Human perception is based on mathematically optimal principles, but the brain implements those principles imperfectly, suggests new research by Elina Stengård and Ronald van den Berg of the University of Uppsala, Sweden.
They present their findings in PLOS Computational Biology.
Bayesian approaches to perception offer a principled, coherent and elegant answer to the central problem of perception: what the brain should believe about the world based on sensory data.
This chapter gives a tutorial introduction to Bayesian inference, illustrating how it has been applied to problems in perception.
Inference in perception One of the central ideas in the study of perception is that the proximal stimulus – the pattern of energy that impinges on sensory receptors, such as the visual image – is not sufficient to specify the actual state of the world outside (the distal stimulus).
That is, while the image of your grandmother on your retina might look like your grandmother, it also looks like an infinity of other arrangements of matter, each having a different combination of 3D structure, surface properties, color properties, etc., so that they happen to look just like your grandmother from a particular viewpoint.
Naturally, the brain generally does not perceive these far-fetched alternatives, but rapidly converges on a single solution which is what we consciously perceive.
A shape on the retina might be a large object that is far away, or a smaller one more nearby, or anything in between.
A mid-gray region on the retina might be a bright white object in dim light, or a dark object in bright light, or anything in between.
An elliptical shape on the retina might be an elliptical object face-on, or a circular object slanted back in depth, or anything in between.
Every proximal stimulus is consistent with an infinite family of possible scenes, only one of which is perceived.
The central problem for the perceptual system is to quickly and reliably decide among all these alternatives, and the central problem for visual science is to figure out what rules, principles, or mechanisms the brain uses to do so.
This process was called unconscious inference by Helmholtz, perhaps the first scientist to appreciate the problem, and is sometimes called inverse optics to convey the idea that the brain must in a sense invert the process of optical projection – to take the image and recover the world that gave rise to it.
The human brain uses imprecise sensory inputs to determine truths about the surrounding environment.
Previous research has suggested that human perception is “Bayesian,” meaning that the brain accounts for uncertainty of sensory observations in a mathematically optimal way.
However, some of those studies have been criticized mathematically, and other research suggests that the brain is inherently imprecise at the neural level.
To address those concerns, Van den Berg and Stengård presented 30 volunteers with a series of perception tests.
These tests involved identifying whether ellipse shapes appearing on a screen were tilted clockwise or counterclockwise from vertical.
Different tests incorporated sensory uncertainty in different ways, such as varying degrees of elongation of the ellipse shape, distractions in the form of nearby ellipses, and a short display time of the ellipse on the screen.
The researchers then analyzed their results against a series of different mathematical models.
They found that the data is best accounted for by a model that is Bayesian at its core, but also subject to imperfections.
This model outperformed both an optimal Bayesian model and all non-Bayesian models that were tested.
“Our results suggest that human perception is blueprinted on optimal strategies, even though the brain’s execution of these strategies seems to be imperfect,” Van den Berg says.
“This novel concept provides a theoretical middle ground between the seemingly opposing literature of optimal models and heuristic models.”
A Bayesian brain with imperfections. The image is credited to Elina Stengård.
Additional research is needed to pinpoint what causes the apparent imperfections in the decision-making process during the ellipse perception tests.
Future research could also test whether the imperfect Bayesian model can account for human behavior in other kinds of perception tests, and in higher-level cognitive decision-making tasks.
Funding: RVDB acknowledges support from the Swedish Research Council (Vetenskapsrådet; reg. nr. 2015-00371; http://www.vr.se) and Marie Sklodowska Curie Actions, Cofund (project INCA 600398; https://ec.europa.eu/research). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Ronald van den Berg – PLOS
The image is credited to Elina Stengård.
Original Research: Open access.
Stengård E, van den Berg R (2019) “Imperfect Bayesian inference in visual perception”. PLOS Computational Biology 15(4): e1006465 doi:10.1371/journal.pcbi.1006465