Conflict Monitoring and Cognitive Control (Botvinick et al., 2001)

“Conflict Monitoring and Cognitive Control”

Overview

This implements a model concerning the role of the anterior cingulate cortex (ACC) in situations calling for their involvement. The demand for control is evaluated by monitoring for conflicts in information processing. The model is supported by data concerning the anterior cingulate cortex, a brain area involved in cognitive control, which also appears to respond to the occurrence of conflict. This script implements the system and plot from Figure 1 of the conflict monitoring and cognitive control paper.

The Model

The model is built as the bidirectional Stroop model by Cohen & Huston (1994): stimulus information in two hidden layers (color, word & neutral), and task information (color naming or word reading) are connected with each other in a bidirectional way. The response layer receives inputs from both hidden layers. A graph of the model is shown below.

Network System

COLOR INPUT LAYER: a TransferMechanism with input_shapes = 3 (one unit for the input of one color, respectively here blue & green), and assigned a Linear function with slope = 1.0 and intercept = 0.0.

WORD INPUT LAYER: a TransferMechanism with input_shapes = 3 (one unit for the input of one word, respectively, here blue & green), and assigned a Linear function with slope = 1.0 and intercept = 0.0.

TASK INPUT LAYER: a TransferMechanism with input_shapes = 2 (one unit specified with a task value of one, the other element set to zero), and assigned a Linear function with slope = 1.0 and intercept = 0.0.

COLOR HIDDEN LAYER: a RecurrentTransferMechanism with input_shapes = 3 (one element for each of the two colors, one element for the neutral color and assigned a Logistic function with gain = 4.0 and bias = 1.0. The integrator_mode = True and smoothing_factor = 0.01. Both units receive mutually inhibitory weights (hetero = -2).

WORD HIDDEN LAYER: a RecurrentTransferMechanism with input_shapes = 3 (one element for each of the two colors, one element for the neutral color and assigned a Logistic function with gain = 4.0 and bias = 1.0. The integrator_mode = True and smoothing_factor = 0.01. Both units receive mutually inhibitory weights (hetero = -2).

TASK DEMAND LAYER: a RecurrentTransferMechanism with input_shapes = 2 (one element for each of the two tasks, and assigned a Logistic function with gain = 1.0 and bias = 0.0. The integrator_mode = True and smoothing_factor = 0.01. Both units receive mutually inhibitory weights (hetero = -2).

RESPONSE LAYER: a RecurrentTransferMechanism with input_shapes = 2 (one element for each of the two responses, and assigned a Logistic function with gain = 1.0 and bias = 0.0. The integrator_mode = True and smoothing_factor = 0.01. Both units receive mutually inhibitory weights (hetero = -2).

PROJECTIONS: The weights of the network are implemented as MappingProjections. The matrix parameter from the COLOR INPUT_LAYER, the WORD INPUT_LAYER, and the BIAS INPUT_LAYER to the COLOR HIDDEN LAYER and WORD HIDDEN LAYER are all set with a numpy array with a value of 1.0 for the diagonal elements and all off-diagonal elements are set to 0. The color hidden layer projects to the TASK LAYER with a numpy array with a value of 4.0 on the the first column, and 0.0 on the second column, and receive inputs from the TASK LAYER with a numpy array with a value of 4.0 on the first row and a value of 0.0 in the second row. The word hidden layer projects to the TASK LAYER with a numpy array with a value of 4.0 on the the second column, and 0.0 on the first column, and receive inputs from the TASK LAYER with a numpy array with a value of 4.0 on the second row and a value of 0.0 in the first row. The RESPONSE LAYER receives projections from two layers: the COLOR HIDDEN LAYER with a numpy array with a value of 1.5 on the diagonal elements and 0.0 on the off-diagonal elements. The WORD HIDDEN LAYER with a numpy array with a value of 2.5 on the diagonal elements and 0.0 on the off-diagonal elements.

Execution

All units are set to zero at the beginning of the simulation. Each simulation run starts with a settling period of 500 time steps. Then the stimulus is presented for 1000 time steps and is presented by setting the input units to 1.0 for a given trial. Conflict is computed on the output_port of the RESPONSE LAYER. The figure plots conflict over one trial for each of the three conditions. The log function is used to record the output values of RESPONSE LAYER. These values are used to produce the plot of the Figure.

Please note:

Note that this script implements a slightly different Figure than in the original Figure in the paper. However, this implementation is identical with a plot we created with an old MATLAB code which was used for the conflict monitoring simulations.

Script: Botvinick_conflict_monitoring_model.py