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


Prof. Viola Störmer (

Fridays, 9am; Anderson Conference Room (5th 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:

Background readings:

Whitney, D., & Yamanashi Leib, A. (2018). Ensemble perception. Annual review of psychology, 69, 105-129.            **

 Alvarez, G. A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends in cognitive sciences, 15(3), 122-131.  **

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 Neuroscience4(7), 739.   **

 Ariely, D. (2001). Seeing sets: Representation by statistical properties. Psychological Science12(2), 157-162. **

Chong, S. C., & Treisman, A. (2003). Representation of statistical properties. Vision research43(4), 393-404. **

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.                                                                                      **

Michael, E., De Gardelle, V., & Summerfield, C. (2014). Priming by the variability of visual information. Proceedings of the National Academy of Sciences, 201308674. **


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 research45(7), 891-900. **

Alvarez, G. A., & Oliva, A. (2008). The representation of simple ensemble visual features outside the focus of attention. Psychological science19(4), 392-398. **

02.08 - High-level ensemble processing

Haberman, J., & Whitney, D. (2007). Rapid extraction of mean emotion and gender from sets of faces. Current Biology17(17), R751-R753. **

Haberman, J., & Whitney, D. (2009). Seeing the mean: ensemble coding for sets of faces. Journal of Experimental Psychology: Human Perception and Performance35(3), 718. **

Leib, A. Y., Kosovicheva, A., & Whitney, D. (2016). Fast ensemble representations for abstract visual impressions. Nature communications7, 13186. **


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. **

Haberman, J., & Whitney, D. (2010). The visual system discounts emotional deviants when extracting average expression. Attention, Perception, & Psychophysics72(7), 1825-1838. **


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. **

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 Cognition41(3), 921. **


03.08. - Relation to crowding

Balas, B., Nakano, L., & Rosenholtz, R. (2009). A summary-statistic representation in peripheral vision explains visual crowding. Journal of vision9(12), 13-13. **

Bulakowski, P. F., Post, R. B., & Whitney, D. (2011). Reexamining the possible benefits of visual crowding: dissociating crowding from ensemble percepts. Attention, Perception, & Psychophysics73(4), 1003-1009. **

***background reading on crowding***

Whitney, D., & Levi, D. M. (2011). Visual crowding: A fundamental limit on conscious perception and object recognition. Trends in cognitive sciences15(4), 160-168. **

Pelli, D. G. (2008). Crowding: A cortical constraint on object recognition. Current opinion in neurobiology18(4), 445-451. **

 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 Neuroscience32(22), 7685-7700. **

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 behaviour1(11), 828. **

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. **