Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anytime Prediction as a Model of Human Reaction Time   Neural networks today often recognize objects as well as people do, and thus
might serve as models of the human recognition process. However, most such
networks provide their answer after a fixed computational effort, whereas human
reaction time varies, e.g. from 0.2 to 10 s, depending on the properties of
stimulus and task. To model the effect of difficulty on human reaction time, we
considered a classification network that uses early-exit classifiers to make
anytime predictions. Comparing human and MSDNet accuracy in classifying
CIFAR-10 images in added Gaussian noise, we find that the network equivalent
input noise SD is 15 times higher than human, and that human efficiency is only
0.6\% that of the network. When appropriate amounts of noise are present to
bring the two observers (human and network) into the same accuracy range, they
show very similar dependence on duration or FLOPS, i.e. very similar
speed-accuracy tradeoff. We conclude that Anytime classification (i.e. early
exits) is a promising model for human reaction time in recognition tasks.

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The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
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The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph