Anticipatory dynamics in the human brain guide foraging for primary rewards

Foraging is a fundamental food-seeking behavior in a wide range of species that enables survival in an uncertain world. During foraging, behavioral agents constantly face a trade-off between staying in their current location or exploring another. Despite ethological generality and importance of foraging, it remains unclear how the human brain guides continuous decision in such situations. Here we show that anticipatory activity dynamics in the anterior prefrontal cortex (aPFC) and hippocampus underpin foraging for primary rewards. While functional MRI was performed, humans foraged for real liquid rewards available after tens of seconds, and continuous decision during foraging was tracked by a dynamic pattern of brain activity that reflected anticipation of a future reward. When the dynamic anticipatory activity in the aPFC was enhanced, humans remained in their current environment, but when this activity diminished, they explored a new environment. Moreover, the anticipatory activity in the aPFC and hippocampus was associated with distinct decision strategies: aPFC activity was enhanced in humans adopting an exploratory strategy, whereas those remaining stationary showed enhanced activity in the hippocampus. Our results suggest that anticipatory dynamics in the fronto-hippocampal mechanisms underlie continuous decision-making during human foraging.

Building Bayesian Cognitive Models of Visual Foraging

Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in `runs' of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, confounding any inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area.

Model-based foraging using latent-cause inference

Foraging has been suggested to provide a more ecologicallyvalidcontext for studying decision-making. However, the environmentsused in human foraging tasks fail to capture thestructure of real world environments which contain multiplelevels of spatio-temporal regularities. We ask if foragers detectthese statistical regularities and use them to construct amodel of the environment that guides their patch-leaving decisions.We propose a model of how foragers might accomplishthis, and test its predictions in a foraging task with a structuredenvironment that includes patches of varying quality andpredictable transitions. Here, we show that human foragingdecisions reflect ongoing, statistically-optimal structure learning.Participants modulated decisions based on the current andpotential future context. From model fits to behavior, we canidentify an individual’s structure learning ability and relate itto decision strategy. These findings demonstrate the utility ofleveraging model-based reinforcement learning to understandforaging behavior.

Efficient Lévy walks in virtual human foraging

Efficient foraging depends on decisions that account for the costs and benefits of various activities like movement, perception, and planning. We conducted a virtual foraging experiment set in the foothills of the Himalayas to examine how time and energy are expended to forage efficiently, and how foraging changes when constrained to a home range. Two hundred players foraged the human-scale landscape with simulated energy expenditure in search of naturally distributed resources. Results showed that efficient foragers produced periods of locomotion interleaved with perception and planning that approached theoretical expectations for Lévy walks, regardless of the home-range constraint. Despite this constancy, efficient home-range foraging trajectories were less diffusive by virtue of restricting locomotive search and spending more time instead scanning the environment to plan movement and detect far-away resources. Altogether, results demonstrate that humans can forage efficiently by arranging and adjusting Lévy-distributed search activities in response to environmental and task constraints.

The foraging gene affects alcohol sensitivity, metabolism and memory in Drosophila

The genetic basis of alcohol use disorder (AUD) is complex. Understanding how natural genetic variation contributes to alcohol phenotypes can help identify mechanisms underlying the genetic contribution of AUD. Recently, a single nucleotide polymorphism in the human foraging (for) gene ortholog, Protein Kinase cGMP-Dependent 1 (PRKG1), was found to be associated with stress-induced risk for alcohol abuse. However, the mechanistic role that PRKG1 plays in AUD is not well understood. We use natural variation in the Drosophila for gene to describe how variation of cGMP-dependent protein kinase (PKG) activity modifies ethanol-induced phenotypes. We found that variation in for affects ethanol-induced increases in locomotion and memory of the appetitive properties of ethanol intoxication. Further, these differences may stem from the ability to metabolize ethanol. Together, this data suggests that natural variation in PKG modulates cue reactivity for alcohol, and thus could influence alcohol cravings by differentially modulating metabolic and behavioral sensitivities to alcohol.

The Predation Game: Does dividing attention affect patterns of human foraging?

Attention is known to play an important role in shaping the behaviour of both human and animal foragers. Here, in two experiments, we built on our previous interactive tasks to create an online foraging game for studying divided attention in human participants exposed to the (simulated) risk of predation. Participants used a “sheep” object to collect items from different target categories randomly distributed across the display. Each trial also contained “wolf” objects, whose movement was inspired by classic studies of multiple object tracking. For “hunted” participants, collision with any wolf terminated the trial, making the need to monitor and avoid the predators crucial to success. For “distracted” participants, the wolf objects did not interact with the sheep, and could effectively be ignored. In Experiment 1, we used an established Feature/Conjunction manipulation to vary the difficulty of target selection. In Experiment 2, we varied the value and the prevalence of target items to examine potential trade-offs between risk and reward. In both experiments, we found very clear differences between the foraging patterns of hunted versus distracted participants. We were also able to replicate basic foraging patterns associated with target complexity and reward, respectively. Unexpectedly, hunted participants did not show a tendency to restricting their search to a single category, the hallmark of attention limited foraging. Rather, they were more likely to select from all available categories, compared to the distracted participants. Such behaviour is consistent with the idea that risk of predation in our task modulated levels of alertness/arousal, counteracting the costs of having to both select targets and monitor for wolves. While the effects of phasic changes in alertness and arousal are well captured in standard capacity models of attention and are also central to recent attempts to explain individual differences in human performance they do not as yet play a major role in the attention models applied to either human or animal foraging.

Foraging tempo: Human run patterns in multiple-target search are constrained by the rate of successive responses

Human foraging tasks are beginning to provide new insights into the roles of vision, attention, and working memory during complex, multiple-target search. Here, we test the idea that “foraging tempo”—the rate of successive target selections—helps determine patterns of behaviour in these tasks. Previously, we established that the majority of target selections during unconstrained foraging happen at regular, rapid intervals, forming the “cruise phase” of a foraging trial. Furthermore, we noted that when the temporal interval between cruise phase responses was longer, the tendency to switch between target categories increased. To directly explore this relationship, we modified our standard iPad foraging task so that observers had to synchronise each response with an auditory metronome signal. Across trials, we increased the tempo and examined how this changed patterns of foraging when targets were defined either by a single feature or by a conjunction of features. The results were very clear. Increasing tempo systematically decreased the tendency for participants to switch between target categories. Although this was true for both feature and conjunction trials, there was also evidence that time constraints and target complexity interacted. As in our previous work, we also observed clear individual differences in how participants responded to changes in task difficulty. Overall, our results show that foraging tempo does influence the way participants respond, and we suggest this parameter may prove to be useful in further explorations of group and individual strategies during multiple-target search.

Moving foraging into three dimensions: Feature- versus conjunction-based foraging in virtual reality

Visual attention evolved in a three-dimensional (3D) world, yet studies on human attention in three dimensions are sparse. Here we present findings from a human foraging study in immersive 3D virtual reality. We used a foraging task introduced in Kristjánsson et al. to examine how well their findings generalise to more naturalistic settings. The second goal was to examine what effect the motion of targets and distractors has on inter-target times (ITTs), run patterns, and foraging organisation. Observers foraged for 50 targets among 50 distractors in four different conditions. Targets were distinguished from distractors by either a single feature (feature foraging) or a conjunction of features (conjunction foraging). Furthermore, those conditions were performed both with static and moving targets and distractors. Our results replicate previous foraging studies in many aspects, with constant ITTs during a “cruise-phase” within foraging trials and response time peaks at the end of foraging trials. Some key differences emerged, however, such as more frequent switches between target types during conjunction foraging than previously seen and a lack of clear mid-peaks during conjunction foraging, possibly reflecting that differences between feature and conjunction processing are smaller within 3D environments. Observers initiated their foraging in the bottom part of the visual field and motion did not have much of an effect on selection times between different targets (ITTs) or run behaviour patterns except for the end-peaks. Our results cast new light upon visual attention in 3D environments and highlight how 3D virtual reality studies can provide important extensions to two-dimensional studies of visual attention.

The life history of human foraging: Cross-cultural and individual variation

Human adaptation depends on the integration of slow life history, complex production skills, and extensive sociality. Refining and testing models of the evolution of human life history and cultural learning benefit from increasingly accurate measurement of knowledge, skills, and rates of production with age. We pursue this goal by inferring hunters’ increases and declines of skill from approximately 23,000 hunting records generated by more than 1800 individuals at 40 locations. The data reveal an average age of peak productivity between 30 and 35 years of age, although high skill is maintained throughout much of adulthood. In addition, there is substantial variation both among individuals and sites. Within study sites, variation among individuals depends more on heterogeneity in rates of decline than in rates of increase. This analysis sharpens questions about the coevolution of human life history and cultural adaptation.