Nils Kolling

Neural Mechanisms of Foraging

Looking for Greener Pastures Humans, like other animals, have evolved to forage. Brain-imaging studies by Kolling et al. (p. 95) suggest that activity in the dorsal anterior cingulate cortex supplies a continuous signal of environmental richness predicted by foraging theory. The signal exhibits a frame of reference that is tied to the key foraging decision of whether to engage with the current choice or to search for alternatives. The same strategy is used when humans are making other types of decisions. In contrast, the ventromedial prefrontal cortex, a brain region that lacks any signals pertinent to foraging, encodes choice values in a manner uninfluenced by environmental richness. A brain signal in the dorsal anterior cingulate cortex tracks the average value of a person’s environment. Behavioral economic studies involving limited numbers of choices have provided key insights into neural decision-making mechanisms. By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey or food. On each encounter, the animal chooses whether to engage or, if the environment is sufficiently rich, to search elsewhere. The cost of foraging is also critical. We demonstrate that humans can alternate between two modes of choice, comparative decision-making and foraging, depending on distinct neural mechanisms in ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC) using distinct reference frames; in ACC, choice variables are represented in invariant reference to foraging or searching for alternatives. Whereas vmPFC encodes values of specific well-defined options, ACC encodes the average value of the foraging environment and cost of foraging.

Mechanisms underlying cortical activity during value-guided choice.

When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity that is fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. We investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions using magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.

Valuation and decision-making in frontal cortex: one or many serial or parallel systems?

We evaluate the merits of different conceptualizations of frontal cortex function in value-guided decision-making. According to one view each frontal cortical region is concerned with a different aspect of the process of learning about and evaluating choices and then selecting actions. An alternative view, however, sees sets of decision-making circuits working in parallel within the frontal lobes in order to make different types of decisions. While there is a neural circuit for making choices between pairs of simultaneously presented items in the manner that is frequently assessed in the laboratory, there is also evidence that other frontal lobe circuits have evolved to make other types of choices such as those made during the course of foraging.

Re-evaluating the role of the orbitofrontal cortex in reward and reinforcement.

The orbitofrontal cortex and adjacent ventromedial prefrontal cortex carry reward representations and mediate flexible behaviour when circumstances change. Here we review how recent experiments in humans and macaques have confirmed the existence of a major difference between the functions of the ventromedial prefrontal cortex and adjacent medial orbitofrontal cortex (mOFC) on the one hand and the lateral orbitofrontal cortex (lOFC) on the other. These differences, however, may not be best accounted for in terms of specializations for reward and error/punishment processing as is commonly assumed. Instead we argue that both lesion and functional magnetic resonance imaging studies reveal that the lOFC is concerned with the assignment of credit for both reward and error outcomes to the choice of specific stimuli and with the linking of specific stimulus representations to representations of specific types of reward outcome. By contrast, we argue that the ventromedial prefrontal cortex/mOFC is concerned with evaluation, value‐guided decision‐making and maintenance of a choice over successive decisions. Despite the popular view that they cause perseveration of behaviour and inability to inhibit repetition of a previously made choice, we found that lesions in neither orbitofrontal subdivision caused perseveration. On the contrary, lesions in the lOFC made animals switch more rapidly between choices when they were finding it difficult to assign reward values to choices. Lesions in the mOFC caused animals to lose their normal predisposition to repeat previously successful choices, suggesting that the mOFC does not just mediate value comparison in choice but also facilitates maintenance of the same choice if it has been successful.

Multiple signals in anterior cingulate cortex.

Highlights • There are multiple signals in anterior cingulate cortex (ACC).• ACC activity reflects value of behavioural change even after controlling for difficulty.• ACC activity reflects updating of internal models even after controlling for difficulty.

Value, search, persistence and model updating in anterior cingulate cortex

Dorsal anterior cingulate cortex (dACC) carries a wealth of value-related information necessary for regulating behavioral flexibility and persistence. It signals error and reward events informing decisions about switching or staying with current behavior. During decision-making, it encodes the average value of exploring alternative choices (search value), even after controlling for response selection difficulty, and during learning, it encodes the degree to which internal models of the environment and current task must be updated. dACC value signals are derived in part from the history of recent reward integrated simultaneously over multiple time scales, thereby enabling comparison of experience over the recent and extended past. Such ACC signals may instigate attentionally demanding and difficult processes such as behavioral change via interactions with prefrontal cortex. However, the signal in dACC that instigates behavioral change need not itself be a conflict or difficulty signal.

Multiple Neural Mechanisms of Decision Making and Their Competition under Changing Risk Pressure

Summary Sometimes when a choice is made, the outcome is not guaranteed and there is only a probability of its occurrence. Each individual’s attitude to probability, sometimes called risk proneness or aversion, has been assumed to be static. Behavioral ecological studies, however, suggest such attitudes are dynamically modulated by the context an organism finds itself in; in some cases, it may be optimal to pursue actions with a low probability of success but which are associated with potentially large gains. We show that human subjects rapidly adapt their use of probability as a function of current resources, goals, and opportunities for further foraging. We demonstrate that dorsal anterior cingulate cortex (dACC) carries signals indexing the pressure to pursue unlikely choices and signals related to the taking of such choices. We show that dACC exerts this control over behavior when it, rather than ventromedial prefrontal cortex, interacts with posterior cingulate cortex.

A neural mechanism underlying failure of optimal choice with multiple alternatives

Despite widespread interest in neural mechanisms of decision-making, most investigations focus on decisions between just two options. Here we adapt a biophysically plausible model of decision-making to predict how a key decision variable, the value difference signal—encoding how much better one choice is than another—changes with the value of a third, but unavailable, alternative. The model predicts a surprising failure of optimal decision-making: greater difficulty choosing between two options in the presence of a third very poor, as opposed to very good, alternative. Both investigation of human decision-making and functional magnetic resonance imaging–based measurements of value difference signals in ventromedial prefrontal cortex (vmPFC) bore out this prediction. The vmPFC signal decreased in the presence of low-value third alternatives, and vmPFC effect sizes predicted individual variation in suboptimal decision-making in the presence of multiple alternatives. The effect contrasts with that of divisive normalization in parietal cortex.

Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control

We discuss a recent approach to investigating cognitive control, which has the potential to deal with some of the challenges inherent in this endeavour. In a model-based approach, the researcher defines a formal, computational model that performs the task at hand and whose performance matches that of a research participant. The internal variables in such a model might then be taken as proxies for latent variables computed in the brain. We discuss the potential advantages of such an approach for the study of the neural underpinnings of cognitive control and its pitfalls, and we make explicit the assumptions underlying the interpretation of data obtained using this approach.

Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex

In many natural environments the value of a choice gradually gets better or worse as circumstances change. Discerning such trends makes predicting future choice values possible. We show that humans track such trends by comparing estimates of recent and past reward rates, which they are able to hold simultaneously in the dorsal anterior cingulate cortex (dACC). Comparison of recent and past reward rates with positive and negative decision weights is reflected by opposing dACC signals indexing these quantities. The relative strengths of time-linked reward representations in dACC predict whether subjects persist in their current behaviour or switch to an alternative. Computationally, trend-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-term estimate (or expectation) of prediction errors. Using such a model, we find a relative predominance of expected prediction errors in dACC, instantaneous prediction errors in the ventral striatum and choice signals in the ventromedial prefrontal cortex.