What modern vision science reveals about the awareness puzzle: Summary-statistic encoding plus decision limits underlie the richness of visual perception and its quirky failures

There is a fundamental puzzle in understanding our awareness of the visual world. On one hand, our subjective experience is one of a rich visual world, which we perceive effortlessly. However, when we actually test perception, observers know surprisingly little. A number of tasks, from search, through inattentional blindness, to change blindness, suggest that there is surprisingly little awareness or perception without attention. Meanwhile, another set of tasks, such as multiple object tracking, dual-task performance, and visual working memory tasks suggest that both attention and working memory have low capacity. These two components together - poor perception without attention, and greatly limited capacity for attention and memory - imply that perception is impoverished. How can we make sense of this awareness puzzle, of the riddle of our rich subjective experience coupled with poor performance on experimental tasks? I suggest that, looked at in the right way, there is in fact no awareness puzzle. In particular, I will argue that the tasks that show limits are inherently difficult tasks, and that there exists a unified explanation for both the rich subjective experience and the apparent limits.

[1]  Eero P. Simoncelli,et al.  Metamers of the ventral stream , 2011, Nature Neuroscience.

[2]  R. Rosenholtz Capabilities and Limitations of Peripheral Vision. , 2016, Annual review of vision science.

[3]  D. Dennett No bridge over the stream of consciousness , 1998 .

[4]  Honghua Chang,et al.  Search performance is better predicted by tileability than presence of a unique basic feature , 2016, Journal of vision.

[5]  Ruth Rosenholtz,et al.  Capacity limits and how the visual system copes with them , 2017, HVEI.

[6]  G. Brelstaff,et al.  Is the Richness of Our Visual World an Illusion? Transsaccadic Memory for Complex Scenes , 1995, Perception.

[7]  J. O'Regan,et al.  Solving the "real" mysteries of visual perception: the world as an outside memory. , 1992, Canadian journal of psychology.

[8]  David Whitney,et al.  Ensemble perception: Summarizing the scene and broadening the limits of visual processing. , 2012 .

[9]  N. Block Perceptual consciousness overflows cognitive access , 2011, Trends in Cognitive Sciences.

[10]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[11]  R. Rosenholtz,et al.  A summary-statistic representation in peripheral vision explains visual crowding. , 2009, Journal of vision.

[12]  Krista A. Ehinger,et al.  Rethinking the Role of Top-Down Attention in Vision: Effects Attributable to a Lossy Representation in Peripheral Vision , 2011, Front. Psychology.

[13]  I. Rock,et al.  Inattentional blindness: Perception without attention. , 1998 .

[14]  Ruth Rosenholtz,et al.  What your visual system sees where you are not looking , 2011, Electronic Imaging.

[15]  G. Alvarez Representing multiple objects as an ensemble enhances visual cognition , 2011, Trends in Cognitive Sciences.

[16]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[17]  Jason M Haberman,et al.  Seeing the mean: ensemble coding for sets of faces. , 2009, Journal of experimental psychology. Human perception and performance.

[18]  Allison Yamanashi Leib,et al.  Fast ensemble representations for abstract visual impressions , 2016, Nature Communications.

[19]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[20]  R. Rosenholtz,et al.  Pooling of continuous features provides a unifying account of crowding , 2016, Journal of vision.

[21]  C. Koch,et al.  Visual Search and Dual Tasks Reveal Two Distinct Attentional Resources , 2004, Journal of Cognitive Neuroscience.

[22]  D. Ariely Seeing Sets: Representation by Statistical Properties , 2001, Psychological science.

[23]  Steven L Franconeri,et al.  Selecting and tracking multiple objects. , 2015, Wiley interdisciplinary reviews. Cognitive science.

[24]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[25]  V. Lamme How neuroscience will change our view on consciousness , 2010, Cognitive neuroscience.

[26]  Ronald A. Rensink,et al.  TO SEE OR NOT TO SEE: The Need for Attention to Perceive Changes in Scenes , 1997 .

[27]  Michael A. Cohen,et al.  What is the Bandwidth of Perceptual Experience? , 2016, Trends in Cognitive Sciences.

[28]  R. Rosenholtz,et al.  A summary statistic representation in peripheral vision explains visual search. , 2009, Journal of vision.

[29]  Krista A. Ehinger,et al.  A general account of peripheral encoding also predicts scene perception performance. , 2016, Journal of vision.