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Dear Colleagues:<br>
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Thank all those who commented. Please further give your views and
suggestions so that we can improve the BMI web in its page: Why-Me?
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This is the last email of the 6 emails, one for each discipline. <br>
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<h4 align="center">I Am a Psychologist</h4>
<p align="left">In experimental psychology, a vast literature
already exists about brain-mind behaviors. For example, Ida
Stockman (Stockman 2010) reviewed rich evidence that movement and
action are critical for perceptual and cognitive learning. Linda
Smith and coworkers (Yu et al. 2009) have demonstrated that
perception-action loops play important roles in children's visual
learning. Connectionist modeling since the early 1980s (e.g.,
McClelland et al. 1986, Elman et al. 1997, Shultz 2003) is a quest
for a deeper causality — the computational causality. The
brain-anatomy inspired computational model for the brain-mind by
Weng 2010 seems to predict how the motor areas represent states of
spatiotemporal context that are necessary for brain representation
and reasoning. For example, this network model predicted, through
computer simulations, how the complete transfers in human
perceptual learning recently reported (see, e.g., Xiao et al.
2008, Zhang et al. 2010) can occur computationally. Many
psychologists, including cognitive neuroscientists, talk about
brain as a symbolic network (e.g., with rigid functional modules)
but do not see how representations emerge inside the brain. Many
computational models in psychology use GOFAI (Good Old Fashioned
AI, e.g., symbolic Bayesian models). Therefore, they want to learn
biology, neuroscience, computer science (e.g., the automata
theory, symbolic AI, and the complexity theory), electrical
engineering (e.g., signal processing and system theory), and
mathematics (e.g., vectors, probability, statistics, and
optimization theory). For example, an increasing number of
psychological departments are changing the composition of their
faculty toward this direction. </p>
<h4 align="center">Why Learning Psychology?</h4>
<p align="left">The field of developmental psychology has
accumulated much evidence that the brain gradually develops its
capabilities for perception, cognition, behavior, and motivation.
Furthermore, psychology has a rich collection of models about
animal learning, including sensitization, habituation, classical
conditioning, instrumental conditioning, extinction, blocking,
homeostasis, cognitive learning, and language acquisition.
However, the brain learns autonomously, fully autonomous inside
the brain skull, while displaying capabilities some of which are
described by these qualitative learning models. Many models in
pattern recognition, AI and neural networks use either supervised
learning or unsupervised learning. In the former, class labels are
provided. In the latter, class labels are not provided that the
system must form clusters in the sensory space. These two learning
modes are not exactly what the brain uses. The brain does not need
a human teacher to provide discrete class labels. The brain does
not use unsupervised clusters in the sensory space alone. Instead,
the brain uses its body to be motor-supervised by the physical
world and uses its own actions to autonomously self-supervise
(practice). One may say that the machine learning community has
already reinforcement learning. However, many such reinforcement
learning models are symbolic, using a rigid time-discount value
model and not using emergent internal representations. Knowledge
in psychology enables you to rethink how to overcome your hurdles
(e.g., problems in providing discrete class labels).</p>
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<pre class="moz-signature" cols="72">--
--
Juyang (John) Weng, Professor
Department of Computer Science and Engineering
MSU Cognitive Science Program and MSU Neuroscience Program
3115 Engineering Building
Michigan State University
East Lansing, MI 48824 USA
Tel: 517-353-4388
Fax: 517-432-1061
Email: <a moz-do-not-send="true" class="moz-txt-link-abbreviated" href="mailto:weng@cse.msu.edu">weng@cse.msu.edu</a>
URL: <a moz-do-not-send="true" class="moz-txt-link-freetext" href="http://www.cse.msu.edu/%7Eweng/">http://www.cse.msu.edu/~weng/</a>
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