Connectionists: New Paper Attentional Bias, Cateogorization and Deep Learning

[Stephen Jose Hanson]

New paper


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

Attentional Bias in Human Category Learning: The Case of Deep Learning
Catherine Hanson, Leyla Roskan Caglar and Stephen José Hanson
RUBIC, Psychology, Rutgers University


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

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