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1 Introduction. 1 Perception and Reality “What is real? How do you define real? If you’re talking about what you can feel, what you can smell, what you.

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Presentation on theme: "1 Introduction. 1 Perception and Reality “What is real? How do you define real? If you’re talking about what you can feel, what you can smell, what you."— Presentation transcript:

1 1 Introduction

2 1 Perception and Reality “What is real? How do you define real? If you’re talking about what you can feel, what you can smell, what you can taste and see, then real is simply electrical signals interpreted by your brain. This is the world that you know.” —Morpheus’ answer to Neo in The Matrix, 1999 Compare the ‘Brain in a Vat’ scenario, e.g. Arnold Zuboff’s ‘The Story of a Brain’

3 1 Some Themes 1. Perception as the construction of a model of the environment 2. Why do we take perception for granted? 3. Nature of the model constructed in perception: a symbolic description (or symbolic representation) 4. Perception really is a difficult and impressive accomplishment: 5. The physiological approach to perception: the perceptual process as a causal chain 6. Parallel approaches to perception from studies of visual phenomena or visual performance (Psychophysics) and from physiology and anatomy 7. Perception as an Inverse Problem 8. Marr’s three levels of Analysis: Hardware, Algorithmic, Computational…

4 1 Some Themes 1. The world as we perceive it is an internal neural representation or model, that we construct and rely on.

5 1 Some Themes: 2. Why do we take perception for granted? If the model is to be useful we have to take it for real: that’s why we evolved the ability to make it. Perceptual ability is universal: not very much individual variation Perception generally requires no conscious effort, unlike other challenging cognitive accomplishments

6 1 Some Themes 3. Nature of the model constructed in perception: a symbolic description (or symbolic representation) A point-to-point projection, preserving the information in the stimulus, is NOT enough (homunculus fallacy) Example of a better approach: ‘bug detectors’

7 1 Some Themes 4. Evidence that perception really is a difficult and impressive accomplishment: It takes up nearly half the brain for vision alone Infants spend the first year of life mainly learning it Engineers can barely achieve even the most primitive symbolic description with artificial vision systems

8 1 Early Philosophy of Perception Plato’s “The Allegory of the Cave” (380 BCE)

9 1 Some Themes 5. The physiological approach to perception: the perceptual process as a causal chain Phantom limbs Phosphenes

10 1 Descartes and Phantom Limbs Descartes: Reflex arc for action vs Consciousness (Pineal )

11 1 Phosphenes Tapping in at an intermediate stage of processing: A visual prosthesis—can use retina, optic nerve or cortex Compare auditory or visual ‘phosphenes’

12 1 Some Themes 5. The physiological approach to perception: the perceptual process as a causal chain - Phantom limbs - Phosphenes 6. Parallel approaches to perception from studies of visual phenomena or visual performance (Psychophysics) and from physiology and anatomy –Why these should yield a consistent understanding 7. Perception as an Inverse Problem - Assumptions/natural constraints: Ames room, etc. 8. Marr’s three levels of Analysis: - Computational, Algorithmic, Hardware

13 Without binocular vision, projection still happens… But now it requires solution of an inverse problem: What object size and distance produced the given image? Given one value (image size), we must infer two. The mapping from objects to images is many to one. The restoration of a definite object distance in perception resolves some of this uncertainty: -- correctly if the inverse problem is solved correctly -- incorrectly otherwise A rational way to attack the inverse problem: --Choose the most plausible of the alternatives. --In a Bayesian framework, this would simply be the most statistically likely, given the context as we know it. [Although: Hoffman says this is circular logic] --Example: in Chapter 2, assume uniformity of texture density in order to compute tilt and slant from the pattern of non-uniformity in the image 7. Perception as an Inverse Problem An example: Use of assumptions/natural constraints (Ames room, etc.)

14 7. Perception as an Inverse Problem - Use of assumptions about natural constraints: Ames room(below) Other constraints: Chapter 2 (texture uniformity constraint), etc…

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17 Projection (Kepler 1611): 1 world ->2 images -> 1 perceived object (projected into 3D) Binocular disparity can fix distance: Descartes 1637 (stick analogy): Briggs, 1676 Barlow et al., 1968, etc. Partial decussation: Newton, 1682 (pub. 1704) Are not the Species [images, in modern terminology] of Objects seen with both Eyes united where the optick Nerves meet before they come into the Brain, the fibres on the right side of both Nerves uniting there, and after union going thence into the Brain in the Nerve which is on the right side of the Head [the right optic tract, in present usage], and the fibres on the left side of both Nerves uniting in the same place, and after union going into the Brain in the Nerve [optic tract] which is on the left side of the Head, and these two Nerves meeting in the Brain in such a manner that their fibres make but one entire Species or Picture, half of which on the right side of the Sensorium comes from the right side of both Eyes through the right side of both optick Nerves to the place where the Nerves meet [chiasm], and from thence on the right side of the Head into the Brain, and the other half on the left side of the Sensorium comes in like manner from the left side of both Eyes. For the optick Nerves of such Animals as look the same way with both Eyes (as of Men, Dogs, Sheep, Oxen, &c.) meet before they come into the Brain, but the optick Nerves of such Animals as do not look the same way with both Eyes (as of Fishes and of the Chameleon) do not meet, if I am rightly informed. Newton, 1682

18 When perceptual constancy is achieved, instead of failing as it does in the Ames room, the result is a closer correspondence, or isomorphism, between the state of the external environment and the perceptual impression of it—(closer than the correspondence between the external environment and the sensory stimulus through which we know it.) Action depends on such a simple and orderly mapping, eg x,y vs pixel #; (Hayek, Kohonen); pointing; Frog computations to control action --Prediction --Generalization --Example of pitch and frequency (and period, and…) Failure of perceptual isomorphism in intermediate representations: Perception as a model, with afferent neural representation as data, which needn’t look like the model, just as a scientific theory should be a model of reality (and therefore isomorphic with reality in some sense), yet neither the theory nor the reality it describes has any similarity to the data as such (vectors of numbers, marks on paper, etc). A different point of view: Don Hoffman….

19 8. Marr’s three levels of Analysis: - Computational, Algorithmic, Hardware

20 A Hymn to the 3 levels of analysis: “Onward, Marrian soldiers” Onward, vision scientists, Marching on with Marr; Algorithmic theories Beckon from afar. Leave aside the neurons Filling up your head; Think of computations, They will do instead. For those computations, From their hardware free’d, Form the firm foundation Of our noble creed. Fear no contradiction While Marr leads the way; Views of such abstraction Few will dare gainsay. Armed with zero-crossings,  2 G our shield, We can spread confusion O’er the battlefield. With its special concepts Deep and broad and true Marr’s approach is surely Good enough for you. Refrain: Onward, vision scientists, Marching on with Marr. Algorithmic theories Beckon from afar Text: Sabine Baring-Gould, Music: Arthur S. Sullivan, Textbook reference: Preface, page xi and Ch2, p38


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