10.4: Top-down Cognitive Control from Sustained PFC Firing- The Stroop Model
- Page ID
- 12625
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)We now turn to a series of computer simulations to explore various facets of executive function. We begin with perhaps the single most studied task used to test for executive function, the Stroop task, named after John Ridley Stroop, who first described the basic phenomenon back in 1935 (Stroop, J. R., 1935). The computational model of this task, developed initially by Cohen, Dunbar & McClelland (1990), has been applied (with appropriate change of labels) to a remarkably wide range of different phenomena. Thus, this deceptively simple task and model capture the most critical features of executive function.


In the Stroop paradigm (Figure 10.14) subjects are presented with color words (e.g., "red", "green") one at a time and are required to either read the word (e.g., "red"), or name the color of the ink that the word is written in. Sometimes the word "red" appears in green ink, which represents the incongruent or conflict condition. The "Stroop effect" is that error rates and response times are larger for this incongruent condition, especially in the case of color naming (Figure 10.15). That color naming is particularly difficult in the incongruent condition has been attributed to the relatively "automatic", well-practiced nature of reading words, so that the natural tendency to read the word interferes with attending to, and naming, the color of the ink.
The Cohen et al. (1990) Stroop model showed how a maintained PFC representation can provide a strong top-down bias to support the weaker color processing channel in the face of the stronger word-reading pathway. They were able to establish the difference between word reading and color naming simply as a function of the amount of training provided on each of these tasks. Our simulation reproduces these same core features.
The Stroop model helps clarify the role of inhibition in executive function. Many people describe the Stroop task as requiring people to inhibit the prepotent word reading pathway, in order to focus on the ink color, and the model also does involve inhibitory dynamics. However, the PFC in the model does not provide a directed form of inhibition to the word reading pathway specifically. Instead, it provides excitatory top-down support to the weaker pathway (color naming), which then enables this pathway to better compete (via lateral inhibitory interactions) with the more dominant word reading pathway. Thus, inhibition is seen as a more collateral, automatic process operating throughout the cortex, and top-down biasing is involved in exciting relevant information, rather than inhibiting irrelevant information.
Open the Stroop model and follow the directions from there.