Chapter 2:
Modeling mental imagery
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
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]
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Earlier themes
•The nature of mental representation
– Miller and chunking  informationprocessing depends on how information is coded
– Winograd and procedural semantics 
representation of “knowledge” in terms of
algorithmic routines
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
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
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
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
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
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
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
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
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’
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Cooper 1975
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 José Luis Bermúdez / Cambridge University Press 2010
Scanning mental images
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
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”
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)
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Solving a problem
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
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010