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Abstract

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.


Abstract

Slide 1


Introduction

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.


Abstract

Slide 2


Abstract

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 stimulus-response 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.


Abstract

Slide 3


Abstract

Task

“blue”

“green”

“red”

“yellow”


Abstract

Slide 4


Abstract

  • 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.


Abstract

Slide 5


Experiment 1 design

Experiment 1 – Design

A

Shape naming

Shape naming

Shape naming

Color naming

Shape naming

B

C

Color naming

Shape naming

Task sequence

Day 1 2 3 4 5 6 7

A B C B C B C

Task seq.

Boxed conditions used in ANOVA


Abstract

Slide 6


Abstract

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.


Abstract

Slide 7


Experiment 1 results

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.

Mean RT (ms)


Abstract

Slide 8


Abstract

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 speed-accuracy tradeoff.


Abstract

Slide 9


Regression analysis

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?

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.

  • Overall, the model accounted for 48% of the variance in observed color naming interference.

Relative speed of shape naming and color naming

Days of training

Number of training trials on a particular shape

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


Abstract

Slide 10


Abstract

  • 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

ERP effects of color naming

Low interference group

High interference group

Low int. High int.

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)

. 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.


Abstract

Slide 11


Experiment 2

Experiment 2

  • We hypothesized that practice can affect cognitive control in two ways:

  • Strengthening / speeding an irrelevant stimulus-response association

  • 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.


Abstract

Slide 12


Experiment 2 design

Experiment 2 Design

Shape naming

Shape naming

Shape naming

Color naming

Color naming

Color naming

Color naming

Shape naming

Shape naming

Shape naming

Group 1

Session 1

Session 3

Session 2

Group 2

Boxed conditions used in ANOVA


Abstract

Slide 13


Experiment 2 anova results

Experiment 2: ANOVA Results

  • Hypothesis: the presence of irrelevant colors during shape naming practice may create interference with color naming.

  • Subjects were both slower and less accurate to name colors when they appeared within incongruent shapes

  • Interference was created only when subjects saw colored shapes during training

Group 1

Group 2


Abstract

Slide 14


Regression analysis1

Regression Analysis

  • To compare practice effects to effects of relative speed after practice, we measured improvement during training in two ways:

  • Improvement (decrease in response time) between blocks of 20 trials within a session.

  • 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)


Abstract

Slide 15


Regression results

Relative speed

Creation of interference

in color naming

Average improvement within training sessions

Average improvement between training sessions

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.

Experiment 2

Experiment 3

  • 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


Abstract

Slide 16


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