OSF | Levels of Representation in a Deep Learning Model of Categorization

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r.ee.get(this.agentIdentifier).abort(),!1}}}({features:[R,I,S],loaderType:"lite"})})()})();</script> <meta name="fragment" content="!"> <script src="/static/vendor/bower_components/raven-js/dist/raven.min.js"></script> <script> Raven.config("https://f4edd7edde06481584e276d05d80b0bc@sentry2-client.cos.io/9", {}).install(); </script>  <meta name="dc.title" content=" Levels of Representation in a Deep Learning Model of Categorization " /> <meta name="dc.type" content="collection" /> <meta name="citation_title" content=" Levels of Representation in a Deep Learning Model of Categorization " /> <meta name="citation_doi" content=" None" /> <meta name="dc.identifier" content=" None" /> <meta name="dc.identifier" content=" None" /> <meta name="citation_publisher" content="OSF" /> <meta name="dc.abstract" 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. Hosted on the Open Science Framework " /> <meta name="dc.license" content=" " /> <meta name="dc.datemodified" content=" 2024-05-06 " /> <meta name="dc.datesubmitted" content=" 2019-05-01 " /> <meta name="dc.publisher" content="OSF" /> <meta name="dc.language" content="en" /> <meta name="dc.identifier" content=" https://osf.io/jxavn/ " /> <meta name="citation_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. Hosted on the Open Science Framework " /> <meta name="citation_public_url" content=" https://osf.io/jxavn/ " /> <meta name="citation_publication_date" content=" 2019-05-01 " />  <meta property="og:ttl" content="3" /> <meta property="og:site_name" content="OSF" /> <meta property="og:url" content=" https://osf.io/jxavn/ " /> <meta property="og:title" content=" Levels of Representation in a Deep Learning Model of Categorization " /> <meta 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. Hosted on the Open Science Framework " /> <meta property="og:image" content=" https://osf.io/static/img/osf-sharing.png" /> <meta property="og:image:type" content="image/png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="630" /> <meta property="og:image:alt" content="OSF" /> <meta name="dc.creator" content="Olivia Guest" /> <meta name="citation_author" content="Olivia Guest" /> <meta name="dc.creator" content="Bradley C. Love" /> <meta name="citation_author" content="Bradley C. Love" />  <meta name="twitter:card" content="summary"> <meta name="twitter:site" content="@OSFramework"> <meta name="twitter:creator" content="@OSFramework">  <script type="application/ld+json"> { "@context": "https://schema.org", "@type": "Dataset", "creator": [ { "@type": "Person", "name": "Olivia Guest" }, { "@type": "Person", "name": "Bradley C. Love" } ], "dateCreated": "2019-05-01", "dateModified": "2024-05-06", "description": "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.\n\n", "identifier": [ "https://osf.io/jxavn" ], "keywords": [], "license": [], "name": "Levels of Representation in a Deep Learning Model of Categorization", "publisher": { "@type": "Organization", "name": "Center For Open Science" }, "url": "https://osf.io/jxavn" } </script>    <script src="https://cdnjs.cloudflare.com/ajax/libs/es6-shim/0.35.0/es6-shim.min.js"></script> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script> <script>window.jQuery || document.write('<script 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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). 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