta name="citation_article_type" content="Article" /> <meta name="citation_section" content="New Results" /> <meta name="citation_firstpage" content="626374" /> <meta name="citation_author" content="Olivia Guest" /> <meta name="citation_author_institution" content="Department of Experimental Psychology, University College London" /> <meta name="citation_author" content="Bradley C. 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Proceedings of the National Academy of Sciences. doi: 10.1073/pnas.1403112111" /> <meta name="twitter:title" content="Levels of Representation in a Deep Learning Model of Categorization" /> <meta name="twitter:site" content="@biorxivpreprint" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:image" content="https://www.biorxiv.org/sites/default/files/images/biorxiv_logo_homepage7-5-small.png" /> <meta name="twitter:description" content="Deep convolutional neural networks (DCNNs) rival humans in object recognition. The layers (or levels of representation) in DCNNs have been successfully aligned with processing stages along the ventral stream for visual processing. Here, we propose a model of concept learning that uses visual representations from these networks to build memory representations of novel categories, which may rely on the medial temporal lobe (MTL) and medial prefrontal cortex (mPFC). Our approach opens up two possibilities: a ) formal investigations can involve photographic stimuli as opposed to stimuli handcrafted and coded by the experimenter; b ) model comparison can determine which level of representation within a DCNN a learner is using during categorization decisions. Pursuing the latter point, DCNNs suggest that the shape bias in children relies on representations at more advanced network layers whereas a learner that relied on lower network layers would display a color bias. These results confirm the role of natural statistics in the shape bias (i.e., shape is predictive of category membership) while highlighting that the type of statistics matter, i.e., those from lower or higher levels of representation. We use the same approach to provide evidence that pigeons performing seemingly sophisticated categorization of complex imagery may in fact be relying on representations that are very low-level (i.e., retinotopic). Although complex features, such as shape, relatively predominate at more advanced network layers, even simple features, such as spatial frequency and orientation, are better represented at the more advanced layers, contrary to a standard hierarchical view." /> <meta name="og-title" property="og:title" content="Levels of Representation in a Deep Learning Model of Categorization" /> <meta name="og-url" property="og:url" content="https://www.biorxiv.org/content/10.1101/626374v2" /> <meta name="og-site-name" property="og:site_name" content="bioRxiv" /> <meta name="og-description" property="og:description" content="Deep convolutional neural networks (DCNNs) rival humans in object recognition. The layers (or levels of representation) in DCNNs have been successfully aligned with processing stages along the ventral stream for visual processing. Here, we propose a model of concept learning that uses visual representations from these networks to build memory representations of novel categories, which may rely on the medial temporal lobe (MTL) and medial prefrontal cortex (mPFC). Our approach opens up two possibilities: a ) formal investigations can involve photographic stimuli as opposed to stimuli handcrafted and coded by the experimenter; b ) model comparison can determine which level of representation within a DCNN a learner is using during categorization decisions. Pursuing the latter point, DCNNs suggest that the shape bias in children relies on representations at more advanced network layers whereas a learner that relied on lower network layers would display a color bias. These results confirm the role of natural statistics in the shape bias (i.e., shape is predictive of category membership) while highlighting that the type of statistics matter, i.e., those from lower or higher levels of representation. We use the same approach to provide evidence that pigeons performing seemingly sophisticated categorization of complex imagery may in fact be relying on representations that are very low-level (i.e., retinotopic). Although complex features, such as shape, relatively predominate at more advanced network layers, even simple features, such as spatial frequency and orientation, are better represented at the more advanced layers, contrary to a standard hierarchical view." /> <meta name="og-type" property="og:type" content="article" /> <meta name="og-image" property="og:image" content="https://www.biorxiv.org/sites/default/files/images/biorxiv_logo_homepage7-5-small.png" /> <meta name="citation_date" content="2019-05-22" /> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="/content/10.1101/626374v2.ppt" /> <meta name="description" content="bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution" /> <meta name="generator" content="Drupal 7 (http://drupal.org)" /> <link rel="canonical" href="https://www.biorxiv.org/content/10.1101/626374v2" /> <link rel="shortlink" 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class="highwire-article-citation highwire-citation-type-highwire-article node743427" data-node-nid="743427" id="node-743427--2390659037" data-pisa="biorxiv;626374v2" data-pisa-master="biorxiv;626374" data-apath="/biorxiv/early/2019/05/22/626374.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-biorxiv-article-top clearfix has-author-tooltip" > <span class="biorxiv-article-type"> New Results </span> <h1 class="highwire-cite-title" id="page-title">Levels of Representation in a Deep Learning Model of Categorization</h1> <div class="highwire-cite-authors" ><span class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Olivia</span> <span class="nlm-surname">Guest</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Bradley C.</span> <span class="nlm-surname">Love</span></span></span></div> <div class="highwire-cite-metadata" ><span class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">doi:</span> https://doi.org/10.1101/626374 </span></div> </div> <div id="hw-article-author-popups-node-743427--2390659037" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Olivia Guest </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Experimental Psychology, University College London</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Olivia%2BGuest%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Guest%20O&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AOlivia%2BGuest%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Bradley C. Love </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>Department of Experimental Psychology, University College London, and The Alan Turing Institute</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Bradley%2BC.%2BLove%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Love%20BC&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3ABradley%2BC.%2BLove%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-panel-tabs pane-panels-ajax-tab-tabs" > <div class="pane-content"> <div class="item-list"><ul class="tabs inline panels-ajax-tab"><li class="first"><a href="/content/10.1101/626374v2" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_art" data-target-id="highwire_article_tabs" data-entity-context="node:743427" data-trigger="" data-url-enabled="1">Abstract</a><a href="/panels_ajax_tab/biorxiv_tab_art/node:743427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/626374v2.full-text" class="panels-ajax-tab-tab" data-panel-name="article_tab_full_text" data-target-id="highwire_article_tabs" data-entity-context="node:743427" data-trigger="full-text" data-url-enabled="1">Full Text</a><a href="/panels_ajax_tab/article_tab_full_text/node:743427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/626374v2.article-info" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_info" data-target-id="highwire_article_tabs" data-entity-context="node:743427" data-trigger="article-info" data-url-enabled="1">Info/History</a><a href="/panels_ajax_tab/biorxiv_tab_info/node:743427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/626374v2.article-metrics" class="panels-ajax-tab-tab" data-panel-name="article_tab_metrics" data-target-id="highwire_article_tabs" data-entity-context="node:743427" data-trigger="article-metrics" data-url-enabled="1">Metrics</a><a href="/panels_ajax_tab/article_tab_metrics/node:743427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/626374v2.external-links" class="panels-ajax-tab-tab" data-panel-name="article_tab_data_code" data-target-id="highwire_article_tabs" data-entity-context="node:743427" data-trigger="external-links" data-url-enabled="1">Data/Code</a><a href="/panels_ajax_tab/article_tab_data_code/node:743427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li class="last"><a href="/content/10.1101/626374v2.full.pdf+html" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_pdf" data-target-id="highwire_article_tabs" data-entity-context="node:743427" data-trigger="full.pdf+html" data-url-enabled="1"><i class="icon-file-alt"></i> Preview PDF</a><a href="/panels_ajax_tab/biorxiv_tab_pdf/node:743427/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li></ul></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-panel-tabs-container" > <div class="pane-content"> <div data-panels-ajax-tab-preloaded="biorxiv_tab_art" id="panels-ajax-tab-container-highwire_article_tabs" class="panels-ajax-tab-container"><div class="panels-ajax-tab-loading" style ="display:none"><img class="loading" src="https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif" alt="Loading" title="Loading" /></div><div class="panels-ajax-tab-wrap-biorxiv_tab_art"><div class="panel-display panel-1col clearfix" > <div class="panel-panel panel-col"> <div><div class="panel-pane pane-highwire-markup" > <div class="pane-content"> <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2 class="">Abstract</h2><p id="p-4">Deep convolutional neural networks (DCNNs) rival humans in object recognition. The layers (or levels of representation) in DCNNs have been successfully aligned with processing stages along the ventral stream for visual processing. Here, we propose a model of concept learning that uses visual representations from these networks to build memory representations of novel categories, which may rely on the medial temporal lobe (MTL) and medial prefrontal cortex (mPFC). Our approach opens up two possibilities: <em>a</em>) formal investigations can involve photographic stimuli as opposed to stimuli handcrafted and coded by the experimenter; <em>b</em>) model comparison can determine which level of representation within a DCNN a learner is using during categorization decisions. Pursuing the latter point, DCNNs suggest that the shape bias in children relies on representations at more advanced network layers whereas a learner that relied on lower network layers would display a color bias. These results confirm the role of natural statistics in the shape bias (i.e., shape is predictive of category membership) while highlighting that the type of statistics matter, i.e., those from lower or higher levels of representation. We use the same approach to provide evidence that pigeons performing seemingly sophisticated categorization of complex imagery may in fact be relying on representations that are very low-level (i.e., retinotopic). Although complex features, such as shape, relatively predominate at more advanced network layers, even simple features, such as spatial frequency and orientation, are better represented at the more advanced layers, contrary to a standard hierarchical view.</p></div><div class="section fn-group" id="fn-group-1"><h2>Footnotes</h2><ul><li class="fn-supported-by" id="fn-1"><p id="p-1">This work was supported by NIH (Grant 1P01HD080679), and a Wellcome Trust Investigator Award (Grant WT106931MA) to BCL, as well as The Alan Turing Institute under the EPSRC grant EP/N510129/1. Some of this work was originally reported at the 39th Annual Meeting of the Cognitive Science Society in 2017.</p></li><li class="fn-other" id="fn-2"><p id="p-2">The authors declare that they have no competing interests. The authors would like to thank the members of the Love lab at UCL for their useful input when preparing the manuscript. The code used to run these experiments is available here: <a href="https://osf.io/jxavn/">https://osf.io/jxavn/</a>.</p></li><li class="fn-dataset fn-group-external-links" id="fn-3"><p id="p-5"> <a href="https://osf.io/jxavn/">https://osf.io/jxavn/</a> </p></li></ul></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-biorxiv-copyright" > <div class="pane-content"> <div class="field field-name-field-highwire-copyright field-type-text field-label-inline clearfix"><div class="field-label">Copyright </div><div class="field-items"><div class="field-item even">The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.<span class="license-type"> It is made available under a <a href="http://creativecommons.org/licenses/by/4.0/" class="" data-icon-position="" 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