Abstract
Cognitive control processes reduce the effects of irrelevant or misleading
information on performance. We report a study suggesting that effective
cognitive control mechanisms are configured quickly during training. In a
Stroop-like task, participants practiced naming abstract shapes using color
words (with one shape called “red,” another called “blue,” etc.—these are
referred to as “shape names”) In a subsequent test, naming the actual color of
the shape was impaired when the color name and the shape name conflicted.
Using regression analysis, we found that both the relative speed of basic shape
and color naming processes and the amount of training on an individual shape
made independent contributions to the amount of interference created. ERP
recording in the same task revealed a larger frontal N200 component for
participants who showed more behavioral interference.
Slide 1
Introduction
Human and animal sensory systems are constantly bombarded with an
overload of information, and environmental demands often require the rapid
integration of many sensory stimuli in order to choose an appropriate response.
This job is facilitated by selective attention, the situation-specific control over
which stimuli and responses are fully processed. To the degree that selective
attention fails, irrelevant information interferes with performance.
An important step in understanding how selective attention works is
developing theories of how interference is created. One simple model of how
interference is created is a ‘horserace’ model based on the processing speeds for
relevant and irrelevant attributes of the stimulus. That is, the relative speed of
basic perceptual/motor processing of relevant and irrelevant stimulus attributes
predicts how much interference will be produced.
Evidence also exists that the relationship between relative processing speed
and interference is not linear, but an inverted-U shaped function (Dyer, 1973).
Faster or earlier processing of irrelevant stimuli does not always lead to increased
interference. For example, if irrelevant features are processed sufficiently faster
than relevant stimuli, it appears that the irrelevant response can be primed and then
inhibited before the relevant processing reaches the critical stage where it is
vulnerable to interference.
Slide 2
Another factor contributing to interference is the automaticity of stimulus
processing—the degree to which the flow of information along a stimulus–
response pathway is independent of controlled attention. Automaticity and relative
speed may independently affect cognitive control, and thus measured interference.
Alternately, an automaticity-based account may replace relative speed as an
explanation for why interference occurs.
MacLeod and Dunbar (1988) used training in a Stroop-like paradigm in a
study designed to investigate the effects of both relative speed and stimulusresponse automaticity. Participants practiced naming irregular shapes with color
names (e.g., “blue”). They were then tested on both naming the shapes when they
appeared in colors (‘shape naming’) and on naming the colors in which shapes
were presented (‘color naming’).
MacLeod and Dunbar found that the
interference created on each of these two tasks was equal after five days of
practice, but that the relative speeds of the color naming and shape naming
processes were not equalized until 20 days of practice. Based on their results, they
suggested that automaticity, created by training, is a better predictor of interference
than relative speed.
Slide 3
Task
“blue”
“red”
“green”
“yellow”
Slide 4
Shape Naming
•Participants verbalized an arbitrary color word (red, blue, green, or yellow) upon
presentation of one of four shapes; these are the “shape names”.
•The shape names were the same as the four non-white stimulus colors: red, blue,
green, or yellow.
•The actual colors of stimuli in the shape-naming task were either congruent with the
shape’s name, incongruent with the shape’s name, or white (neutral condition), with
equal proportions of each type. The three incongruent colors for each shape were
presented equally often.
Color Naming
•Participants verbalized the actual color of one of the four random polygons shown
above, or of a colored circle.
•Stimuli appeared in the four non-white colors of the previous task, in equal
proportions of each.
•For the polygons, colors were either congruent or incongruent with the shape name.
The circles served as a neutral condition. The three incongruent shapes for each
color were presented equally often.
Slide 5
Experiment 1 – Design
Task sequence
A
Color naming
Shape naming
Shape naming
B
Shape naming
C
Shape naming Color naming
Day 1 2 3 4 5 6 7
Task seq. A B C B C B C
Shape naming
Boxed conditions used in ANOVA
Slide 6
During shape naming, each of the four shapes was
presented a different number of times, to allow the effects of
number of practice trials and session to be assessed
independently in a regression analysis.
•
Number of practice trials naming each shape by the end of
Session 7 varied between 0 and ~2600.
The number of practice trials on a particular shape varied
between participants.
Slide 7
Mean RT (ms)
Experiment 1 Results
• The right half of the
graph contains the
results for color naming.
The shape naming
results (left half of the
graph) are discussed
elsewhere, although they
are included for interest.
Slide 8
Omnibus ANOVA results showed that congruence affected color naming
latency. Colors with incongruent shapes were named more slowly than those
with neutral or congruent shapes. There was no significant main effect of
session, F < 1. The significant interaction between session and congruence,
F(6,66) = 2.61, MSE = 854, p < .05, demonstrated that interference increased
slightly with practice.
A planned comparison testing for a linear change in incongruent relative to
neutral across days revealed a significant increase in interference with practice,
F(1,11) = 5.96, MSE = 324, p < .05.
Few errors were made on the task. However, an analysis of accuracy rates
yielded similar results, demonstrating that there was no substatial speedaccuracy tradeoff.
Slide 9
Regression Analysis
MacLeod and Dunbar suggested that training affects performance
above and beyond the effects of relative processing speed. This
analysis investigated whether training and relative speed
independently contribute to interference.
What factors predict amount of
interference?
Relative speed of shape naming and color naming
Days of training
Number of training trials on a particular shape
Results
•Significant effect of relative speed of processing, F(1,148)
= 7.95, MSE = 84.16, p < .01. Faster shape naming relative
to color naming produced a linear increase in interference.
•No effect of day of training, p > .10
•Significant effect of number of training trials, F(1,148) =
4.94, MSE = 84.16, p = < .05. More training on a specific
shape produced more interference.
Effects of subject and shape entered as blocking factors
Dependent measure
Interference ratio:
RTI - RTC
RTC
I is incongruent, C is congruent; RT = reaction time
•Overall, the model accounted for 48% of the variance in
observed color naming interference.
Slide 10
Faster shape naming relative to color
naming produced a linear increase in
interference.
More training on a specific shape
produced more interference.
ERP effects of color naming
ERPs were recorded during the color naming task in an 8th session, after completion of
the seven practice days. The graphs show a larger overall frontal negative shift (blue) in
the N200 time window for participants who were successful at resolving interference.
A
B
C
N200 waveform (235-275 ms)
Low interference group
High interference group
Low int.
High int.
. Scalp distributions and waveforms for the color naming task, 235 – 275 ms after stimulus presentation.
A,B: Scalp distributions for subjects who showed low and high behavioral interference effects, respectively.
C: ERP waveforms during the ~n200 time window for each subject group. The thick solid line is neutral
(circles), the dotted line is incongruent, and the thin solid line represents averages over congruent trials.
Slide 11
Experiment 2
We hypothesized that practice can affect cognitive control in two
ways:
1. Strengthening / speeding an irrelevant stimulus-response
association
2. Weakening a relevant association that has been consistently
ignored in practice.
This experiment manipulated the practice stimuli to isolate the
“weakening” effect. We predicted that the presence of
irrelevant colors during shape naming practice may create
interference with color naming. We compared shape naming
practice on shapes with irrelevant colors to practice naming
white shapes.
In addition, based on the previous experiment, we reasoned that
measures of improvement during training might predict
interference independent of the relative speed of processing
relevant and irrelevant dimensions at test.
Slide 12
Experiment 2 Design
Group 1
Color naming
Shape naming
Session 1
Shape naming
Session 2
Shape naming
Color naming
Session 3
Group 2
Color naming
Shape naming
Shape naming
Boxed conditions used in ANOVA
Shape naming
Color naming
Slide 13
Experiment 2: ANOVA Results
Hypothesis: the presence of irrelevant colors during shape naming practice may create interference with color naming.
Experiment 2: Median latency in color naming
3-way interaction: F(2, 56)=4.7817, p=.01
700
680
660
Median latency (ms)
640
620
• Subjects were both
slower and less
accurate to name colors
when they appeared
within incongruent
shapes
600
580
• Interference was
created only when
subjects saw colored
shapes during training
560
540
520
500
480
Congruent
460
Session 1
Session 3
Training: Colored shapes
Group 1
Session 1
Session 3
Training: White shapes
Group 2
Incongruent
Slide 14
Regression Analysis
To compare practice effects to effects of relative
speed after practice, we measured improvement
during training in two ways:
1.
Improvement (decrease in response time)
between blocks of 20 trials within a session.
2.
Improvement between the last 3 blocks of one
session and the first three blocks of the next.
Participants improved substantially over this
“consolidation period” between sessions.
•
Relative speed was measured during the final
color naming block in the same way as in
Experiment 1, and the regression was
conducted between subjects (n = 34).
•
A follow-up Experiment 3 (n = 12), similar to
Experiment 2 except in monetary
compensation, was analyzed independently
using the same regression.
Predictors
• Relative speed
• median shape naming latency for
white shapes - median color naming
latency for new shapes
• Average improvement in training within
sessions
• RTblock N - RTblock N+1
• Averaged within sessions (19 blocks)
and over sessions (4)
•Average improvement in training between
sessions
• Consider only last 100 trials of
session N and 1st 100 trials of session
N+1
• RT session N - RT session N+1
• Averaged over sessions (4)
Slide 15
Regression Results
Hypothesis: Training effects on interference may not be due to changes in relative speed in processing alone. Measures
of improvement during training may predict interference independent of relative speed.
Relative speed
Average improvement
within training sessions
Average improvement
between training
sessions
•
•
•
•
Experiment 2
Experiment 3
Creation of
interference
in color naming
Relative speed: both quadratic and linear predictors were significant
Predictors with arrows leading to the DV were significant at p < .05.
Relative speed: quadratic predictor only significant
All predictors shown were of marginal significance, p < .12
Slide 16