Academic journal article Cognitive, Affective and Behavioral Neuroscience

Probing Human and Monkey Anterior Cingulate Cortex in Variable Environments

Academic journal article Cognitive, Affective and Behavioral Neuroscience

Probing Human and Monkey Anterior Cingulate Cortex in Variable Environments

Article excerpt

Previous research has identified the anterior cingulate cortex (ACC) as an important node in the neural network underlying decision making in primates. Decision making can, however, be studied under a large variety of circumstances, ranging from the standard well-controlled lab situation to more natural, stochastic settings, in which multiple agents interact. Here, we illustrate how these different varieties of decision making studied can influence theories of ACC function in monkeys. Converging evidence from unit recordings and lesion studies now suggest that the ACC is important for interpreting outcome information according to the current task context to guide future action selection. We then apply this framework to the study of human ACC function and discuss its potential implications.

Human and animal decision making consists of the combined cognitive processes that lead to the selection of a course of action among alternatives. In order to complete these processes successfully, a decision maker (agent) needs to identify the current environmental state, the agent's own behavioral goals given this environmental state, and the agent's own internal state; compute the relative contribution of each action toward obtaining this goal; and finally select and execute the most appropriate action.

A large network of regions in the frontal cortex and basal ganglia of the primate brain has been shown to be involved in the selection of goal-directed actions (Passingham, 1993), but the precise role of each node in this network remains a topic of ongoing debate. A central role in decision making has been attributed to the anterior cingulate cortex (ACC), which, it has been suggested, is involved in executive attention, supervisory attentional control, selection for action, conflict detection, and several varieties of performance monitoring (e.g., Botvinick, Braver, Barch, Carter, & Cohen, 2001; Posner & DiGirolamo, 1998; Posner, Petersen, Fox, & Raichle, 1988; Ridderinkhof, Nieuwenhuis, Crone, & Ullsperger, 2004). Rather than incorporating all these functions within a single framework by attributing to the ACC the role of an all-powerful homunculus presiding over action selection, the challenge is to find a common denominator of these functions that accurately describes the role of the ACC hi decision making.

In this article, we examine how this challenge has been taken up in the study of decision making in primates. Our mam thesis is that there are various circumstances in which decision making can be studied, and that the specific variety of circumstances studied has a strong influence on the conclusions that can be drawn. We will concentrate specifically and separately on the ideas to emerge from work on nonhuman and human primates, and illustrate the differences in approaches in studying these different species. We will start by illustrating the various types of decision making that can be identified and briefly describe the anatomical underpinnings of ACC function. Following this, we will illustrate the paradigms employed in monkey ACC research within this decision-making framework and discuss the conclusions that can be drawn. Finally, we will apply this knowledge to the study of human ACC function.

Varieties of Decision Making

Figure 1 illustrates some of the varieties of decision making referred to in this article. In its simplest form, decision making is concerned with a single agent, acting in a stable environment and having complete information (Figure IA). In these circumstances decision making, although not trivial, is relatively straightforward. Given a particular stimulus, the agent knows that a certain action will lead to a certain reward, whereas the alternative action will not. Sometimes the properties of the environment are not known and have to be obtained through learning. Given the stability of the environment, however, this learning can be relatively straightforward, such as simple trial-and-error learning based on external feedback. …

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