Transcript Human Information Processing - Cognitive - ppt

The Human
Processing and Memory
Human Computer Interaction, 2nd Ed.
Dix, Finlay, Abowd, and Beale
Chapter 1
Model Human Processor + Attention
Recall, “purely and engineering abstraction”
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Sensory store
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Perceptual processor
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Recognizes symbols, phonemes
Aided by LTM
Cognitive processor
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Rapid decay “buffer” to hold
sensory input for later processing
Uses recognized symbols
Makes comparisons and
decisions
Problem solving
Interacts with LTM and WM
Motor processor
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Input from cog. proc. for action
Instructs muscles
Feedback
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Results of muscles by senses
Attention
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Allocation of resources
Overview
• Will look at elements of human information processing from a slightly
different orientation than “engineering abstraction”
• A bit more fine grained analysis, following from psychological studies
– But, it is these psychological studies from which the “engineering
abstraction” is derived
• 3 stage model of human memory
– Iconic buffer, STM, LTM
• Models of LTM
• Reasoning
• Problem solving
Model Human Processor + Attention
Recall, “purely and engineering abstraction”
•
Sensory store
–
•
Perceptual processor
–
–
•
Recognizes symbols, phonemes
Aided by LTM
Cognitive processor
–
–
–
–
•
Rapid decay “buffer” to hold
sensory input for later processing
Uses recognized symbols
Makes comparisons and
decisions
Problem solving
Interacts with LTM and WM
Motor processor
–
–
–
Input from cog. proc. for action
Instructs muscles
Feedback
•
•
Results of muscles by senses
Attention
–
Allocation of resources
3-Stage Model of Human Memory
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Sensory (here, iconic) memory – “very” short term memory
– lasts 1-2 seconds, infinite capacity
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Short-term memory (Working memory)
– lasts ~ 18 seconds, holds 1.75 (7+/-2 items)
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Long-term memory
– infinite capacity; short of damage is permanent
– Recall vs. Recognition (Remember vs. Know)
• Retrieval cues
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Will demonstrate later in class …
http://www.if.uidaho.edu/~marbjm/class%202.pdf
“Executive” - Attention
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Central “executive” controls tasking
– Pays, or allocates, attention
– Bandwidth of attention is limited
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Tasks that require the same resources interfere with one another
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Attention is both a low-level and high-level property of vision
http://www.if.uidaho.edu/~marbjm/class%202.pdf
Sensory Memory:
“Very” Short Term Memory
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Sensory buffers for stimuli received through senses
– iconic memory: visual stimuli
– echoic memory: aural stimuli
– haptic memory: tactile stimuli
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Examples
– “sparkler” trail
– stereo sound
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Continuously overwritten – demo follows
A Test – of Visual Iconic Memory
• Will present figure briefly (~1/2 second)
• Try to remember as many elements as you can
• Write them down
The Phenomenon
• After presentation, did you continue to “see” the
items?
– Some purely physiological based “seeing”:
• Afterimage
• Bleaching of pigments
• “bright, or colored, stuff”
– But also, there is a more “memory-based” image
(process further downstream in memory system)
• Iconic memory
• “dark, or veridical, stuff”
• Reading from the iconic buffer
Reading from the Iconic Buffer, 1
• Typically can list 3 – 7 items named
• Short lived visual, or iconic, buffer
– holds the image for a second or two
• Read images and place in STM
– 3-stage model
Set of miscellaneous symbols
• Can get about 5-7 items until run out of
short term (working) memory capacity
• Limitation of 5-7 comes from:
– Decay of iconic memory
– Rate can read from visual buffer
– Capacity of working memory
• In each fixation between saccadic eye
movements, image of world captured
Useful Visual
Field of View
Visual
Search or
Monitoring
Strategy
Eye
Movement
Control
Reading from the Iconic Buffer, 2
• Again, Limitation of 7 comes from:
– Decay of iconic memory
– Rate can read from visual buffer
– Capacity of working memory
• From each image,
– brain must identify objects,
– match them with objects previously
perceived, and
– take information into working memory for
symbolic analysis
• Search light model of attention (for vision)
– Visual information is acquired by pointing
fovea at regions of visual field that are
interesting
– Then using a scanning process in which
objects are read from an image buffer from
more extensive processing
Set of miscellaneous symbols
Useful Visual
Field of View
Visual
Search or
Monitoring
Strategy
Eye
Movement
Control
Attention
• Spotlight metaphor
– Spotlight moves serially from one input channel to another
– Can focus attention (and perceptual processor) on only one input channel at a
time
• Location in visual field, voice in auditory field, …, anything
• Visual dominance:
– Easier to attend to visual channels than auditory channels
• All stimuli within spotlighted channel are processed in parallel
– Whether you want to or not
– Can cause “interference” - demo
Say the Colors of the Words
• Easy enough – didn’t take too long
Say the Colors of the Words
• Took longer … Stroop effect
• For design:
– Choose secondary characteristics of display to reinforce message
Again, Human Memory Stages
• Sensory (here, iconic) memory
– lasts 1-2 seconds, infinite capacity
• Short-term memory (Working memory)
– lasts ~ 18 seconds, holds 1.75 (7+/-2 items)
• Long-term memory
– infinite capacity; short of damage is permanent
– Recall vs. Recognition (Remember vs. Know)
• Retrieval cues
http://www.if.uidaho.edu/~marbjm/class%202.pdf
Short-Term Memory (STM)
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“Scratch-pad” (or buffer) for temporary recall
– rapid access ~ 70ms
– rapid decay ~ 200ms
– limited capacity - 7± 2 chunks
• Chunking, recoding, etc.
– affects amount of information retained, entering LTM
Example - Chunking
HEC ATR ANU PTH ETR EET
Long-term Memory (LTM)
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Repository for all our knowledge
– slow access ~ 1/10 second
– slow decay, if any
– huge or unlimited capacity
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Episodic and semantic memory
– Episodic (episodes): Serial memory of events
– Semantic (“meanings”): Structured memory of facts, concepts, skills
• Also, procedural and declarative memory
– “Processes” vs. “facts”
LTM – Models of Semantic Memory
• Semantic memory structure
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Contains LTM knowledge of world
Provides access to information
Generic knowledge -- specific details lost
Represents relationships between bits of information
Important for rule-based behavior
• Supports inference
• Many models, theories, accounts, schemata proposed
• Semantic network model (example next slide):
– E.g., Inheritance – child nodes inherit properties of parent nodes
– Relationships between bits of information explicit
– Supports inference through inheritance
• Other Models (examples follow):
– Scripts, frames, production rules
Early Model of Semantic Memory
Collins and Quillian
• Collins & Quillian’s Teachable
Language Comprehender
• Semantic memory is organized
as a network of interrelated
concepts
• Each concept is represented as
a node
• Concepts are linked together by
pathways
• Economy of representation
• Activation of one concept
spreads to interconnected nodes
• Remind you of anything from
computer science?
Early Model of Semantic Memory
Collins and Quillian
• Collins & Quillian’s Teachable
Language Comprehender
Early Model of Semantic Memory
Collins and Quillian
• Spreading Activation
• Working memory is activated
LTM
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When a concept becomes
active, activation spreads to all
other interconnected nodes
• Activation spreads to all related
nodes
• How do you evaluate sentences
like “Is a robin is an animal”?
Early Model of Semantic Memory
Collins and Quillian
• Spreading Activation
• Activation spreads from each of
the concept nodes (Robin &
Animal)
• When two spreading activations
meet, an intersection is formed
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Robins ==> BIRD <== Animals
• If no intersection, relatively fast
no
• If intersection, decision stage
operates to determine if sentence
is valid
Is a robin an animal?
Tests of Spreading Activation
• Sentence verification task
– Time to respond yes or no
• Takes time for activation to
spread
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Greater distances ==> longer RT
• Verification time for items 0, 1,
and 2 links
But, it’s not that simple ...
• E.g. typicality effects - how
many links separate:
– A canary is a bird?
– A robin is a bird?
– A chicken is a bird?
– An ostrich is a bird?
• But, RT varied - less typical
birds took longer than more
typical birds
FYI - Semantic Relatednes and
Semantic Priming
• Semantic relatedness
– Spreading activation between related concepts
– Activation of one concept partially activates
semantically related concepts
• Semantic priming
– Stimulus 1 ==> Stimulus 2
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(Prime) ==> (Probe)
– Test spreading activation by manipulating
semantic relationship between prime & probe
• Concepts linked by spreading activation
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Prime:
Doctor
Bread
Doctor
Bread
Probe:
Nurse
Butter
Butter
Nurse
Sometimes prime facilitates processing
FYI - Semantic Relatednes
• Recall, semantic
relatedness
– Spreading activation
between related concepts
– Activation of one concept
partially activates
semantically related
concepts
• So, can focus on
relatedness, without
explicitly indicating links
Spreading Activation - Fin
• Semantic priming commonplace
– Can exploit in design
– Indeed, in design can exploit all information about how human operates
• Spreading activation is thought to be automatic
• Governed by data-driven aspects of processing
• How do expectancies affect semantic access?
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Automatic vs Conscious Strategies (attentional)
Fast vs Slow
Effortless vs Effortful
Benefits vs Costs & Benefits
Fyi – Another semantic network
Models of LTM – Frames, or Schemata
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Information organized in “memorial data
structures”
Schemata
– Stored frameworks or body of knowledge
– Conceptual framework for interpreting
information
– Biased information processing to relate new
material to what we already know
– Alters way we perceive things
– Individual differences in perception and memory
• Frames
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Slots in structure instantiated with values for
instance of data
Type–subtype relationships
COLLIE
Fixed
breed of: DOG
type: sheepdog
Default
size: 65 cm
Variable
colour
DOG
Fixed
legs: 4
Default
diet: carniverous
sound: bark
Variable
size:
colour
Models of LTM - Scripts
• Model of stereotypical information required to interpret situation
• Script has elements that can be instantiated with values for context
Script for a visit to the vet
Entry conditions: dog ill
vet open
owner has money
Result:
dog better
owner poorer
vet richer
Props:
examination table
medicine
instruments
Roles:
vet examines
diagnoses
treats
owner brings dog in
pays
takes dog out
Scenes:
arriving at reception
waiting in room
examination
paying
Tracks:
dog needs medicine
dog needs operation
Models of LTM - Production Rules
• Representation of procedural knowledge.
• Condition/action rules
if condition is matched
then use rule to determine action.
IF dog is wagging tail,
THEN pat dog
IF dog is growling,
THEN run away
LTM - Storage of information
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LTM much studied in psychology:
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Rehearsal
– information moves from STM to LTM
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Total time hypothesis
– amount retained proportional to rehearsal time
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Distribution of practice effect
– optimized by spreading learning over time
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Structure, meaning and familiarity
– information easier to remember
LTM - Forgetting
• Decay
– information is lost gradually but very slowly
• Interference
– new information replaces old: retroactive interference
– old may interfere with new: proactive inhibition
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So, ... may not forget at all, memory is selective …!
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Also, affected by emotion – can subconsciously `choose' to forget
LTM - Retrieval
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Should be familiar from heuristics ...
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Recall
– information reproduced from memory can be assisted by cues,
e.g. categories, imagery
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Recognition
– information gives knowledge that it has been seen before
– less complex than recall - information is cue
Thinking – Cognitive Processing
• Humans reason, process information, like, well, humans
– Recall, any theory is an abstraction and, thus, captures some elements of
phenomenon, and misses others
– Question is …
• Is the account (theory, model) useful in the context and for the purpose for which it is
used?
Thinking – Cognitive Processing
• Humans reason, process information, like, well, humans
– Recall, any theory is an abstraction and, thus, captures some elements of
phenomenon, and misses others
– Question is …
• Is the account (theory, model) useful in the context and for the purpose for which it is
used?
• Basic forms of reasoning, or, forming inferences, are useful in
understanding broad outlines of human cognition
– Deduction
– Induction
– Abduction
• Problem solving
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Gestalt
Problem Space
Analogy
Skill acquisition
Reasoning
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Deduction:
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derive logically necessary conclusion from given premises
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e.g., If it is Friday, then she will go to work - It is Friday, therefore she will go to work
Logical conclusion not necessarily true:
e.g., If it is raining, then the ground is dry - It is raining, therefore the ground is dry
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Induction:
– Generalize from cases seen to cases unseen
• e.g., All elephants we have seen have trunks - therefore all elephants have trunks.
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Unreliable (but useful):
• Can only prove false not true
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Abduction:
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Reasoning from event to cause
e.g., Sam drives fast when drunk.
If I see Sam driving fast, assume drunk.
Unreliable:
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can lead to false explanations
FYI - Induction vs. Deduction
• Induction: Make observations first, then draw
conclusions
– Organized data survey (structured analysis,
visualization) of raw data provide basis for interpretation
process
– Interpretation process will produce knowledge that is
being sought
– Experience of individual scientist (observer) is crucial
– Important: selection of relevant data, collection method,
and analysis method
– Data mining is an important knowledge discovery
strategy
• ubiquitious data collection, filtering, classification, and focusing is
crucial
• Deduction: Formulate hypothesis first, then test
hypothesis
– Via experiment and accept/reject
– Data collection more targeted than in induction
– Only limited data mining opportunities
Mueller, 2003
Problem Solving
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Process of finding solution to unfamiliar task using knowledge
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Complex, time consuming process
Selections not immediately obvious
May require many steps
May involve insight
May use analogy
Solutions often counterintuitive
Several theories, or accounts
Gestalt
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Problem solving both productive and reproductive
Productive draws on insight and restructuring of problem
Attractive but not enough evidence to explain “insight” etc.
Move away from behaviourism and led towards information processing theories
• Others: Insight,
Functional fixedness, Analogy
Problem Solving Cycle
• One schema – consider “task performance”
Insight
• Early - Kohler (a Gestalt psychologist) in Canary islands in WWI
– Studied problem solving in chimpanzees
– Sultan and the Banana:
• Learned how to get banana with longer pole
• Then given shorter poles that wouldn’t reach
• Flash of “insight”, Sultan put the poles together
• Sudden perception of useful or proper relations
– Solutions will sometimes “spring to mind”
– Pieces fall into place
• First attempts to solve don’t work
– Production hindered by unwarranted assumptions
– Insight occurs when the assumption is removed
Strayer, Utah: http://www.psych.utah.edu/psych3120classroom/
Problem solving (cont.)
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Problem space theory
– Problem space comprises problem states
– Problem solving involves generating states using legal operators
– Heuristics may be employed to select operators
e.g. means-ends analysis
– Operates within human information processing system
e.g. STM limits etc.
– Largely applied to problem solving in well-defined areas
e.g. puzzles rather than knowledge intensive areas
Problem solving (cont.)
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Analogy
– Analogical mapping:
• novel problems in new domain?
• use knowledge of similar problem from similar domain
– Analogical mapping difficult if domains are semantically
different
• Skill acquisition – e.g., “expert” performance
– Skilled activity characterized by chunking
• lot of information is chunked to optimize STM
– Conceptual rather than superficial grouping of problems
– Information is structured more effectively
Individual Differences
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Long term
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Short term
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Changing
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Dix says ask:
– Sex, physical and intellectual abilities
– Effect of stress or fatigue
– Age
– Will design decision exclude section of user population?
– (or, more generally) How does design differentially affect sections of the
population?
– i.e., Universal Usability
The “Model Human Processor + Attention” is
Similar to Ware (2004) Model
• We’ll look at one more
model of cognitive (and
visual) processing
– All are in fact much the same,
but focus on different goals
The “Model Human Processor + Attention” is
Similar to Ware (2004) Model
• We’ll look at one more
model of cognitive (and
visual) processing
– All are in fact much the same,
but focus on different goals
• Card et al. model was
developed in context of
predicting user
performance
– E.g., set parameters and
perform simulation
• Ware’s model includes
much the same elements,
but focuses on those which
are most relevant for
processing of visual
information in context of
task performance
The “Model Human Processor + Attention” is
Similar to Ware (2004) Model
•
Sensory store
–
•
Perceptual processor
–
–
•
Recognizes symbols, phonemes
Aided by LTM
Cognitive processor
–
–
–
–
•
Rapid decay “buffer” to hold
sensory input for later processing
Uses recognized symbols
Makes comparisons and
decisions
Problem solving
Interacts with LTM and WM
Motor processor
–
–
–
Input from cog. proc. for action
Instructs muscles
Feedback
•
•
Results of muscles by senses
Attention
–
Allocation of resources
A Model of Perceptual Processing
Quick Overview
What we do is design information displays!
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An information processing (the dominant paradigm) model
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“Information” is transformed and processed
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Gives account to examine aspects important to visualization
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In spirit of visualization as evolving discipline, yet to develop its theories, laws, …
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Physical light does excite neurons, but at this “level of analysis” consider information
Here, clearly, many neural subsystems and mapping of neural to ip is pragmatic
Stage 1: Parallel processing to extract low-level properties of the visual scene
Stage 2: Pattern perception
Stage 3: Sequential goal-directed processing
Stage 1: Parallel Processing to Extract Low-level
Properties of Visual Scene
• (Very first) neurons fire
• Visual information 1st processed
by
– large array of neurons in eye
– primary visual cortex at back of brain
• Individual neurons selectively
tuned to certain kinds of
information
– e.g., orientations of edges or color of
light
– Evoked potential experiments
• In each subarea large arrays of
neurons work in parallel
– extracting particular features of
environment (stimulus)
Stage 1: Parallel Processing to Extract Low-level
Properties of Visual Scene
• At early stages, parallel
processing proceeds involuntarily
– Largely independent of what choose
to attend to (though not where look)
• Is rapid,
– If want people to understand
information fast, should present in
way so is easily detected by these
large, fast computational systems in
brain
• Stage 1 processing is:
– Rapid and parallel
– Entails extraction of features,
orientation, color, texture, and
movement patterns
– “transitory”, only briefly held in iconic
store
– Bottom up, data-driven
Stage 2: Pattern Perception
• Rapid processes
• Divide visual field into regions and
simple patterns, e.g.,
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Continuous contours
Regions of same color
Regions of same texture
…
• “Active”, but not conscious
processes
• Specialized for object recognition
– Visual attention and memory
• E.g., for recognition must match
features with memory
– Task performing will influence what
perceived
– Bottom up nature of Stage 1, influenced
by top down nature of Stage 3
Stage 2: Pattern Perception
• Specialized for interacting with
environment
– E.g., tasks involving eye-hand coordination
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“Two-visual system hypothesis”
– One system for locomotion and eye-hand
coordination
• The “action system”
– One system for symbolic object
manipulation
• The “what system”
• Characteristics:
– Slow serial processing
– Involvement of both working (vs. iconic)
and long-term memory
– Both bottom up and top down
• More emphasis on arbitrary aspects of
symbols than Stage 1
• Top-down processing
– Different pathways for object recognition
and visually guided motion
Stage 3: Sequential Goal-Directed Processing
• At highest level of perception are
the objects held in visual memory
by demands of active attention
• To use an external visualization,
we construct a sequence of visual
queries that are answered through
visual search strategies
• Only a few objects can be held at a
time
• They are constructed from
available patterns providing
answers to the visual queries
Stage 3: Sequential Goal-Directed Processing
• They are constructed from
available patterns providing
answers to the visual queries
• E.g., if use a road map to look for a
route, the visual query will trigger a
search for connected red contours
(representing major highways)
between two visual symbols
(representing cities)
• Are other subsystems, as well
– Visual object identification process
interfaces with the verbal linguistic
subsystems of the brain so that words
can be connected to images
– The perception-for-action subsystem
interfaces with the motor systems that
contril muscle movements
End
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