PSYC 272: Ensemble perception - how (and why) the visual system extracts summary statistics

 

Prof. Viola Störmer (vstoermer@ucsd.edu)

Fridays, 9am; Anderson Conference Room (5^th floor McGill Hall)

Class overview:

In this course we will learn about “ensemble perception” – the processing of summary statistics in the visual system, a topic that has received quite some attention over the past 20 years or so. We will start by reading the early, ‘original’ ensemble papers, discuss what ensemble processing is and how it might differ across different stimuli / levels of representations; we will also look at the role of attention in ensemble processing, how it relates to crowding, what ensemble processing means for visual working memory, and what we know about the neural processes underlying ensemble perception.

Each week we will read ~2 papers, which will be presented by a student in class. All other students are required to also read the papers and submit a few discussion points the day before to the presenter and me. Grades will be based on 1) attendance and participation, 2) submitting the discussion notes, and 3) the class presentations. Discussion points can be submitted here: https://goo.gl/forms/QElAwV1fO0dOgb9j2

Background readings:

Whitney, D., & Yamanashi Leib, A. (2018). Ensemble perception. Annual review of psychology, 69, 105-129.            **https://www.annualreviews.org/doi/10.1146/annurev-psych-010416-044232

 Alvarez, G. A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends in cognitive sciences, 15(3), 122-131.  **https://scholar.harvard.edu/alvarez/files/alvarez-2011-tics-ensemble.pdf

Class schedule:

01.11. – Introduction

01.18. - Ensemble processing for simple visual features

Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4(7), 739.   **http://www.cnbc.cmu.edu/~samondjm/papers/Parkesetal2001.pdf

 Ariely, D. (2001). Seeing sets: Representation by statistical properties. Psychological Science, 12(2), 157-162. **https://journals.sagepub.com/doi/pdf/10.1111/1467-9280.00327

Chong, S. C., & Treisman, A. (2003). Representation of statistical properties. Vision research, 43(4), 393-404. **https://www.sciencedirect.com/science/article/pii/S0042698902005965

01.25. -  Beyond the average: extracting variance and spatial distributions

Alvarez, G. A., & Oliva, A. (2009). Spatial ensemble statistics are efficient codes that can be represented with reduced attention. Proceedings of the National Academy of Sciences, pnas-0808981106.                                                                                      ** https://www.pnas.org/content/early/2009/04/17/0808981106.short

Michael, E., De Gardelle, V., & Summerfield, C. (2014). Priming by the variability of visual information. Proceedings of the National Academy of Sciences, 201308674. ** https://www.pnas.org/content/pnas/early/2014/05/09/1308674111.full.pdf

 

02.01. - The role of attention in ensemble perception

Chong, S. C., & Treisman, A. (2005). Statistical processing: Computing the average size in perceptual groups. Vision research, 45(7), 891-900. **https://www.sciencedirect.com/science/article/pii/S0042698904005139

Alvarez, G. A., & Oliva, A. (2008). The representation of simple ensemble visual features outside the focus of attention. Psychological science, 19(4), 392-398. ** https://journals.sagepub.com/doi/abs/10.1111/j.1467-9280.2008.02098.x

02.08 - High-level ensemble processing

Haberman, J., & Whitney, D. (2007). Rapid extraction of mean emotion and gender from sets of faces. Current Biology, 17(17), R751-R753. **https://www.sciencedirect.com/science/article/pii/S096098220701576X

Haberman, J., & Whitney, D. (2009). Seeing the mean: ensemble coding for sets of faces. Journal of Experimental Psychology: Human Perception and Performance, 35(3), 718. **http://whitneylab.berkeley.edu/PDFs/Haberman_Whitney_2009_JEP.pdf

Leib, A. Y., Kosovicheva, A., & Whitney, D. (2016). Fast ensemble representations for abstract visual impressions. Nature communications, 7, 13186. **https://www.nature.com/articles/ncomms13186

 

02.15. - NO CLASS

02.22.  - Is it really ‘ensemble’ processing? (Or strategic subsampling?)

Marchant, A. P., Simons, D. J., & de Fockert, J. W. (2013). Ensemble representations: Effects of set size and item heterogeneity on average size perception. Acta psychologica, 142(2), 245-250. **https://www.sciencedirect.com/science/article/pii/S0001691812001849

Haberman, J., & Whitney, D. (2010). The visual system discounts emotional deviants when extracting average expression. Attention, Perception, & Psychophysics, 72(7), 1825-1838. **https://link.springer.com/article/10.3758/APP.72.7.1825

 

03.01. - The role of ensembles in visual working memory

Brady, T. F., & Alvarez, G. A. (2011). Hierarchical encoding in visual working memory: Ensemble statistics bias memory for individual items. Psychological Science, 22(3), 384-392. **https://journals.sagepub.com/doi/abs/10.1177/0956797610397956

Brady, T. F., & Alvarez, G. A. (2015). No evidence for a fixed object limit in working memory: Spatial ensemble representations inflate estimates of working memory capacity for complex objects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(3), 921. **https://pdfs.semanticscholar.org/38a8/ba51b6d5a3ce960bb2e8669c670a7393cb69.pdf

 

03.08. - Relation to crowding

Balas, B., Nakano, L., & Rosenholtz, R. (2009). A summary-statistic representation in peripheral vision explains visual crowding. Journal of vision, 9(12), 13-13. **https://jov.arvojournals.org/article.aspx?articleid=2122150

Bulakowski, P. F., Post, R. B., & Whitney, D. (2011). Reexamining the possible benefits of visual crowding: dissociating crowding from ensemble percepts. Attention, Perception, & Psychophysics, 73(4), 1003-1009. **https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3089728/pdf/13414_2010_Article_86.pdf

***background reading on crowding***

Whitney, D., & Levi, D. M. (2011). Visual crowding: A fundamental limit on conscious perception and object recognition. Trends in cognitive sciences, 15(4), 160-168. **https://www.sciencedirect.com/science/article/pii/S1364661311000325

Pelli, D. G. (2008). Crowding: A cortical constraint on object recognition. Current opinion in neurobiology, 18(4), 445-451. **https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3624758/

 03.15. - Neural mechanisms of ensemble processing

Cant, J. S., & Xu, Y. (2012). Object ensemble processing in human anterior-medial ventral visual cortex. Journal of Neuroscience, 32(22), 7685-7700. **http://www.jneurosci.org/cgi/pmidlookup?view=long&pmid=22649247

Im, H. Y., Albohn, D. N., Steiner, T. G., Cushing, C. A., Adams, R. B., & Kveraga, K. (2017). Differential hemispheric and visual stream contributions to ensemble coding of crowd emotion. Nature human behaviour, 1(11), 828. ** https://www.nature.com/articles/s41562-017-0225-z

Whitney, D., Haberman, J., Sweeny, T. D., Werner, J. S., & Chalupa, L. M. (2014). From textures to crowds: multiple levels of summary statistical perception. The new visual neurosciences, 695-710. **https://whitneylab.berkeley.edu/PDFs/Whitney_Habermen_Sweeny_WerberChalubaBook.pdf