Neural Mechanisms of Foraging

Self Cite

343

388

Reward-guided decision-making depends on a network of brain regions. Among these are the orbitofrontal and the anterior cingulate cortex. However, it is difficult to ascertain if these areas constitute anatomical and functional unities, and how these areas correspond between monkeys and humans. To address these questions we looked at connectivity profiles of these areas using resting-state functional MRI in 38 humans and 25 macaque monkeys. We sought brain regions in the macaque that resembled 10 human areas identified with decision making and brain regions in the human that resembled six macaque areas identified with decision making. We also used diffusion-weighted MRI to delineate key human orbital and medial frontal brain regions. We identified 21 different regions, many of which could be linked to particular aspects of reward-guided learning, valuation, and decision making, and in many cases we identified areas in the macaque with similar coupling profiles.orbitofrontal cortex | anterior cingulate cortex | decision making | resting state functional connectivity | comparative anatomy A s humans we make decisions by taking into account different types of information, weighing our options carefully, and eventually coming to a conclusion. We then learn from witnessing the outcome of our decisions. Human functional MRI (fMRI) has had a major impact on elucidating the neural networks mediating decision making and learning, but key insights can only be obtained in neural recording, stimulation, and focal lesion studies conducted in animal models, such as the macaque. Combining insights from human fMRI and animal studies is, however, not straightforward because there is uncertainty about basic issues, such as anatomical and functional correspondences between species (1). For example, although there are many reports of decision value-related activity in the human ventromedial prefrontal cortex (vmPFC) (2, 3), it is unclear whether they can be related to reports of reward-related activity either on the ventromedial surface of the frontal lobe (4, 5), in the adjacent medial orbitofrontal sulcus (6), or indeed to any macaque brain area. It is claimed that some areas implicated in rewardguided decision making and learning, such as parts of anterior cingulate cortex (ACC), are not found in macaques (7), but such theories have never been formally tested.In addition, there is uncertainty about the basic constituent components of decision-making and learning circuits. To return to the example of the vmPFC, although this region is often contrasted with similarly large subdivisions of the frontal cortex, such as the lateral orbitofrontal cortex (lOFC) and ACC (8), it is unclear whether, and if so how, it should be decomposed into further subdivisions. Moreover, there are sometimes fundamental disagreements about how brain areas contribute to decision making and learning. For example, it has been claimed both that the ACC does (9-11) and does not (12) contribute to reward-based decision making and that it is concerned with dist...

Section: Resultssupporting

confidence: 55%

“…In a further analysis, we examined an even more dorsal perigenual region linked to individual variation in cost-benefit decision making (figure 4d in ref. 10, and in the supplementary information in ref. 37), and found that it was associated with a similar pattern of coupling (SI Appendix, Fig.…”

Section: Resultsmentioning

confidence: 99%

Connectivity reveals relationship of brain areas for reward-guided learning and decision making in human and monkey frontal cortex

Neubert

Mars

Sallet

et al. 2015

Self Cite

343

388

“…The first process might reflect the intervention of an opponent motivation signal that would be continuously subtracted to cost evidence throughout effort and rest periods. This signal might come from brain regions involved in reward processing, or in the trade-off between reward and effort, such as the ventral striatum, the anterior cingulate cortex, or the ventromedial prefrontal cortex (9,32,33). The second process might implement the intuitive psychological phenomenon that, when motivated, we literally push back our limits, allowing our body to work closer from exhaustion.…”

Section: Discussionmentioning

confidence: 99%

Neurocomputational account of how the human brain decides when to have a break

Meyniel

Sergent

Rigoux

et al. 2013

119

No pain, no gain: cost–benefit trade-off has been formalized in classical decision theory to account for how we choose whether to engage effort. However, how the brain decides when to have breaks in the course of effort production remains poorly understood. We propose that decisions to cease and resume work are triggered by a cost evidence accumulation signal reaching upper and lower bounds, respectively. We developed a task in which participants are free to exert a physical effort knowing that their payoff would be proportional to their effort duration. Functional MRI and magnetoencephalography recordings conjointly revealed that the theoretical cost evidence accumulation signal was expressed in proprioceptive regions (bilateral posterior insula). Furthermore, the slopes and bounds of the accumulation process were adapted to the difficulty of the task and the money at stake. Cost evidence accumulation might therefore provide a dynamical mechanistic account of how the human brain maximizes benefits while preventing exhaustion.

“…Like intertemporal decision making, efficient foraging involves striking a balance between temporal costs and outcome value (8,16,(19)(20)(21). The attractive features of the foraging framework afford great potential for integrating the behavioral ecological perspective with neuroscientific and psychological investigations of behavior, an approach that has proven fruitful previously (7,(22)(23)(24)(25)(26).…”

mentioning

confidence: 99%

Subjective costs drive overly patient foraging strategies in rats on an intertemporal foraging task

Wikenheiser¹,

Stephens

Redish

2013

110

Laboratory studies of decision making often take the form of twoalternative, forced-choice paradigms. In natural settings, however, many decision problems arise as stay/go choices. We designed a foraging task to test intertemporal decision making in rats via stay/go decisions. Subjects did not follow the rate-maximizing strategy of choosing only food items associated with short delays. Instead, rats were often willing to wait for surprisingly long periods, and consequently earned a lower rate of food intake than they might have by ignoring long-delay options. We tested whether foraging theory or delay discounting models predicted the behavior we observed but found that these models could not account for the strategies subjects selected. Subjects' behavior was well accounted for by a model that incorporated a cost for rejecting potential food items. Interestingly, subjects' cost sensitivity was proportional to environmental richness. These findings are at odds with traditional normative accounts of decision making but are consistent with retrospective considerations having a deleterious influence on decisions (as in the "sunk-cost" effect). More broadly, these findings highlight the utility of complementing existing assays of decision making with tasks that mimic more natural decision topologies.