<html><head><meta http-equiv="Content-Type" content="text/html charset=windows-1252"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><div>Dear colleagues,</div><div><br></div><div>… just to make you aware of next week's Psychology Seminar. We’ll be hosting <b>Niko Kriegeskorte</b> from the MRC Cognition and Brain Sciences Unit in Cambridge (where he is the programme leader of the Memory and Perception group). He is particularly well known for his work on object vision as well as methodological developments in fMRI analysis that have been very influential.</div><div><br></div>Location<span class="Apple-tab-span" style="white-space:pre"> </span><b>A1, Psychology Building</b><div>Date(s)<span class="Apple-tab-span" style="white-space:pre"> </span><b>Wednesday 18th February 2015 (16:00-17:00)</b><div><div><br><a href="http://www.nottingham.ac.uk/Psychology/events/external-seminar-dr-nikolaus-kriegeskorte-mrc-cbu.aspx">http://www.nottingham.ac.uk/Psychology/events/external-seminar-dr-nikolaus-kriegeskorte-mrc-cbu.aspx</a>
</div></div></div><div><br></div><div>Everyone welcome!</div><div><br></div><div>Best,</div><div>Denis</div><div><br></div><div><br></div><div><b>Vision as transformation of representational geometry </b></div><div><br></div><div><div>Dr Nikolaus Kriegeskorte</div><div><a href="http://www.mrc-cbu.cam.ac.uk/people/nikolaus.kriegeskorte/">http://www.mrc-cbu.cam.ac.uk/people/nikolaus.kriegeskorte/</a></div></div><div><br></div><div>Abstract<br><br></div><div>Vision can be understood as the transformation of representational geometry from one visual area to the next, and across time, as recurrent dynamics converge within a single area. The geometry of a representation can be usefully characterized by a representational distance matrix computed by comparing the patterns of brain activity elicited by each of a set of visual stimuli. This approach enables us to compare representations between brain areas, between different latencies after stimulus onset, between different individuals and between brains and computational models. Results from fMRI suggest that the early visual image representation is transformed into an object representation that emphasizes behaviourally important categorical divisions more strongly than accounted for by visual-feature computational models that are not explicitly optimized to distinguish categories. Twenty-eight computational model representations, ranging from classical computer-vision features to neuroscientifically motivated models like HMAX, failed to fully explain the strong categorical divisions in inferior temporal (IT) cortex of human and nonhuman primates. A deep convolutional neuronal network trained by supervised techniques on over a million category-labeled images came closest to explaining the IT representation. The IT categorical clusters appear to be consistent across individual human brains. However, the continuous representational space is unique to each individual and predicts individual idiosyncrasies in object similarity judgements. The representation flexibly emphasizes task-relevant category divisions through subtle distortions of the representational geometry. MEG results further suggest that the categorical divisions emerge dynamically, with the latency of categoricality peaks suggesting a role for recurrent processing.</div><br><br><div apple-content-edited="true">
<div style="color: rgb(0, 0, 0); letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><div>..</div><div>Denis Schluppeck, DPhil</div><div>Associate Professor</div><div>School of Psychology</div><div>University of Nottingham</div><div><a href="mailto:denis.schluppeck@nottingham.ac.uk">denis.schluppeck@nottingham.ac.uk</a></div><div><br></div></div><br class="Apple-interchange-newline"><br class="Apple-interchange-newline">
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