Perception is rich and probabilistic

Abstract

When we see a stimulus, e.g. a star-shaped object, our intuition is that we should perceive a single, coherent percept (even if it is inaccurate). But the neural processes that support perception are complex and probabilistic. Simple lines cause orientation-selective neurons across a population to fire in a probabilistic-like manner. Does probabilistic neural firing lead to non-probabilistic perception, or are the representations behind perception richer and more complex than intuition would suggest? To test this, we briefly presented a complex shape and had participants report the correct shape from a set of options. Rather than reporting a single value, we used a paradigm designed to encourage to directly report a representation over shape space-participants placed a series of Gaussian bets. We found that participants could report more than point-estimates of shape. The spread of responses was correlated with accuracy, suggesting that participants can convey a notion of relative imprecision. Critically, as participants placed more bets, the mean of responses show increased precision. The later bets were systematically biased towards the target rather than haphazardly placed around bet 1. These findings strongly indicate that participants were aware of more than just a point-estimate; Perceptual representations are rich and likely probabilistic.

Figure 1

Experiment Paradigm. (a) Trials began with a 500 ms fixation. A target shape then appeared (at some random bearing off-center) for a short duration of 40 ms. For illustration the target is noiseless, but actually was noised in the experiment. Thereafter, the participant is free to make their bets on what they saw using the shape wheel. The currently selected shape appeared on the center of the screen. (b) Participant bets add Gaussian (SD = 4°) component on the circle, which was always previewed to participants pre-click. Participants were awarded points based on the height on the target shape’s position on the circle. Participants were given incentive to spread their bets as stacking provided diminishing returns on height. (c) Illustration of the noise effect of replacing the black pixels with the background color. The shapes and shape wheel were adapted from Li et al..

Figure 2

Comparing the error distribution of the first response across trials (red) versus the drawn uncertainty profiles (after all six bets) averaged across trials (blue), for each participant. (a) The 40 participants of in the main study. (b) 20 participants from a control study where participants had the ability to undo a response.

Figure 3

Individual response errors (red) and cumulative errors (blue) as a function of response order. Cumulative errors are calculated as the error of the mean of responses. For example, the cumulative error for response 3 would be influenced by response 2 and response 1, whereas the individual response error for response 3 is solely determined by that bet. (a) Errors for Experiment 1. (b) The equivalent errors for Experiment 2.