Gardner Lab

Psychology Department

Neurosciences Institute

Stanford University


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shared:vision_lunch 2018/06/07 23:41 shared:vision_lunch 2018/08/12 21:20 current
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-====== Vision Breakfast ====== +====== Vision Brunch ======
-Weekly meeting of people at and around Stanford interested in vision research-related topics.+
-**We meet Wednesdays at 9:30 AM in room 419 at [[https://www.google.com/maps/search/jordan+hall+stanford+map/@37.4286074,-122.1714145,235m/data=!3m1!1e3|Jordan Hall]], Stanford University **+Please visit our [[https://vision.danbirman.com|improved website]]!
-Please visit [[https://mailman.stanford.edu/mailman/listinfo/vis-lunch-announce|vis-lunch-announce]] to sign up! 
-Organizers [[mareikegrotheer@gmail.com|Mareike Grotheer]] and [[danbirman@gmail.com|Daniel Birman]]+====== Archive ====== 
 +=== Winter 2018 === 
 +^Date^Speaker^Topic^ 
 +|3/21/18|Journal Club: Jesse Gomez |Differential Sampling of Visual Space in Ventral and Dorsal Early Visual Cortex (http://www.jneurosci.org/content/38/9/2294)| 
 +|3/7/18|Journal Club: Mareike Grotheer |Facephenes and Rainbows (http://www.pnas.org/content/114/46/12285)| 
 +|2/14/18| [[https://vistalab.stanford.edu/rosemary-le/|Rosemary Le]] (PhD Student, Wandell Lab, Stanford)|Stimulus dependency of population receptive fields in the VWFA and visual field maps| 
 +|2/7/18| [[https://people.stanford.edu/pjkohler//|Peter Kohler]] (PostDoc, Norcia Lab, Stanford)|Symmetry as a fundamental feature dimension in mid-level vision| 
 +|1/24/18|Journal Club (continued): Dan Yamins|[[https://www.biorxiv.org/content/early/2017/10/11/201764|Deep convolutional models improve predictions of macaque V1 responses to natural images]]| 
 +|1/17/18|Journal Club: Dan Yamins|[[https://www.biorxiv.org/content/early/2017/10/11/201764|Deep convolutional models improve predictions of macaque V1 responses to natural images]]| 
 +=== Fall 2017 ===
-===== Next meeting: 6/12/18 and 6/13/18 Shaul Hochstein and Hsin-Hung Li =====+^Date^Speaker^Topic^ 
 +|**Note**: Friday 12/15/17 in 380-381U|[[http://www0.cs.ucl.ac.uk/staff/Z.Li/|Zhaoping Li]]| Central versus Peripheral vision:  computational roles and experimental data | 
 +|11/29/17|[[https://cfn.upenn.edu/~jonghoyi/|Jongho Lee]] (Prof. of EE, Seoul National University)|Imaging myelin and iron in the brain| 
 +|11/8/17|Catherine Manning (Postdoc, Norcia Lab)|The development of perceptual decision-making in children: A drift-diffusion study of behavioural and EEG data| 
 +|11/1/17|[[http://gru.stanford.edu|Justin Gardner]] (Prof. of Psychology, Stanford)|Inverted encoding models of human population response conflate noise and neural tuning width| 
 +|10/25/17|Journal Club: Dan Birman|[[https://www.nature.com/neuro/journal/v20/n10/full/nn.4622.html|Selective attention within the foveola]]| 
 +|10/18/17|[[http://www.psy.vanderbilt.edu/tonglab/sonia/Personal/Home.html|Sonia Poltoratski]] (Postdoc Grill-Spector Lab)|Contextual effects in the early visual system and their modulation by attention| 
 +|10/11/17|[[https://med.stanford.edu/profiles/keren-haroush|Keren Haroush]] (Prof. of Neurobiology, Stanford)|The role of Dorsal Anterior Cingulate in Cooperative Social Interactions| 
 +|10/4/17| [[http://www.judithfan.net/|Judy Fan]] (Postdoc, Goodman Lab)|Visual communication in context| 
 +|9/27/17| [[http://niru.org/|Niru Maheswaranathan]] (PhD, Neuroscience Program, Stanford)|Deep learning models of the retinal response to natural scenes| 
 +|9/13/17|Journal Club: Mareike Grotheer|[[http://www.jneurosci.org/content/37/32/7700|Interaction between Scence and Object Processing Revealed by Human fMRI and MEG Decoding]]|
-=== 6/12/18: Set Summary Perception, Outlier Pop Out, and Categorization: A Common Underlying Computation? ==+===== Pre Fall 2017 Archive =====
- +
- +
-== Abstract == +
-Recent research has focused on perception of set statistics. Presented briefly with a group of elements, either simultaneously or successively, observers report precisely the mean of a variety of set features, but are unaware of individual element values. This has been shown for both low and high level features, from circle size to facial expression. A remaining puzzle is how can the perceptual system compute the mean of element values without first knowing the individual values. We performed a series of studies to extend these findings and shed light on this conundrum. We found that set mean computation is performed automatically and implicitly, affecting performance of an unrelated task, and that it is performed on-the-fly for each psychophysical trial, independently. We find that observers also rapidly identify outliers within a set, indicating that they perceive the range of stimulus sets. We find that range perception, too, is automatic, implicit, and on-the-fly. In purposely-designed parallel studies, we find similar characteristics for set and category perception. In particular, category prototype and boundary correspond to set mean and range. Our matching findings suggest that categorization and set summary perception might share computational elements. We suggest and analyze a fundamental computational procedure, based on population encoding, that encompasses all the features of set summary perception, and might also underlie categorization. Finally, we note that this computational procedure might preclude the classic debate concerning category representation by prototype or boundary.   +
- +
-=== 6/13/18: Attention model of binocular rivalry === +
- +
-== Abstract == +
-When the two eyes are presented with incompatible images, perception alternates between the two images, creating a phenomena known as binocular rivalry. During rivalry, perceptual experience evolves dynamically while the external inputs are held constant. Binocular rivalry thereby offers a gateway for studying intrinsic cortical computations. In conventional theories of binocular rivalry, the competition between the two percepts has been characterized as mutual inhibition between two populations of neurons selective for each of the two stimuli. However, converging experimental evidence has shown that rivalry also depends on attention: rivalry is largely eliminated when attention is diverted away from the stimuli. In addition, the competing image in one eye suppresses the target image in the other eye through a gain change similar to that induced by attentional modulation. These results require a revision of the current theories of binocular rivalry, in which the role of attention is ignored.  +
- +
-We investigated the role of attention in binocular rivalry in a psychophysical and a preliminary MEG experiment. We found that binocular competition is driven by both attention and mutual inhibition, which have distinct selectivity. We developed a new computational model of rivalry, and with a bifurcation analysis, we identified the parameter space in which the model’s behavior was consistent with experimental results. The model provides a parsimonious account of various perceptual dynamics of rivalry for which there was no previous explanation. +
- +
-====== Schedule ====== +
- +
-===== Presenting at Vision Breakfast ===== +
- +
-We would like to encourage anyone in the Stanford community (or outside it) who is working on vision-related research to come and talk about their stuff. In particular, Vision Breakfasts are intended to allow people in Vision labs at Stanford to hear about each other's work early on, when feedback can be important, and the results are new and exciting. +
- +
-Please email [[mailto:mareikegrotheer@gmail.com|Mareike]] and [[mailto:danbirman@gmail.com|Dan]] if you're interested in presenting.  +
- +
-In general, it should be no problem to bump journal clubs to a later week, so if you see a date you're interested in, chances are we can accommodate you. +
- +
-===== Coming soon in 2018 ===== +
- +
-^Date^Speaker/JC^Title^ +
-|06/12/18: 11.30am|Talk: Shaul Hochstein|Set Summary Perception, Outlier Pop Out, and Categorization: A Common Underlying Computation?| +
-|06/13/18|Talk: Hsin-hung Li|TBA| +
- +
--- we will reconvene in Fall 2018! Please send Dan or Mareieke an email if you are interested in presenting -- +
- +
- +
- +
-===== Previous meetings ===== +
- +
-^Date^Speaker/JC^Title^ +
-|06/06/18|Journal Club: Elias Wang|Image reconstruction by domain-transform manifold learning (https://www.nature.com/articles/nature25988)| +
-|05/29/18|Talk: Guillaume Riesen (PhD Student, Gardner Lab, Stanford)|Rivalry and fusion can coexist in a tristable dynamic state| +
-|05/16/18|Talk: Xiaomo Chen (Postdoc, Moore Lab, Stanford)|Dissonant Representations of Visual Space in Prefrontal Cortex during Eye Movements| +
-|05/09/18|Talk: Kathryn Bonnen (PhD Student, Huk Lab, UT Austin)| Encoding and decoding 3D motion | +
-|05/02/18|Talk: Dan Birman|Flexible readout of stable cortical representations support motion visibility perception| +
-|04/25/18|Marc Zirnsak (Postdoc, Moore Lab, Stanford)|A potential source of saliency in the primate brain| +
-|04/11/18|Journal Club: Dan Birman |Feedback determines the structure of correlated variability in primary visual cortex. (https://www.nature.com/articles/s41593-018-0089-1)| +
- +
- +
-===== Archive ===== +
- +
-An archive of Vision Lunch meetings of the last year(s) exists [[http://gru.stanford.edu/doku.php/shared/vision_lunch_archive|here]].+
An archive of Vision Lunch meetings prior to 9/13/17 exists on the [[http://web.stanford.edu/group/vista/cgi-bin/wiki/index.php/Vision_Lunch|Vista Lab wiki]]. An archive of Vision Lunch meetings prior to 9/13/17 exists on the [[http://web.stanford.edu/group/vista/cgi-bin/wiki/index.php/Vision_Lunch|Vista Lab wiki]].