In cognitive psychology, any result of interest
involves a subtraction between two measures, most
often involving the same respondent (the person
who is responding).
We test a group of participants (usually around
30), and compute the average difference between
two (sometimes more) experimental conditions
What kind of subtraction?
In early work, subtractions were between two
different tasks.
Mental Chronometry
Subtractive Methodology
Simple reaction time (RT) task
STIMULUS
DETECTION
RESPONSE
EXECUTION
CHOICE REACTION TIME (RT) TASK
GO/NO GO REACTION TIME (RT) TASK
Simple RT
Choice RT
Go/Nogo RT
Choice RT - Go/nogo RT =
Choice RT - Go/Nogo RT =
Donders’ method is based on three assumptions.
First, it is assumed that the mental processes of stimulus detection, stimulus identification,
response selection and response execution are arranged sequentially, in the sense that the
output of one serves as the input to the next.
Second, it is assumed that only one process can be active at each moment in time between
stimulus input and response output.
Third, it is assumed that a mental process can be added or omitted without affecting the duration
of the other processes, the so-called assumption of pure insertion.
Donders:
Som e people giv e the response (in Task C) when
the y shou ld hav e re m ain e d sile n t. An d if this
ha ppe n s on ce, the w hole se rie s m u st be re j e cte d:
fo r, ho w c an w e be su re t hat w he n t he y had t o
m ak e the re spon se an d did m ak e it, the y had
properly waited until they should have
discrim in ate d?
????????
Go/NoGo
Choice RT
The subtraction: Choice RT-Go/NoGo RT may not
provide a valid measure of
Data: Error rate
2AFC
GNG
400
RT (ms)
Error Rate
0.15
0.1
0.05
0
Data: RT
no/nogo
200
0
yes/go
Model: Error rate
Model: RT
RT (steps)
Error Rate
Figure 1: Systematic error0.2biases in the GNG
task.
(A) The
figure shows error rates associated
15
Task:
Lexical
2AFCDecision
If
this
happens
once,
the
whole
series
must
be
rejected:
for,and
how
can(forced
with a perceptual decision-making task performed by subjects
in both
Go/NoGo
Yes/No
GNG
0.15
choice)
the error
rates
in the
settings
wereand
similar
both classes
we settings.
be sureAlthough
that when
they
had
to forced
makechoice
the response
didformake
it,
10
there wasthey
a significant
bias
the Gountil
response
theaGNG
task, with
more false alarms than
for
a word
No
nonsense
word
0.1towards
had Yes
properly
waited
theyinfor
should
have
discriminated
omission errors. (B) Mean response time on the GNG task was lower
than for the same stimulus on
5
0.05
the 2AFC task. (Data adapted from Bacon-Mace et al., 2007).
0
left/nogo
right/go
0
Task: Go/NoGo
Stimulus
predictions of the optimal decision-making model, including those that specifically differ from the
fixed-threshold DDM approximation [2, 12].
Yes for a word
Do not respond to a nonsense
word
2 Bayesian inference and risk minimization in choice tasks
Human choice behavior in the GNG and 2AFC tasks exhibits a consistent Go bias in the GNG task
that is not apparent for the same stimulus in the 2AFC task. For example, Figure 1 shows data
from a task in which subjects must identify whether a briefly-presented noisy image contains an
animal or not [16], under two different response conditions: GNG (only respond to animal-presen
images), and 2AFC (respond yes/no to each image). Subjects showed a significant bias towards the
Go response in the GNG task, in the form of higher false alarms than omission errors (Figure 1A)
as well as faster RT than for the same stimulus in the 2AFC task (Figure 1B).
For the 2AFC task, a large body of literature supports the “accumulate-to-bound” model of perceptual decision-making, [23, 20, 26], where moment-to-moment sensory input (“evidence” in favor of
either choice) is accumulated over time until it reaches a bound, at which point, a response is gen-
Lexical Decision Task (Choice)
BOOK
YES
Letter Identification
LYNX
MOOK
Word Identification
Data: Error rate
YES
0.1
Letter Identification
400
200
Word Identification
0.05
0
LYNX
2AFC
GNG
RT (ms)
BOOK
Error Rate
0.15
Data: RT
GO/NoGo Task
no/nogo
0
yes/go
Model: Error rate
Figure 1:
MOOK
Model: RT
RT (steps)
Error Rate
Systematic error0.2biases in the GNG task. (A) The 15
figure shows error ra
2AFC
with a perceptual decision-making task performed by subjects
in both Go/NoGo and Y
GNG
0.15
choice) settings. Although the error rates in the forced choice settings
were similar for
10
there was a significant bias0.1towards the Go response in the GNG task, with more fals
omission errors. (B) Mean response time on the GNG task was lower
than for the sam
5
0.05
the 2AFC task. (Data adapted from Bacon-Mace et al., 2007).
0
left/nogo
right/go
0
Stimulus
predictions of the optimal decision-making model, including those that specifically d
fixed-threshold DDM approximation [2, 12].
2 Bayesian inference and risk minimization in choice tasks
Human choice behavior in the GNG and 2AFC tasks exhibits a consistent Go bias in
Donders’s assumption of pure insertion: an evaluation
on the basis of response dynamics.
Ralph Ulrich, Stefan Mattes & Jeff Miller
Acta Psychologica 1999
Response force was measured by
means of a force key ..... One end of
a leaf spring was held fixed by an
adjustable clamp, and the other end
remained free. The participant's
forearm rested comfortably on a
table while his or her index finger
bent down the free end of the leaf
spring in response to the stimulus. A
force of 10 N bent the free end by
about 1 mm. The resolution of this
device was about 2 cN
(approximately 2 g). Strain gauges
were attached to the leaf spring, so
force applied to its free end caused
changes in an electrical signal that
was digitized with a sampling rate of
500 Hz
Why use response force as
an additional measure?
Thus, response force may specifically probe the motor system
and thus assess potential differences (if any) between the various
RT tasks devised by Donders. In sum, if the type of task affects
response execution, then response force should vary as a
function of task, providing evidence at odds with Donders’s
assumption of an invariant response execution stage.
Task:
Go/NoGo
If circle is on the right, respond.
If circle is on the left, do not respond.
Go trial
Task:
Go/NoGo
If circle is on the right, respond.
If circle is on the left, do not respond.
NoGo trial
Task:
Go/NoGo
If circle is on the left, respond.
If circle is on the right, do not respond.
Go trial
Task:
Go/NoGo
If circle is on the left, respond.
If circle is on the right, do not respond.
NoGo trial
Task: Choice
If circle is on the right,respond with
the right key.
If circle is on the left, respond with
the left key.
Right
keypress
Task: Choice
If circle is on the right,respond with
the right key.
If circle is on the left, respond with
the left key.
Left
keypress
Subject 1
Subject 2
Subject 3
.
.
.
.
Subject N
Average RTgo/nogo
Average RTgo/nogo
Average RTgo/nogo
Average RTchoice
Average RTchoice
Average RTchoice
Average RTgo/nogo
Average RTchoice
.
Group
Average RTgo/nogo
Total=36
Average RTchoice
QR
Choice versus Go/Nogo
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It is possible that this extra focusing of attention in the go/nogo task would increase the
apparent brightness of the stimulus in this task as compared to the other two tasks.
It has been shown in previous publications that brighter stimuli produce more forceful
responses.
So this attentional hypothesis might explain why participants produced especially forceful
responses in the go/nogo tasks of the preceding experiments, even if response execution
processes operated identically in all tasks.
+
+
Instructions: Respond with a right key press if the stimulus is an
S and with a left key press if stimulus is an X.
+SX
Go/Nogo RT
Instructions: Respond if the stimulus is an X. Do not respond if
the stimulus is an S.
+S
X
The assumption of pure insertion is
again challenged by these results.
Go/Nogo RT’s do not have the same
motor output as Choice and Simple RT’s
Main result: The go/nogo task produces
especially vigorous, or
forceful, responses.
Why?
Consider: How does the subject
prevent himself from responding
on Nogo trials?
Recall Donders’ remark:
Som e people giv e the response (in Task C) when the y
shou ld hav e re m ain e d sile n t.
Threshold
Motor preparation
Go/Nogo
Choice
To produce a “Go” response, the motor system needs a lot of activation from its initial ‘ready’
or prepared state (more than for a simple or choice RT). This bigger activation produces a
more
forceful action!
Interactive-Activation
A
B
C
WHICH MODEL IS CONSISTENT WITH THE FOLLOWING!
CLAIM BY CATTELL?!
‘we do not therefore perceive separately the letters of which a word is composed but the word
as a whole’.
Cattell
HAND
###D
###D
HAND
A modern idea
JAMES L. MCCLELLAND AND DAVID E. RUMELHART
380
pie weighted average to yield a net input to
the unit, which may be either excitatory
(greater than zero) or inhibitory. In mathematical notation, if we let «,(<) represent
the net input to the unit, we can write the
equation for its value as
The net input to a node drives the activation of the node up or down, depending
on whether it is positive or negative. The
degree of the effect of the input on the node
is modulated by the node's current activity
level to keep the input to the node from driving it beyond some maximum and minimum
- 2 yikik(t), (1) values (Grossberg, 1978). When the net in= 2
put is excitatory, n,(0 > 0, the effect on the
where ej(t) is the activation of an active ex- node, fj(t), is given by
citatory neighbor of the node, each ik(t) is
(2)
the activation of an active inhibitory neighbor of the node, and a,j and yik are associated where M is the maximum activation level of
weight constants. Inactive nodes have no in- the unit. The modulation has the desired
fluence on their neighbors. Only nodes in an effect, because as the activation of the unit
active state have any effects, either excit- approaches the maximum, the effect of the
input is reduced to zero. M can be thought
atory or inhibitory.
Bottom-u
Words
Feedforward
Letters
Features
Figure 3. A few of the neighbors of the node for the letter T in the first position in a word, and their
interconnections.
Excitatory Inhibitory
featural analysis employed by Rumelhart
(1970) and by Rumelhart and Siple (1974), have been excluded. This sample appears to
illustrated in Figure 4. Although the exper- be sufficient to reflect the essential characiments we have simulated employed differ- teristics of the language and to show how
ent type fonts, we assume that the basic re- the statistical properties of the language can
sults do not depend on the particular font affect the process of perceiving letters in
used. The simplicity of the present analysis words.
recommends it for the simulations, though
it obviously skirts several fundamental issues An Example
about the lower levels of processing.
Let us now consider a sample run of our
Finally, our simulations have been revaluesMODEL,
em- PART 1
stricted to four-letter words. We have simulation model. The parameter
INTERACTIVE ACTIVATION
383
INTERACTIVE
ACTIVATION
MODEL,
PART
1
ployed
in
the
example
are
those
used
to
simequipped our program with knowledge of
all of the nodes in the system once each cycle
ulate allon the
experiments
discussed
in the
1,179 four-letter
words
occurring
at DAVID
least
the
basis
of the values on the
previous
all of the
inE.the
system once each
cycle
JAMES L. MCCLELLAND
ANDnodes
RUMELHART
384
Obviously,
this
isThese
simply a matter
of are detwo times per million in the Kucera
remainder
of
Part
1.
values
on the basisand
of the values
on thecycle.
previous
INTERACTIVE
MODEL, PART 1
convenience
and notACTIVATION
a funcycle.
is
a computational
matter
down below its resting level, as a result
of Obviously,
weak, and this
theyscribed
aresimply
easily driven
downofwell
damental
assumption.
have endeavored
in
detail
in the We
following
section. For
Francis (1967)
word
inflected
competition
withcount.
work. The Plurals,
words wear
and
below zero,
as a result and
of competition
from
computational
convenience
not
a
funto keep the time slices "thin" enough so that
are consistent
with the
only letter
ac- theassumption.
other word units.
activations
of
these
damental
We
have
endeavored
forms, first weak
names,
proper
names,
acronyms,
the
purposes
this
example,
imagine
that
all The
of
the
nodes
inof
thebehavior
system
each cycle
theof model's
isonce
continuous
for
all
tive in the first letter position, and onetoofkeep
the the
units
do not
drop
quite as
low,
as
time
slices
"thin"
enough
socourse,
that
intents
and
purposes.
on
the
basis
of
the
values
on
the
previous
abbreviations,
and
occasional
unfamiliar
enthe
word
WORK
has
been
presented
to
the
the
activation
level
of
words
such
as
gill,
two active in the fourth letter position.the
They
model's behavior iscycle.
continuous
for all
Any
simulation
of the amodel
involves
this
matter
of
in Obviously,
common
with
the is simply
are also inconsistent with the letters active which contain nothing
intents
and
purposes.
making
explicit
assumptions
about
the
apsubject
andnot convenience
that
has extracted
tries arisingin Positions
from 2apparent
sampling
flukesinformation.
Although
shown the subject
and 3. Thus, the
activation presented
Figure 5. A hypothetical set of features tha
computational
andof not
a funpropriate
featural analysis
the input
Any simulation
thewords
model
involves
in Figure 6, of
these
attain
near-miniextracted
on a trial in an experiment on word
they receive from the letter level is quite
those
features
shown
Figure
5.font.
In the
first
damental
assumption.
We in
have
endeavored
We
have,
simplicity,
chosen
the font
and
making explicit assumptions
about
the
ap-for
Figureemployed
5. A hypothetical
set of features that might be
featural
analysis
by
Rumelhart
to
keep
the
time
slices
"thin"
enough
so
that
propriate featural analysis
of the
input font.
three
letter
positions,
the
features
ofon have
theperception.
leton
a trial
an experiment
word
been excluded. This sample ap
and byextracted
Rumelhart
andinSiple
(1974),
We have,
for simplicity,the
chosen
the(1970)
fontbehavior
and
model's
ishave
continuous
for
all
word
activations
be sufficient
ters
W,
O,
and
R
been
completely
ex- to reflect the essential
illustrated
in
Figure
4.
Although
the
experfeatural analysis employed
by and
Rumelhart
intents
purposes.
teristics
of the
iments
we have
simulated
employedThis
differhave
been excluded.
sample
appears
to language and to sh
(1970) and by Rumelhart
and
Siple
(1974),
tracted.
In
the
final
position
a
set
of
features
the statistical properties of the lang
Any simulation
of
the
model
involves
ent type fonts,
assume
that
the
basic
bewesufficient
to reflect
there-essential
characillustrated in Figure 4. Although the
experaffect
the
process
of perceiving l
sultswith
do assumptions
notthe
depend
onofthe
particular
making
about
the apconsistent
letters
and
Rfont
have
been
teristics
theK
language
and
to show
workexplicit
iments we have simulated
employed
differFigure
5. Ahow
hypothetical set of featu
words.
used.
The simplicity
of
the
present
analysis
propriate
featural
analysis
of
the
input
font.
the
statistical
properties
ofwould
theextracted
language
ent type fonts, we assume
that therecommends
basic
re- the
oncan
a trial in an experiment on
extracted,
with
features
that
disit for
the simulations,
C
simplicity,
chosen
the fontofthough
and
affect
the fundamental
process
perceiving letters in
sults do not depend onWe
the have,
particular
font skirts
itfor
obviously
several
issues
o
An
Example
ambiguate
the
letter
• 1-1
words.
featural
employed
Rumelhart We wish
used. The simplicity of
the presentanalysis
analysis
about
the lower
levels unavailable.
ofbyprocessing.
-p
have
excluded.
sam
Letbeen
usrenow
consider aThis
sample
ru
Finally,
our activity
simulations
have
been rerecommends it for thenow
simulations,
though
(1970)
by
Rumelhart
and Siple
o
toand
chart
the
of(1974),
the
system
simulation
model.
parameter
va
stricted
to four-letter
words.
We
have be
it obviously skirts severalillustrated
fundamental
issues
sufficient
to The
reflect
the ess
in
Figure
4.
Although
the
experAn
Example
from
thisprogram
presentation.
6 the
ployed in of
the
example
are thoseand
use
equipped our
with knowledge ofFigure
about the lower levelssulting
of
processing.
teristics
language
iments
we1,179
havefour-letter
simulated
employed
differulate
allofthe
discussed
occurring
at aleast
Letwords
us now
consider
sample
run
ourexperiments
Finally, our simulations
have
been
re- course
O
shows
the
time
of
the
activations
for
the
statistical
properties
of
the
type fonts,
we assume
thatinmodel.
the
retwo times
persimulation
million
the basic
Kucera
and remainder
of Part 1. These values
The parameter
values emstricted to four-letter ent
words.
We
have
affect
the
process
of
perceiv
scribed
in
detail
in
the
following
sec
Francis
(1967)
word
count.
Plurals,
inflected
sults
do
not
depend
on
the
particular
font
selected
nodes
at
the
word
and
letter
levels,
equipped our program with knowledge of ployed in the example are those used to simfirst names,
proper
names,analysis
acronyms, words.
the purposes of this example, imag
used.
Theforms,
simplicity
of
the
present
ulate
all
the
experiments
discussed
in
the
1,179 four-letter words
occurring
at
least
respectively.
abbreviations, and occasional unfamiliar en- the word WORK has been presente
two times per million recommends
in the Kucera
and
remainder
of sampling
Part though
1. These
itarising
for the
simulations,
subjectare
anddethat the subject has e
from
apparent
flukes values
-0.40
thetries
word
level,
charted
the
scribed
inwe
detailhave
in the
following
section.
For shown in Figure 5. In
Francis (1967) word count.
Plurals,
inflected
itAt
obviously
skirts
several
fundamental
issues
those
features
An
Example
forms, first names, proper
names,
acronyms,
the
purposes
of
this
example,
imagine
that
three
letter
positions,
the features o
about the levels
lower levels
processing.
activity
oftheof
the
nodes
for the
words
abbreviations, and occasional
unfamiliar
enword have
WORKbeen
has been
presented
to
the
ters
W,us
O, now
and Rconsider
have beena compl
Let
samp
Finally,
our
simulations
reletter
activations
work,
word,
wear,
and
weak.
that
tracted.
In the final position a set of
subject
and
that theNote
subjectfirst
has
extracted
tries arising from apparent
sampling
simulation
model.
TheKparame
stricted
to flukes
four-letter
words.
We in
have
consistent
with
the letters
and R h
those
features
shown
Figure
5.
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work
is the
word
inpositions,
the lexicon
consisployed
in with
the
arethat
thos
equipped
our only
program
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w
threewith
letter knowledge
theoffeatures
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let- example
ambiguate
the letter
unavailable.
ulate
all
the
experiments
disW
1,179
four-letter
words
occurring
at
least
tent
with
all
the
presented
information.
As
ters
W,
O,
and
R
have
been
completely
exFigure 4. The features used to construct the letters in
nowoftofeatures
chartofthePart
activity
of the syv
tracted.
In the
final
position
a remainder
set
timesits
peractivation
million
in the
Kucera
and
1. These
the font assumed by the
a two
result,
level
is letters
the
highest
and
sulting
from
this presentation. F
c simulation program, and the
consistent
with the
K and
R havein
been
scribed
detail
in
the
followin
Francis
(1967)
word
count.
Plurals,
inflected
o
shows the
time course of the activa
letters themselves. (From "Process of Recognizing Ta- reaches a value extracted,
of .8
through
the that
first
40
with acronyms,
the features
would
dis- ofthe
forms, first names, proper
names,
the
purposes
this
example,
selected
nodes at
word
and lette
chistoscopically Presented Words" by David E. Ruambiguate the letter unavailable.
We wish
respectively.
time
cycles.
The
word
word
is
consistent
with
abbreviations,
and occasional
unfamiliar
enthe
word
WORK
has
been
pre
now to chart the activity of the At
system
re- level, we have cha
melhart and Patricia Siple, Psychological Review, 1974,
theand
word
subject
that
the
subject
triesbulk
arisingoffrom
apparent
sampling
flukes
sulting
from
this
presentation.
Figure
6
the
the
information
presented
and
activity
levels
of
the
nodes
for
th
81, 99-118. Copyright O1974 by the American Psychoshown
in Figure
shows the time course of the those
activations
work, features
word,for
wear,
and weak.
Note
o
therefore first rises
and
later
is
pushed
back
logical Association. Reprinted
by permission.)
three
letter
positions,
featu
work
the only
word in the
the lexico
selected nodes at the word and
letteris
levels,
3CIEFGHI
JKLMNDPQR
BTU^WXYZ
w
O
3CIEFGHI
JKLMNDPQR
3CIEFGHI
BTU^WXYZ
JKLMNDPQR
w
-P
-0.40
BTU^WXYZ
w 3CIEFGHI
tent W,
withO,
all and
the presented
respectively.
Figure 4. The features
used to construct the letters in ters
R have informa
been c
the font assumed byAt
the the
simulation
result, its
activation
is the hig
wordprogram,
level, and
we the
havea charted
Inthe
the finallevel
position
a
letters themselves. (From "Process of Recognizing Ta- tracted.
reaches
a value of .8 through the
activity Words"
levels by
of David
the E.
nodes
the words
chistoscopically Presented
Ru- for
consistent
with
the
letters
K
an
time cycles.
The word word is consist
work,
wear,Review,
and weak.
first that
melhart and Patricia
Siple,word,
Psychological
1974, Note
extracted,
the features
th
the bulk
of with
the information
presen
81, 99-118. Copyright
by the
American
work 1974
is the
only
word Psychoin the lexicon
consistherefore
first
risesletter
and later
is push
logical Association. Reprinted by permission.)
ambiguate the
unavaila
JKLMNDPQR
BTU^WXYZ
w
Figure 4. The features used to construct the letters in
font assumed
by word
the simulation
program,
and the
Figure 6. The time course of activations the
of selected
nodes at the
and letter levels
after extraction
of the features shown in Figure 5.
letters themselves. (From "Process of Recognizing Tachistoscopically Presented Words" by David E. Rumelhart and Patricia Siple, Psychological Review, 1974,
81, 99-118. Copyright 1974 by the American Psychological Association. Reprinted by permission.)
tent with all the presented information. As
now
to chart
a result, its activation level is the
highest
and the activity of t
sulting
from
reaches a value of .8 through
the first
40 this presentatio
time cycles. The word word is consistent
shows thewith
time course of the a
the bulk of the information selected
presented nodes
and at the word and
therefore first rises and later is
pushed back
respectively.
Figure 4. The features used to construct the letters in
the font assumed by the simulation program, and the
letters themselves. (From "Process of Recognizing Tachistoscopically Presented Words" by David E. Rumelhart and Patricia Siple, Psychological Review, 1974,
81, 99-118. Copyright 1974 by the American Psychological Association. Reprinted by permission.)
At the word level, we hav
activity levels of the nodes f
work, word, wear, and weak. N
work is the only word in the l
tent with all the presented in
a result, its activation level is th
reaches a value of .8 through
time cycles. The word word is c
the bulk of the information p
therefore first rises and later i
two active in the fourth letter position. They
are also inconsistent with the letters active
in Positions 2 and 3. Thus, the activation
they receive from the letter level is quite
units do not drop quite as low, of course, as
the activation level of words such as gill,
which contain nothing in common with the
presented information. Although not shown
in Figure 6, these words attain near-mini-
word activations
work
C
o
• 1-1
-p
o
JAMES L. MCCLELLAND AND DAVID E. RUMELHART
384
down below its resting level, as a result of
competition with work. The words wear and
weak are consistent with the only letter active in the first letter position, and one of the
two active in the fourth letter position. They
are also inconsistent with the letters active
in Positions 2 and 3. Thus, the activation
they receive from the letter level is quite
weak, and they are easily driven down well
below zero, as a result of competition from
the other word units. The activations of these
units do not drop quite as low, of course, as
the activation level of words such as gill,
which contain nothing in common with the
presented information. Although not shown
in Figure 6, these words attain near-mini-
O
O
-0.40
word activations
letter activations
work
C
o
c
o
• 1-1
-p
o
O
O
-P
O
o
-0.40
-0.40
letter activations
Figure 6. The time course of activations of selected nodes at the word and letter levels after extraction
of the features shown in Figure 5.
c
o
K on its own is quite
confusable with R.
-P
O
o
-0.40
Figure 6. The time course of activations of selected nodes at the word and letter levels after extraction
of the features shown in Figure 5.
K in WORK is more confusable
with D than R
The logic of additive factors
What is a factor?
Any type of stimulus or response can be varied in
some systematic way.
We can make letters small or big
BIG
SMALL
We can make words familiar or less familiar
BOOK ROOK
We can make shapes easy to see or harder to see
The logic of additive factors
Each of these properties of the stimulus (we can
do the same for responses) is termed a factor if
we wish to make use of it in an experiment.
We can make letters small or big
BIG
Size
SMALL
We can make words familiar or less familiar
BOOK ROOK
Familiarity
We can make shapes easy to see or harder to see
Stimulus Quality
Why might we want to vary a stimulus or response
factor?
Stimulus Identification
Response Mapping
Movement Programming
Response mapping. Respond with the hand on the same side as the target object
Compatible Mapping
Response mapping. Respond with the hand on the opposite side to the target object
Incompatible Mapping
Examples of Compatible versus Incompatible Response Mappings.
ject is instructed to:
Respond with your left hand to
Incompatible response mapping
Respond with your right hand to
Respond with your left hand to
Compatible response mapping
Respond with your right hand to
Say white to
Incompatible response mapping
Say black to
Why might we want to vary a stimulus or response
factor?
Stimulus Quality
620
600
580
Reaction Time
540
500
500
Easy
Hard
Choice of response
Hard to see
Easy to see