Matthew Botvinick

Conflict monitoring and cognitive control.

A neglected question regarding cognitive control is how control processes might detect situations calling for their involvement. The authors propose here that the demand for control may be evaluated in part by monitoring for conflicts in information processing. This hypothesis 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. The present article reports two computational modeling studies, serving to articulate the conflict monitoring hypothesis and examine its implications. The first study tests the sufficiency of the hypothesis to account for brain activation data, applying a measure of conflict to existing models of tasks shown to engage the anterior cingulate. The second study implements a feedback loop connecting conflict monitoring to cognitive control, using this to simulate a number of important behavioral phenomena.

Conflict monitoring and anterior cingulate cortex: an update

One hypothesis concerning the human dorsal anterior cingulate cortex (ACC) is that it functions, in part, to signal the occurrence of conflicts in information processing, thereby triggering compensatory adjustments in cognitive control. Since this idea was first proposed, a great deal of relevant empirical evidence has accrued. This evidence has largely corroborated the conflict-monitoring hypothesis, and some very recent work has provided striking new support for the theory. At the same time, other findings have posed specific challenges, especially concerning the way the theory addresses the processing of errors. Recent research has also begun to shed light on the larger function of the ACC, suggesting some new possibilities concerning how conflict monitoring might fit into the cingulates overall role in cognition and action.

The Neural Basis of Error Detection : Conflict Monitoring and the Error-Related Negativity

According to a recent theory, anterior cingulate cortex is sensitive to response conflict, the coactivation of mutually incompatible responses. The present research develops this theory to provide a new account of the error-related negativity (ERN), a scalp potential observed following errors. Connectionist simulations of response conflict in an attentional task demonstrated that the ERN--its timing and sensitivity to task parameters--can be explained in terms of the conflict theory. A new experiment confirmed predictions of this theory regarding the ERN and a second scalp potential, the N2, that is proposed to reflect conflict monitoring on correct response trials. Further analysis of the simulation data indicated that errors can be detected reliably on the basis of post-error conflict. It is concluded that the ERN can be explained in terms of response conflict and that monitoring for conflict may provide a simple mechanism for detecting errors.

The Expected Value of Control: An Integrative Theory of Anterior Cingulate Cortex Function

The dorsal anterior cingulate cortex (dACC) has a near-ubiquitous presence in the neuroscience of cognitive control. It has been implicated in a diversity of functions, from reward processing and performance monitoring to the execution of control and action selection. Here, we propose that this diversity can be understood in terms of a single underlying function: allocation of control based on an evaluation of the expected value of control (EVC). We present a normative model of EVC that integrates three critical factors: the expected payoff from a controlled process, the amount of control that must be invested to achieve that payoff, and the cost in terms of cognitive effort. We propose that dACC integrates this information, using it to determine whether, where and how much control to allocate. We then consider how the EVC model can explain the diverse array of findings concerning dACC function.

Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function.

According to one influential account, the anterior cingulate cortex (ACC) serves to monitor for conflicts in information processing. According to another influential account, the ACC monitors action outcomes and guides decision making. Both of these perspectives are supported by an abundance of data, making it untenable to reject one view in favor of the other. Instead, the apparent challenge is to discover how the two perspectives might fit together within a larger account. In the present article, we consider the prospects for such a reconciliation. Juxtaposing the conflict-monitoring and decision-making accounts suggests an extension of the conflict-monitoring theory, by which conflict would act as a teaching signal driving a form of avoidance learning. The effect of this mechanism would be to bias behavioral decision making toward cognitively efficient tasks and strategies. We discuss evidence favoring this proposal and present an initial computational model, which lays the foundation for further development.

Anterior Cingulate Cortex, Conflict Monitoring, and Levels of Processing

It has been hypothesized that the anterior cingulate cortex (ACC) contributes to cognition by detecting conflicts that might occur during information processing, to signal the need to engage top-down attentional processes. The present study was designed to investigate which levels of processing are being monitored by the ACC for the presence of conflict. Event-related fMRI was used to measure the response of the ACC during an interference task in which distracting information could be congruent, conflicting at the level of stimulus identification, or conflicting at the response level. Although both types of conflict caused reaction time interference, the fMRI data showed that the ACC is responsive only to response conflict, even when controlling for reaction times. These results suggest a highly specific contribution of the ACC to executive functions, through the detection of conflicts occurring at later or response-related levels of processing.

Decision Making and the Avoidance of Cognitive Demand

Behavioral and economic theories have long maintained that actions are chosen so as to minimize demands for exertion or work, a principle sometimes referred to as the law of less work. The data supporting this idea pertain almost entirely to demands for physical effort. However, the same minimization principle has often been assumed also to apply to cognitive demand. The authors set out to evaluate the validity of this assumption. In 6 behavioral experiments, participants chose freely between courses of action associated with different levels of demand for controlled information processing. Together, the results of these experiments revealed a bias in favor of the less demanding course of action. The bias was obtained across a range of choice settings and demand manipulations and was not wholly attributable to strategic avoidance of errors, minimization of time on task, or maximization of the rate of goal achievement. It is remarkable that the effect also did not depend on awareness of the demand manipulation. Consistent with a motivational account, avoidance of demand displayed sensitivity to task incentives and covaried with individual differences in the efficacy of executive control. The findings reported, together with convergent neuroscientific evidence, lend support to the idea that anticipated cognitive demand plays a significant role in behavioral decision making.

Hierarchically Organized Behavior and Its Neural Foundations: A Reinforcement Learning Perspective.

Research on human and animal behavior has long emphasized its hierarchical structure-the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely considered to reflect prefrontal cortical functions. In this paper, we reexamine behavioral hierarchy and its neural substrates from the point of view of recent developments in computational reinforcement learning. Specifically, we consider a set of approaches known collectively as hierarchical reinforcement learning, which extend the reinforcement learning paradigm by allowing the learning agent to aggregate actions into reusable subroutines or skills. A close look at the components of hierarchical reinforcement learning suggests how they might map onto neural structures, in particular regions within the dorsolateral and orbital prefrontal cortex. It also suggests specific ways in which hierarchical reinforcement learning might provide a complement to existing psychological models of hierarchically structured behavior. A particularly important question that hierarchical reinforcement learning brings to the fore is that of how learning identifies new action routines that are likely to provide useful building blocks in solving a wide range of future problems. Here and at many other points, hierarchical reinforcement learning offers an appealing framework for investigating the computational and neural underpinnings of hierarchically structured behavior.

Hierarchical models of behavior and prefrontal function.

The recognition of hierarchical structure in human behavior was one of the founding insights of the cognitive revolution. Despite decades of research, however, the computational mechanisms underlying hierarchically organized behavior are still not fully understood. Recent findings from behavioral and neuroscientific research have fueled a resurgence of interest in the problem, inspiring a new generation of computational models. In addition to developing some classic proposals, these models also break fresh ground, teasing apart different forms of hierarchical structure, placing a new focus on the issue of learning and addressing recent findings concerning the representation of behavioral hierarchies within the prefrontal cortex. In addition to offering explanations for some key aspects of behavior and functional neuroanatomy, the latest models also pose new questions for empirical research.

The conflict adaptation effect: it's not just priming.

Analyses of trial sequences in flanker tasks have revealed cognitive adaptation, reflected in a reduced interference effect following incompatible trials (Gratton, Coles, & Donchin, 1992). These effects have been explained on the basis of the response conflict monitoring model of Botvinick, Braver, Barch, Carter, and Cohen (2001), who proposed that preceding response conflict triggers stronger topdown control, leading to performance improvements on subsequent trials of similar context. A recent study (Mayr, Awh, & Laurey, 2003) has challenged this account, suggesting that the behavioral adaptations are confined to trial sequences of exact trial repetitions and can therefore be explained by repetition priming. Here, we present two experiments in which the sequential dependency effect was present even on trial sequences that did not involve stimulus repeats. We discuss the data with respect to the conflict-monitoring and repetition-priming accounts.