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<span style="font-family:'Helvetica'; font-size:medium; color:rgba(0, 0, 0, 1);"><b>From:
</b></span><span style="font-family:'Helvetica'; font-size:medium;">Susan S Lanza <<a href="mailto:sdl@mit.edu">sdl@mit.edu</a>><br>
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<span style="font-family:'Helvetica'; font-size:medium; color:rgba(0, 0, 0, 1);"><b>Date:
</b></span><span style="font-family:'Helvetica'; font-size:medium;">January 15, 2014 3:53:10 PM EST</span></div>
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<span style="font-family:'Helvetica'; font-size:medium; color:rgba(0, 0, 0, 1);"><b>Subject:
</b></span><span style="font-family:'Helvetica'; font-size:medium;"><b>special topics course SP 14</b><br>
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Hi Janice,
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<div>Could you please send this announcement out to your students, it is a special topics course, 9.S912 that is being offered this SP on Mondays from 1-4 pm in 46-5056</div>
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<div>Thanks</div>
<div>S</div>
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<blockquote type="cite">Title: Computation and Visual Attention<br>
Instructor: J.K. Tsotsos (<br>
<a href="http://www.cse.yorku.ca/~tsotsos">www.cse.yorku.ca/~tsotsos</a><br>
)<br>
Term: Spring<br>
Years: 2014 only<br>
Level: Graduate<br>
Units: 3 - 0 - 6<br>
Prerequisites: at least one introductory course in computer vision,<br>
visual perception, and/or visual neuroscience<br>
<br>
Description<br>
<br>
Interest in the phenomenon known as attention has a long history, and recently there seems to have been a small explosion of research effort, experimental, theoretical and empirical, whose goal is to develop a deeper and more complete understanding of attention,
particularly visual, in humans, non-human primates and machines. The problem with all this work is that it seems to generate more confusion rather than a deeper understanding. This course will explore many of the different approaches and perspectives in the
current literature with the intent of developing a 'big picture' view of what this thing called visual attention might entail and how can we best further deepen our understanding.<br>
<br>
Each class will have a duration of 3 hours. The opening classes will be lectures by the instructor introducing the topic, providing basic background in the psychology and neurobiology of attention, detailing the computational foundations for attentive processing,
wandering through a brief tour of highlights in attention during the history of Artificial Intelligence, Cognitive Science and Computational Neuroscience, and finally covering one theory, the Selective Tuning theory, in its full detail. Then, the following
classes will be a combination of student led themed discussion and guest lecturers. The point of these is to explore different viewpoints and theories, as contrasts to Selective Tuning, so that the class can develop an appreciation of both strengths and weaknesses
not only of the research but also the methodologies. The last class will be led by the instructor but intended to be class discussion and debate on what has been covered with the goal of prescribing a !<br>
pathway fo<br>
r future research on the topic. Depending on interest and progress, this last class could conclude with writing a brief technical report, authored by the whole class, detailing the routes the class feels would be most fruitful.<br>
<br>
Recommended Reading:<br>
J.K. Tsotsos, A Computational Perspective on Visual Attention, MIT Press, 2011<br>
<br>
<a href="http://mitpress.mit.edu/books/computational-perspective-visual-attention">http://mitpress.mit.edu/books/computational-perspective-visual-attention</a><br>
<br>
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A tentative schedule is:<br>
<br>
Week 1: Introduction to attention<br>
Week 2: Computational foundations of visual attention<br>
Week 3: Selective Tuning Part I<br>
Week 4: Selective Tuning Part II<br>
Week 5: The Feature Integration Theory and Guided Search families<br>
Week 6: Neurobiology of Attention<br>
Week 7: Saliency Map Models<br>
Week 8: Biased Competition and its enhancements<br>
Week 9: Dynamical Systems Models<br>
Week 10: The Roles of Gist, Task and Prior Knowledge<br>
Week 11: Bayesian Methods in Attention<br>
Week 12: Final Integrative Discussion<br>
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The relative ordering of the class topics for weeks 5 through 11 will depend on the availability and scheduling constraints of invited guest lecturers.<br>
<br>
Guest lecturers who will be contacted include:<br>
Week 5: Feature Integration Theory and Guided Search Theory families<br>
Jeremy Wolfe (Harvard)<br>
Week 6: Neurobiology of Attention<br>
Robert Desimone (MIT) ; John Maunsell (Harvard)<br>
Week 7: Saliency Map Models<br>
Neil Bruce (U of Manitoba)<br>
Week 8: Biased Competition and its enhancements<br>
Robert Desimone (MIT)<br>
Week 9: Dynamical Systems Models and the role of oscillations<br>
Salva Ardid (York U)<br>
Week 10: The Roles of Gist, Task and Prior Knowledge<br>
Antonio Torralba (MIT)<br>
Week 11: Bayesian Methods in Attention<br>
Tomaso Poggio (MIT)<br>
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<div><font class="Apple-style-span" color="#230AFB">********************************************************</font></div>
<div><font class="Apple-style-span" color="#230AFB">Susan D. Lanza</font><span class="Apple-tab-span" style="white-space: pre; font-size: 12px; "><font class="Apple-style-span" color="#230AFB">
</font></span></div>
<div><font class="Apple-style-span" color="#230AFB">Undergraduate Administrator</font><span class="Apple-tab-span" style="white-space: pre; font-size: 12px; "><font class="Apple-style-span" color="#230AFB">
</font></span></div>
<div><font class="Apple-style-span" color="#230AFB">MIT Department of Brain & Cognitive Sciences</font><span class="Apple-tab-span" style="white-space: pre; font-size: 12px; "><font class="Apple-style-span" color="#230AFB">
</font></span></div>
<div><font class="Apple-style-span" color="#230AFB">46-2005, 77 Massachusetts Avenue</font></div>
<div><font class="Apple-style-span" color="#230AFB">Cambridge, MA 02139</font></div>
<div><font class="Apple-style-span" color="#230AFB">Tel: 617-253-0482</font></div>
<div><font class="Apple-style-span" color="#230AFB">Fax: 617-253-9767</font></div>
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