Chapter 2: Modeling mental imagery
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Transcript of Chapter 2: Modeling mental imagery
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Chapter 2:Modeling mental imagery
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
The ingredients
•Encountered some of the basic ideas feeding into cognitive science
• move away from associationist models of learning and behavior
• information theory as a tool for exploring the nature and limits of cognitive abilities
• development of “boxological” accounts of how cognitive tasks can be performed
• theory of computation as a model for information-processing
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Putting them together: 3 case studies
• Terry Winograd and SHRDLU [TODAY]
• The imagery debate [MONDAY]
• Marr’s theory of vision [WEDNESDAY]
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Earlier themes
•The nature of mental representation
– Miller and chunking information-processing depends on how information is coded
– Winograd and procedural semantics representation of “knowledge” in terms of algorithmic routines
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Common assumptions about information
•Information is amodal
• Miller’s suggestion that the sensory systems all have the same channel capacity
•Information is coded in a digital/propositional format
• based on the formal languages used to program computers
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Digital information-coding
•Information is coded in a format that has the basic properties of a language
• Basic constituents are individual symbols
• Compositionality – complex structures are built up from individual symbols according to formation rules
• Arbitrary connections between symbolic structures and what they represent
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Digital information-processing
•The model for thinking about digital information-processing are formal languages (e.g. logical languages and computer programming languages)
•Model information-processing on, e.g.
• proofs in logical languages
• implementation of instructions in a production system
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Imagistic information-coding
•Non-symbolic: images are not built up from basic elements
•Not compositional
• The parts of images cannot reoccur in other images
• No rules for building up images from their parts
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Representation in images
•Representation depends upon systematic correlation between properties of representation and properties of what it represents
• pictorial depiction depends upon resemblance
• can be schematic resemblance, as in a map
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Tricky issues
•Imagistic representations can exploit symbols (e.g. maps)• need to distinguish between the representation
and the labeling of the representation
•Imagistic representations ≠ analog representations• a representation is analog just if it permits
continuous variation• there are examples of analog representartions
that are not imagistic and imagistic representations that are not analog
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Imagistic information-processing
•The real issue comes with how information is extracted from imagistic representations
• scanning images
• manipulating images (e.g. rotation)
•Certain types of information are much easier to extract from images than from digital representations
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
The issues for cognitive science
•Is information always encoded in a digital format - or are there cases of imagistically encoded information?
•How can we explore this experimentally?
• By looking at how subjects carry out information-processing tasks involving images
• Seeing whether their behavior provides indirect evidence that they are scanning/manipulating images
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Brooks 1968
F• Form a memory image of a capital F• Trace around the image, starting at the bottom left corner and working clockwise• Indicate for each corner whether it is on a top edge of the figure
• Performance is impaired when responses are made visually (i.e. by pointing to the word ‘Yes’), rather than by saying ‘yes’
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Cooper 1975
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Scanning mental images
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
The strong interpretation
• Subjects perform the task by rotating/scanning mental images in their “mind’s eye”
• The process of mental rotation/scanning has is structurally similar to physical processes of rotation/scanning
• Seems to match evidence from introspection
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Problems with the strong interpretation
• Dennett’s “Cartesian theatre”
• Who or what is doing the scanning/rotating?
• Where is the image projected?
• Threat of regress if we take the metaphor of the “mind’s eye” literally
• Not clear how these mental images relate to “phenomenal images”
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Kosslyn’s theory
• Develops metaphor of images as spatial displays on cathode ray tube
• Mental images are temporarily generated from propositionally encoded information in long-term memory
• Mental images “projected” onto visual buffer (which is where perceptual representations also appear)
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Solving a problem
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Ambiguity
• Personal-level phenomena• phenomenal images• conscious experience of the world• accessible to introspection (not always reliable)
• Subpersonal information-processing explains our personal-level phenomena and abilities