The Interface Theory of Perception

Hoffman, Donald D.

doi:10.1002/9781119170174.epcn216

Cited by 30 publications

(46 citation statements)

References 57 publications

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“…From the point of view of modern theory, almost any form of rational inference will entail some kind of simplicity bias. Hence rather than being a foundational principle, the human simplicity bias may simply be an epiphenomenon of a more basic goal of mental function, such as veridicality, optimal estimation, or, perhaps most fundamentally, adaptive functionality …”

Section: Resultsmentioning

confidence: 99%

“…Hence rather than being a foundational principle, the human simplicity bias may simply be an epiphenomenon of a more basic goal of mental function, such as veridicality, 107 optimal estimation, 108 or, perhaps most fundamentally, adaptive functionality. 109,110 NOTES a Occam himself was arguing against the existence of "universals" (i.e., generalizations), maintaining that one should not posit the existence of entities beyond those that can be directly observed; see Ref 1. b Kolmogorov complexity is uncomputable for essentially the same reason there is no "Smallest uninteresting number"-if there were, that would indeed be very interesting. (see Ref 12 on what Bertrand Russell called the Berry paradox.)…”

Section: Resultsmentioning

confidence: 99%

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The simplicity principle in perception and cognition

Feldman

2016

WIRES Cognitive Science

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The simplicity principle, traditionally referred to as Occam’s razor, is the idea that simpler explanations of observations should be preferred to more complex ones. In recent decades the principle has been clarified via the incorporation of modern notions of computation and probability, allowing a more precise understanding of how exactly complexity minimization facilitates inference. The simplicity principle has found many applications in modern cognitive science, in contexts as diverse as perception, categorization, reasoning, and neuroscience. In all these areas, the common idea is that the mind seeks the simplest available interpretation of observations— or, more precisely, that it balances a bias towards simplicity with a somewhat opposed constraint to choose models consistent with perceptual or cognitive observations. This brief tutorial surveys some of the uses of the simplicity principle across cognitive science, emphasizing how complexity minimization in a number of forms has been incorporated into probabilistic models of inference.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The simplicity principle in perception and cognition

Feldman

2016

WIRES Cognitive Science

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“…This alternative view, and in particular A3, implies that observers-including ourselves-can benefit maximally from beliefs that are not in any meaningful sense "true," but are rather, one might say, "helpful fictions." Such a position has indeed been articulated by Hoffman and others under the label "user interface theory" (Hoffman, 2009;Hoffman & Prakash, 2014;Hoffman, Singh, & Prakash, 2015;Koenderink, 2011Koenderink, , 2015. This possibility is inherently counterintuitive, because it implies that our intuitions about the actual state of the world are, in a deep sense, illusory.…”

Section: If the Cw Is Wrong What's Right?mentioning

confidence: 98%

What Are the “True” Statistics of the Environment?

Feldman

2016

Cognitive Science

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A widespread assumption in the contemporary discussion of probabilistic models of cognition, often attributed to the Bayesian program, is that inference is optimal when the observer's priors match the true priors in the world-the actual "statistics of the environment." But in fact the idea of a "true" prior plays no role in traditional Bayesian philosophy, which regards probability as a quantification of belief, not an objective characteristic of the world. In this paper I discuss the significance of the traditional Bayesian epistemic view of probability and its mismatch with the more objectivist assumptions about probability that are widely held in contemporary cognitive science. I then introduce a novel mathematical framework, the observer lattice, that aims to clarify this issue while avoiding philosophically tendentious assumptions. The mathematical argument shows that even if we assume that "ground truth" probabilities actually do exist, there is no objective way to tell what they are. Different observers, conditioning on different information, will inevitably have different probability estimates, and there is no general procedure to determine which one is right. The argument sheds light on the use of probabilistic models in cognitive science, and in particular on what exactly it means for the mind to be "tuned" to its environment.

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confidence: 99%

“…There remain many intriguing questions: how does optical and retinal processing of images [38,39] influence the types of unsupervised objectives used by brains? How is this learning shared across neural sites and sensory modalities, and how do the statistics of our infant environments [40] shape learning?Unsupervised learning objectives such as data compression and spatial prediction are ecologically plausible ways for biological visual systems to learn, and offer a solution to the problem of how brains seem to represent properties of the distal world without access to ground truth [3,5,6,41,42]. Our work shows for the first time that perceptual dimensions, like gloss, that appear to (imperfectly) estimate properties of the physical world can emerge spontaneously by learning to efficiently encode sensory data.…”

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confidence: 99%

Unsupervised learning predicts human perception and misperception of gloss

Storrs

Fleming

2020

Preprint

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1 Summary Gloss perception is a challenging visual inference that requires disentangling the contributions of reflectance, lighting, and shape to the retinal image [1][2][3]. Learning to see gloss must somehow proceed without labelled training data as no other sensory signals can provide the 'ground truth' required for supervised learning [4][5][6]. We reasoned that paradoxically, we may learn to infer distal scene properties, like gloss, by learning to compress and predict spatial structure in proximal image data. We hypothesised that such unsupervised learning might explain both successes and failures of human gloss perception, where classical 'inverse optics' cannot. To test this, we trained unsupervised neural networks to model the pixel statistics of renderings of glossy surfaces and compared the resulting representations with human gloss judgments.The trained networks spontaneously cluster images according to underlying scene properties such as specular reflectance, shape and illumination, despite receiving no explicit information about them. More importantly, we find that linearly decoding specular reflectance from the model's internal code predicts human perception and misperception of glossiness on an image-by-image basis better than the true physical reflectance does, better than supervised networks explicitly trained to estimate specular reflectance, and better than alternative image statistic and dimensionality-reduction models. Indeed, the unsupervised networks correctly predict well-known illusions of gloss perception caused by interactions between surface relief and lighting [7,8] which the supervised models totally fail to predict. Our findings suggest that unsupervised learning may explain otherwise inexplicable errors in surface perception, with broader implications for how biological brains learn to see the outside world.2 Highlights  We trained unsupervised neural networks to synthesise images of glossy surfaces  They spontaneously learned to encode gloss, lighting and other scene factors  The networks correctly predict both errors and successes of human gloss perception  The findings provide new insights into how the brain likely learns to see 3 Results and DiscussionThe central intuition behind our findings is that learning to compress the complex image structure created by reflections from glossy surfaces into a highly compact code forces the brain to discover representations that partially-but imperfectly-disentangle the distal physical factors responsible for variations within and between images. This potentially explains both the broad successes and specific pattern of errors known to occur in gloss perception [2,3,[7][8][9][10][11][12]. To test this, we rendered 10,000 images from a virtual world of bumpy frontoparallel surfaces with either high or low specular reflectance, random colour and depth of surface relief, illuminated by six natural light fields (Figure 1A-B). We trained ten instances of an unsupervised PixelVAE network [13,14] on this dataset. The model's training objecti...

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The Interface Theory of Perception

Cited by 30 publications

References 57 publications

The simplicity principle in perception and cognition

The simplicity principle in perception and cognition

What Are the “True” Statistics of the Environment?

Unsupervised learning predicts human perception and misperception of gloss

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