Comprehensive Computational Model of ACC: Expected Value of Control

Figure 1: Example of cognitive control failure

A new comprehensive computational model of dorsal anterior cingulate cortex function (dACC) was published in last week's issue of Neuron, sending shockwaves throughout the computational modeling community and sending computational modelers running to neuroscience magazinestands in droves. (That's right, I used the word droves - and you know I reserve that word only for special cases.)

The new model, published by Shenhav, Botvinick, and Cohen, attempts to unify existing models and empirical data of dACC function by modifying the traditional monitoring role usually ascribed to the dACC. In previous models of dACC function, such as error detection and conflict monitoring, the primary role of the dACC was that of a monitor involved in detecting errors, or monitoring for mutually exclusive responses and signaling the need to override prepotent but potentially wrong responses. The current model, on the other hand, suggests that the dACC monitors the expected value associated with certain responses, and weighs the potential cost of recruiting more cognitive control against the potential value (e.g., reward or other positive outcome) for implementing cognitive control.

This kind of tradeoff is best illustrated with a basic task like the Stroop task, where a color word - such as "green" - is presented in an incongruent ink, such as red. The instructions in this task are to respond to the color, and not the word; however, this is difficult since reading a word is an automatic process. Overriding this automatic tendency to respond to the word itself requires cognitive control, or strengthening task-relevant associations - in this case, focusing more on the color and not the word itself.

However, there is a drawback: using cognitive control requires effort, and effort isn't always pleasant. Therefore, it stands to reason that the positives for expending this mental effort should outweigh the negatives of using cognitive control. The following figure shows this as a series of meters with greater cognitive control going from left to right:

Figure 1B from Shenhav et al, 2013

As the meters for control signal intensity increase, so does the probability of choosing the correct option that will lead to positive feedback, as shown by the increasing thickness of the arrows from left to right. The role of the dACC, according to the model, is to make sure that the amount of cognitive control implemented is optimal: if someone always goes balls-to-the-wall with the amount of cognitive control they bring to the table, they will probably expend far more energy then would be necessary, even though they would have a much higher probability of being correct every time. (Study question: Do you know anybody like this?) Thus, the dACC attempts to reach a balance between the cognitive control needed and the value of the outcome, as shown in the middle column of the above figure.

This balance is referred to as the expected value of control (EVC): the difference between control costs and outcome values you can expect for a range of control signal intensities. The expected value can be plotted as a curve integrating both the costs and benefits of increased control, with a clear peak at the level of intensity that maximizes the difference between the expected payoff and control cost (Figure 2):

EVC curves (in blue) integrating costs and payoffs for control intensity. (Reproduced from Figure 4 from Shenhav et al, 2013)

That, in very broad strokes, is the essence of the EVC model. There are, of course, other aspects to it, including a role for the dACC in choosing the control identity which orients toward the appropriate behavior and response-outcome associations (for example, actually paying attention to the color of the stroop stimulus in the first place), which can be read about in further detail in the paper. Overall, the model seems to strike a good balance between complexity and conciseness, and the equations are relatively straightforward and should be easy to implement for anyone looking to run their own simulations.

So, the next time you see a supermodel in a bathtub full of Nutella inviting you to join her, be aware that there are several different, conflicting impulses being processed in your dorsal anterior cingulate. To wit, 1) How did this chick get in my bathtub? 2) How did she fill it up with Nutella? Do they sell that stuff wholesale at CostCo or something? and 3) What is the tradeoff between exerting enough control to just say no, given that eating that much chocolate hazelnut spread will cause me to be unable to move for the next three days, and giving in to temptation? It is a question that speaks directly to the human condition; between abjuring gluttony and the million ailments that follow on vice, and simply giving in, dragging that broad out of your bathtub and toweling the chocolate off her so you don't waste any of it, showing her the door, and then returning to the tub and plunging your insatiable maw into that chocolatey reservoir of bliss, that muddy fountain of pleasure, and inhaling pure ecstasy.