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<p><font size="+1"><tt>New paper</tt></font></p>
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<pre wrap=""><a class="moz-txt-link-freetext" href="http://journal.frontiersin.org/article/10.3389/fpsyg.2018.00374/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Psychology&id=284733">http://journal.frontiersin.org/article/10.3389/fpsyg.2018.00374/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Psychology&id=284733</a></pre>
<p><font size="+1"><tt>Attentional Bias in Human Category Learning:
The Case of Deep Learning<br>
Catherine Hanson, Leyla Roskan Caglar and Stephen José Hanson<br>
RUBIC, Psychology, Rutgers University<br>
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<p>Category learning performance is influenced by both the nature of
the category's structure and the way category features are
processed during learning. Shepard (1964, 1987) showed that
stimuli can have structures with features that are statistically
uncorrelated (separable) or statistically correlated (integral)
within categories. Humans find it much easier to learn categories
having separable features, especially when attention to only a
subset of relevant features is required, and harder to learn
categories having integral features, which require consideration
of all of the available features and integration of all the
relevant category features satisfying the category rule (Garner,
1974). In contrast to humans, a single hidden layer
backpropagation (BP) neural network has been shown to learn both
separable and integral categories equally easily, independent of
the category rule (Kruschke, 1993). This “failure” to replicate
human category performance appeared to be strong evidence that
connectionist networks were incapable of modeling human
attentional bias. We tested the presumed limitations of
attentional bias in networks in two ways: (1) by having networks
learn categories with exemplars that have high feature complexity
in contrast to the low dimensional stimuli previously used, and
(2) by investigating whether a Deep Learning (DL) network, which
has demonstrated humanlike performance in many different kinds of
tasks (language translation, autonomous driving, etc.), would
display human-like attentional bias during category learning. We
were able to show a number of interesting results. First, we
replicated the failure of BP to differentially process integral
and separable category structures when low dimensional stimuli are
used (Garner, 1974; Kruschke, 1993). Second, we show that using
the same low dimensional stimuli, Deep Learning (DL), unlike BP
but similar to humans, learns separable category structures more
quickly than integral category structures. Third, we show that
even BP can exhibit human like learning differences between
integral and separable category structures when high dimensional
stimuli (face exemplars) are used. We conclude, after visualizing
the hidden unit representations, that DL appears to extend initial
learning due to feature development thereby reducing destructive
feature competition by incrementally refining feature detectors
throughout later layers until a tipping point (in terms of error)
is reached resulting in rapid asymptotic learning<br>
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