Transcript Human Information Processing - Sensory - ppt

The Human
Introduction, Modeling, Vision
Human Computer Interaction, 2nd Ed.
Dix, Finlay, Abowd, and Beale
Chapter 1
About group presentations
• In general, quite good – will be even better after having seen others
and with this feedback!
• 2 screens worked well
• Keep any intro to site short
• Need to focus on the interface to the underlying system model
– E.g., always ways to improve business model, but that’s not the interface’s fault!
– Critique of interface, not, e.g., business model
• Organize, so, e.g., base flow of presentation on a particular
heuristic, vs., e.g., flow of task
• Keep time to 12 minutes
– Practice to see how long is
About group presentations
• Keep time to 12 minutes
– Practice to see how long is
• Addressing each of the, e.g., 10 Nielsen, heuristics may not be best
approach, as become “hectic”
– Better to note in passing many, but focus on few to show depth of your analysis
• Consider the view of your displays from perspective of audience
– Visual fatigue is acute after a full day, then the presentation
– E.g., essentially do not scroll, spend less than ~20-30 seconds on a screen or
slide, etc.
Introduction
Recall from discussion of theories
• About IT, HCI and humans …
• Interaction captured in figure
below with “system, task, user”
– So far, have talked about
“interface design” of system
• Guidelines, principles,
evaluation, etc.
System
• Now will look at user
– Will consider “model” of human
– Models capture elements of
whatever is being modeled that
are relevant to some endeavor
• E.g., “cognitive engineering”
Task
and its representation
(possibly manipulable)
“feedback”
User
FYI - Introduction
Recall from discussion of theories
• On describing humans
– To understate, … a question of long standing …
• One of the big questions …
– Has been and can be done in various ways and at different levels of analysis
• Moral, spiritual, …., psychological, … physiological, … chemical
• Reductionist
• Psychology focuses on the individual
• Many orientations through psychology’s short history
– Note: Clinical/personality orientations, e.g., Freud, Jung, have their own utility in
explanation, but are rarely subject to scientific investigation
– Structural, turn of 20th century, relations among elements
– Gestalt, 1930’s and on, general, especially of perception, still useful
– Behaviorist, all S->R, which is fine for some things, but fall short for explanation
of mental phenomena
– Information processing/Cognitive, current orientation
• Influences from Shannon’s ideas on information, shortcomings of behaviorism,
successes in codifying information in computing
• “Human information processor”
Modeling Humans
• Any theory or model is an abstraction
– For HCI, goals are primarily in “Computer” and “Interaction”
– Utility of human model lies in how well it helps with interfaces
• Card, Moran, and Newell (1983, 1986) Model Human Processor
– “Classic” example of cognitive architecture - focus on
humans interacting w/ computers
• Perceptual system
• Motor system
• Cognitive system
– Each has own processor and memory
– Principles of operation dictate system behavior under
certain conditions
– A very simple model
• Dix et. al use similar information processing
division of elements in chapter
Overview
•
Dix et al. model:
1. Information i/o …
• visual, auditory, haptic, movement
2. Information stored in memory
• sensory, short-term, long-term
3. Information processed and applied
• reasoning, problem solving, skill, error
•
Idea tonight
– Give broad overview of elements of human
– Show that for some elements of user interface design, detailed knowledge is at
least useful and perhaps critical
• For design - sensory, perceptual, and cognitive guidelines and
principles
Some Terminology
Recall
Perception
Sensation
•
Transduction of
energy, etc. by
sensory receptors
–
•
E.g., light to
neural impulses
by retinal cells
Forming a “mental
image” or
awareness and
representation
•
•
To “see a
window”
“Top down” and
“bottom up” process
•
Demo, next slide
Cognition
•
Latin –
•
•
•
cognitio – knowledge
“Higher level” mental
representations and
procedures
Process of thought
What’s below?
Demo
Model Human Processor + Attention
Overview (detail next slide)
• A “useful” big picture – Card et al. ’83, ‘86 … plus attention
– Senses/input  f(attention, processing)  motor/output
– Notion of “processors”
• Purely an engineering abstraction
• Detail next slide
– And in lectures!
Model Human Processor + Attention
Detail
• Sensory store
–
Rapid decay “buffer” to hold
sensory input for later processing
• Perceptual processor
–
–
Recognizes symbols, phonemes
Aided by LTM, cf. demo
• Cognitive processor
–
–
–
–
Uses recognized symbols
Makes comparisons & 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
Model Human Processor – Original
Overview – visual – all next slide
• Card et al. ‘83, ‘86
• An architecture with
parameters for cognitive
engineering …
• E.g., memory properties
–
–
–
d: Decay time: how long memory lasts
m: Size: number of things stored
k: Encoding: type of things stored
Model Human Processor – Original
Parameters for each system
• Memory properties
–
–
–
d: Decay time: how
long memory lasts
m: Size: number of
things stored
k: Encoding: type of
things stored
Dix: Information Input and Output
• Dix et al. – slightly different foci
– But, again, human considered as
information processor
• Input channels are the five
senses
– With some more important than
others …
– For hci, vision primarily
• Output channels are human
effectors
– E.g., limbs, fingers, head, vocal
system
Vision
Vision
• Vision and visual perception studied across a range of disciplines
• Points tonight meant to highlight usefulness for HCI in knowing
about vision
• For vision consider (in Dix’s terms):
– 1. Physical reception of stimulus
– 2. Processing and interpretation of stimulus
Eye - Physical Reception of Stimuls
•
Eye - Mechanism for receiving light
and transforming it into nerve
transmissions
– A transducer: light -> nerve “firings”
– Actually, patterns of pulses
•
Light reflects from objects
– Some strikes retina
– Images focused upside-down on
retina!
• Perception vs. Sensation!
•
Ganglion cells (brain!) detect pattern
and movement
•
As camera, has
– equivalent of lens + aperture (pupil)
– and film (retina)
Environment: Visible Light
•
Generally, the body’s sensory
system is the way it is because it
had survival value
– Led to success
• survival and reproduction
•
Here, vision (because of computer),
but all senses share basic notions
•
Humans have receptors for (a
small part of) electromagnetic
spectrum
– Receptors sensitive to (fire when
excited by) energy 400-700nm
– Snakes “see” infrared, some
insects ultraviolet
• i.e., have receptors that fire
– What would it be like if humans
could see other parts of
electromagnetic spectrum?
Environment: Visible Light
•
Generally, the body’s sensory
system is the way it is because it
had survival value
– Led to success
• survival and reproduction
•
Here, vision (because of computer),
but all senses share basic notions
•
Humans have receptors for (a
small part of) electromagnetic
spectrum
– Receptors sensitive to (fire when
excited by) energy 400-700nm
– Snakes “see” infrared, some
insects ultraviolet
• i.e., have receptors that fire
– What would it be like if humans
could see other parts of
electromagnetic spectrum?
Human Eye: Retinal Receptors
•
Two types of (photo) receptors on retina: rods and cones
– Rods look like, well, rods …
– Will later look at color blindness … when cones fail
•
Rods:
–
–
•
Spread all over the retinal surface (75 - 150 million)
Low resolution, no color vision, but very sensitive to low light (scotopic or dim-light vision)
Cones:
–
–
Dense array around central portion of retina - fovea centralis (6 - 7 million) – more later
High-resolution, color vision, but require brighter light (photopic or bright-light vision)
FYI - Human Eye: Compound Lens
Cornea + Lens
• Eye has compound lens:
– cornea (power) and lens (adjust focal length)
f = focal length of lens
d = distance to object
r = distance to image that is formed
– Flexibility of lens changes with age, approaching 0 at 60 years
Depth of Field – Photographic Example
• Photographic Images:
– Depth of field – that part of picture that is “in
focus
– Depth of field longer with small aperture
(numerically higher f stop)
– Range of focus:
Distance
50 cm
1m
2m
3m
Near
Far
43 cm
75 cm
1.2m
1.5m
60 cm
1.5 m
6.0m
Infinity
Depth of focus
17 cm
75 cm
1.8 m
Large
Human Eye: Depth of Field and Focus
• Depth of focus (will see interesting effect)
– Distance over which objects are “in focus” without change in lens (focus)
• Note: Different colors have different depths of focus
• Varies with size of pupil
– Range of focus:
Distance
50 cm
1m
3m
Near
Far
43 cm
75 cm
1.5m
60 cm
1.5 m
Infinity
Depth of focus
17 cm
75 cm
Large
– Rarely do computer systems model either depth of field or depth of focus
Human Eye: Chromatic Aberration
Human Eye: Chromatic Aberration
•
Different wavelengths of light focus
at different distances within eye
– Short-wavelength blue light refracted
(bent) more than long-wavelength red
light
– Focusing on a red patch, an adjacent
blue patch will be significantly out of
focus
•
Strong illusory depth effects
•
Human eye has no correction for
chromatic aberration!
– Visual effects in soap bubbles, crystal
sculptures, etc.
Using Physiology for Design
• Chromatic Aberration
• Different wavelengths focus differently
– Highly separated wavelengths (red & blue) can’t be focused simultaneously!!
• Guideline: Don’t use red-on-blue text
– It looks fuzzy and hurts to read
Interpreting the Visual Signal
Interpreting the Visual Signal
Some terms
•
Size and depth
– Visual angle indicates how much of view object occupies
(relates to size and distance from eye)
– Familiar objects perceived as constant size
(in spite of changes in visual angle when far away)
– Cues like overlapping help perception of size and depth
• Visual acuity is ability to perceive detail (limited)
•
Brightness
– Human perceptual system does not map directly to physical
•
Color
– Different sensory elements responsible for perception of color
– Implications for color use, e.g., color blindness
Human Eye: Visual Angle
• Visual angle - angle subtended by object at eye of viewer
– In degrees, minutes, seconds of arc
o
• 1 = 60 mins
• 1 min = 60 secs
o
• 1 = 360 secs
o
– Thumbnail at arm’s length subtends ~1 of visual angle
o
• 1cm (2/5”) object at 57 cm (20”), monitor distance, ~1 of visual angle
Human Eye: Simple Acuities
• Acuities
– Measurements of abilities to see detail
– Provide ultimate limits of information densities can perceive
• Grating Acuity
• Point, Line, Stereo, Vernier
• Acuity distribution and the visual field
Human Eye: Simple Acuities:
Grating Acuity
• Acuities
– Measurements of abilities to see detail
– Provide ultimate limits of information
densities can perceive
• Grating acuity
– Resolve to ~1 min. (1/60 deg) f arc
– Roughly corresponds to receptor spacing
in fovea
– E.g., to see 2 lines as distinct blank space
between should lie on receptor
– So, should be able to perceive lines
separated by twice receptor spacing
• Also, … superacuities
– Resolution above what expected by
receptor density due to integration of
signals, retinal structure, etc.
Grating Acuity
- 1-2 minutes of arc
- Ability to distinguish a
pattern of bright and dark
bars from a uniform gray
background
Human Eye: Simple Acuities:
Point, Letter, Stereo, Vernier
•
Point acuity
– 1 minute of arc
– Ability to resolve two distinct point
targets
•
Letter acuity
– 5 minute of arc
– Ability to resolve letters
– Snellen eye chart
• 20/20 means a 5-minute letter target
can be seen 90% of time
•
Stereo acuity
– 10 seconds of arc
– Ability to resolve objects in depth
– Measured as difference between 2
angles (a and b) for a just-detectable
difference
•
Vernier acuity
– 10 seconds of arc
– Ability to see if two lines are collinear
Human Eye: Receptors and Fovea
• Lens focuses
image on mosaic
of photoreceptor
cells lining retina
• Fovea
– Small area in
center of retina
densely packed
with cones
– Vision sharpest
– ½o - 2o of arc
Receptor mosaic in fovea
• Blind spot, too
Human Eye: Receptors and Fovea
“The first complexity”
• Lens focuses image on
mosaic of photoreceptor
cells lining retina
• Fovea
– Small area in center of
retina densely packed
with cones
– Vision sharpest
– ½o - 2o of arc
• From 1 – 100 receptors
feed into 1 ganglion cell,
the first “complexity” …
Receptor mosaic in fovea
Human Eye: Acuity Distribution and the
Visual Field
• Again, receptors densely packed at fovea
• Binocular overlap
– Region of visual field viewed by both eyes
– Only here, stereopsis
• Visual acuity non-uniformly distributed
over visual field
– Again, densest receptor field at fovea
o
– E.g., Can resolve only about 1/5 detail at 10
from fovea
• Next slide (tries to) demonstrate “equiresolvability” of characters as a function
of distance from fovea
– resolvability = f(dist. fovea)
– Focus on center, and letters throughout field
about equally “sharp” or “clear”
Human Eye: Resolution
Acuity Distribution
and Visual Field
• Previous showed
equi-resolvability
• As does right
– From C. Ware, “Visual
Thinking for Design”
Brain Pixels and Optimal Screen
Or, “retinal display”
•
So, visual acuity highest at fovea and
lowest at periphery
– Now optimal screen, later optimal display
•
Consider “brain pixels” (bp) as number of
eye receptors
– As screen resolution is from number of
picture elements (pixels) per unit size
• E.g., 100 dpi for 12” wide 1200 pixel screen
•
Acuity graph at right shows:
– foveally (and at center of screen) many
bp’s, for each screen pixel (sp, cf. a, b)
• Circles = receptors, bp
• Squares = screen pixels, sp
• So, could use more screen pixels, i.e.,
higher resolution screens
– At edges, mismatch of bp/sp is less (c, d)
• Large screen might even match bp and sp
• With small screen actually more screen
pixels than brain pixels
– Receptors can make use of this level of
resolution
– So, wasted screen pixels
Brain Pixels and Optimal Screen
Or, “retinal display”
•
Wasted pixels (inefficiency) in display (c)
•
To describe display and brain pixels:
–
–
TPB, total n brain pixels stimulated by display
USBP, n uniquely stimulated brain pixels
•
–
USBP = TPB – redundant brain pixels
•
–
•
How efficiently a display is being used, in terms
of sp and bp
DE = USPB / SP
Finally, proportion of bp in screen area that
are getting unique information
–
–
•
Which takes a while, e.g., in a = 7, and different
for each screen pixel, as different bp density at
different angles
DE, display efficiency
•
–
Some bp get same information (a,b)
VE, visual efficiency
VE = USPB/TPB
Figure (2.22)
–
1m pixel display at 50 cm, considering angle of
view
Brightness, Luminance, Lightness
Luminance, Brightness, Lightness
• Ecologically, need to be able to manipulate objects in
environment
• Information about quantity of light, of relatively little use
– Rather, what need to know about its use
• Human visual system evolved to extract surface properties
– Loose information about quantity and quality of light
– E.g., experience colored objects, not color light
• Color constancy
– Similarly, overall reflectance of a surface
• Lightness constancy
Luminance, Brightness, Lightness
•
Consider physical stimulus and perception
•
Luminance (physical)
– Amount of light (energy) coming from region of
space,
• Measured as units energy / unit area
• E.g., foot-candles / square ft, candelas / square m
• Physical
•
Brightness (perceptual)
– Perceived amount of light coming from a source
– Here, will refer to things perceived as selfluminous
•
Lightness (perceptual)
– Perceived reflectance of a surface
– E.g., white surface is light, black surface is dark
– Physical
• Luminance
– Number of photons
coming from a region
of space
– Perceptual:
• Brightness
– Amount of light
coming from a
glowing source
• Lightness
– Reflectance of a
surface, paint shade
Luminance
•
Amount of light (energy) hitting the eye
•
To take into account human observer:
– Weighted by the sensitivity of the photoreceptors to each wavelength
• Spectral sensitivity function:
700
L   V Ed
400
• E.g., humans about 100 times less sensitive to light at 450nm (blue) than at 510nm (green)
• Note, use of blue for detail, e.g., text, not seem good
– Compounded by chromatic aberration in which blue focuses at different point
•
Later, will examine difference cone sensitivities
Brightness
Perceived brightness and physical intensity – Stevens’ Power Law
• Perceived amount of light coming from a glowing (self-luminous) object
– E.g., instruments
• Perceived brightness very non-linear function of the amount of light
– Shine a light of some intensity on a surface, and ask an observer, “How bright?”
Intensity =
How bright is the point?”
(physical)
(perceptual)
1
4
16
1
2
4
- Stevens’ power law
Perceived ^
Brightness |
Intensity ->
Brightness – Power Law
Perceived ^
Brightness |
Intensity ->
• Stevens power law
–
–
–
–
–
Perceived brightness, B, is proportional to stimulus intensity, I, raised to a power, n
n
B=I
n
Here, Brightness = Luminance
With n = 0.333 for patches of light, 0.5 for points
Applies only to lights in relative isolation in dark, so application more complicated
• Applies to many other perceptual channels
– Loudness (dB), smell, taste, heaviness, force, friction, touch, etc.
• Enables high sensitivity at low levels without saturation at high
levels
Dix: Interpreting the Signal - Color
•
Color
– Made up of hue, intensity, saturation
– Cones sensitive to color wavelengths
– Blue acuity is lowest
– ~8% males and ~1% females color blind
Trichromacy Theory
for color vision
• Recall, that there are 2 types of
retinal receptors
• Rods, low light, monochrome
– So, overstimulated at all but low levels
contribute little
– So, only consider cones for color vision
• Cones, high light, color
– Not evenly distributed on retina
Distribution of receptors across the retina, left eye
shown; the cones are concentrated in the fovea,
which is ringed by a dense concentration of rods
http://www.handprint.com/HP/WCL/color1.html#oppmodel
Trichromacy Theory
for color vision
• Cones (3 types) differentially
sensitive to wavelengths
– “trichromacy”
•
Each type cone has different peak sensitivity:
–
–
–
–
–
S: 450 nm “blue”
M: 540 nm “green”
L: 580 nm “red”
More later
No accident 3 colors in monitor
• Color space:
Cone sensitivity functions
– An arrangement of colors in a 3dimensional space
•
•
•
–
–
–
There are many, each designed for different
purposes
Will consider several
Can match all colors perceived with 3 colors
•
–
Monitor: R,G,B
Primary paint colors: R,Y,B
Printer: cyan, magenta, yellow
Does not matter that spectral composition of that
patch of light may be completely different
But, chickens have 12 …
–
Different gamut, more later
Cone response space, defined by
response of each of the three cone
types. Becomes 2d with color deficiency
FYI:
Color Space Ex.: RGB Color Cube
•
Again, can specify color with 3
– Will see other way
•
RGB Color Cube
– Neutral Gradient Line
– Edge of Saturated Hues
– ppt example
http://graphics.csail.mit.edu/classes/6.837/F01/Lecture02/
http://www.photo.net/photo/edscott/vis00020.htm
Cone Sensitivity Functions
• Cone receptors least sensitive to
(least output for) to blue
Relative sensitivity curves for the three types of
cones, log vertical scale, cone spectral curves from
Vos & Walraven, 1974
Relative sensitivity curves for the three
types of cones, the Vos & Walraven
curves on a normal vertical scale
Color Blindness
• ~8% male, and ~1% females have
some form of color vision
deficiency! (why gender difference?)
• Most common:
– Lack of long wave length sensitive
receptors (red, protanopia)
• See figure at right bottom
– Lack of mid wave length receptors (green,
deuteranopia)
• Results in inability to distinguish red and
green
• E.g., cherries in 1st figure hard to see
• Trichromatic vs. dichromatic vision
Color Blindness Examples
• Normal:
– trichromatic
• No red:
– dichromatic
Color Blindness Examples
• Normal:
– trichromatic
• No red - dichromatic
No green
No blue
Using Physiology for Design
An example guideline based on physiology
• Fovea has few blue (short wavelength) cones
– Can’t resolve small blue features (unless they have high contrast with
background)
• Lens and aqueous humor turn yellow with age
– Blue wavelengths are filtered out
• Lens weakens with age
– Blue is harder to focus
• Guideline: don’t use blue against dark backgrounds where small
details matter (like text!)
Perceived Color
• Color perceived relative to context
– Are the “X”s in the figure below the same color?
Perceived Color
• Color perceived relative to context
– Are the “X”s in the figure below the same color?
– Easy implications for use in maps
• Contrast illusion
– An illusion is an extreme case
• Somewhat “surprising” because it leads to error
– Appears to be different color X!
Perceived Color
• With color of x touching …
Afterimage
• Occurs due to bleaching of
photopigments
– (demo next slide)
• Implications for misperceiving
(especially contiguous colors –
and black and white)
– “I thought I saw …”
• To illustrate:
– On next slide
– Stare at + sign on left
• May see colors around circle
– Move gaze to right
– See yellow and desaturated red
Afterimage Example
Afterimage Example
Dix: Interpreting the signal
(continued)
•
The visual system compensates for:
– movement
– changes in luminance
•
Context is used to resolve ambiguity
•
Optical illusions sometimes occur due to over compensation
FYI: Simultaneous Brightness Contrast
•
Gray patch on a dark background looks lighter than the same patch
on a light background
– Predicted by DOG model of concentric opponent receptive fields
• Again, illusion, system overcompensates for context
Mach Bands
• Saw the phenomenon, what’s going on?
Mach Bands
Ernst Mach
• Like, “light stripes” where two areas meet
• At point where uniform area meets a luminance ramp, bright
band is perceived
– Said another way, appear where abrupt change in first derivative of
brightness profile
– Simulated by DOG model
– Particularly a problem for uniformly shaded polygons in computer graphics
• Hence, various methods of smoothing are applied
FYI: Mach Bands
Ernst Mach
• At point where uniform area meets a luminance ramp, bright
band is perceived
– Said another way, appear where abrupt change in first derivative of
brightness profile
– Simulated by DOG model
– Particularly a problem for uniformly shaded polygons in computer graphics
• Hence, various methods of smoothing are applied
Psychology, Physiology
and Design of Interactive Systems
• Some direct applications
– e.g. blue acuity is poor
 blue should not be used for important detail
• However, correct application generally requires
understanding of context in psychology, and an
understanding of particular experimental conditions
• A lot of knowledge has been distilled in
– guidelines
– cognitive models
– experimental and analytic evaluation techniques
End ?
Optical Illusions
the Ponzo illusion
the Mueller-Lyer illusion
Optical Illusions
Preattentive Processing and Illusions
• What is wrong with this
triangle?
– Impossible (or at least
difficult) to build
• Cues for perception
misleading
– Must rely on conscious
(rational) processes
intelligence to figure it out,
– Conscious/rational
processes much slower
The Other Senses …
• But not smell and taste
– Long latency, practical challenges
• Hearing
• Touch
• Kinesthetic – Movement
– Fitt’s law
Hearing
• Provides information about environment:
distances, directions, objects etc.
• Physical apparatus:
– outer ear
– middle ear
– protects inner and amplifies sound
– transmits sound waves as
– inner ear
vibrations to inner ear
– chemical transmitters are released
and cause impulses in auditory nerve
• Sound
– pitch
– loudness
– timbre
– sound frequency
– amplitude
– type or quality
Hearing (cont)
• Humans can hear frequencies from 20Hz to
15kHz
– less accurate distinguishing high frequencies than
low.
• Auditory system filters sounds
– can attend to sounds over background noise.
– for example, the cocktail party phenomenon.
Touch
• Provides important feedback about environment
• May be key sense for someone who is visually impaired
• Stimulus received via receptors in the skin
– thermoreceptors
– nociceptors
– mechanoreceptors
– heat and cold
– pain
– pressure
(some instant, some continuous)
• Some areas more sensitive than others, e.g., fingers
• Kinethesis - awareness of body position
– affects comfort and performance.
Movement
• Time taken to respond to stimulus:
reaction time + movement time
• Movement time dependent on age, fitness etc.
• Reaction time - dependent on stimulus type:
– visual
– auditory
– pain
~ 200ms
~ 150 ms
~ 700ms
• Increasing reaction time decreases accuracy in the
unskilled operator but not in the skilled operator.
Movement – Fitt’s Law
•
Fitts' Law, 1954
–
Demo: http://www.tele-actor.net/fitts/
Movement – Fitt’s Law
•
Fitts' Law, 1954
–Fundamental law of human sensory-motor system
–Practical application in interface design
–Describes the time taken to hit a screen target
–Time, Mt, to move hand to a target of size, S, at distance, D, away
Mt = a + b log2(D/S + 1)
where:
a and b are empirically determined constants
Mt is movement time
D is Distance
S is Size of target
– “index of difficulty”: log2(D/S + 1)
• Same performance at greater distance with greater size
 targets as large as possible, distances as small as possible
Fitt’s Law – Example Implication
• Hierarchical menus are hard to hit
– Especially when it takes two actions …
End