Paul et al. (2011) Evolution and human Behavior 32 (2011) 21–28 Mood and the speed of decisions about anticipated resources and hazards This was one the first papers I published and resulted from my undergraduate honours project at the University of Bristol. We’re all familiar with the saying that an optimist sees the glass as half full while a pessimist sees it as half empty but, remarkably, there’s a growing body of evidence that suggests this is literally true: that your mood and expectations about the world can actually influence your perceptions. In this study we set out to look at whether anxiety levels affected how undergraduate students responded to ambiguous stimuli. A very large proportion of psychological studies use undergraduate students as subjects, because a very large proportion of psychological studies are done in universities where undergraduate students are plentiful and easily bribed or coerced into taking part. You might wonder if undergraduate students are representative of humanity as a whole and whether this might affect the reliability of the results and you’d be very wise so to do. It’s a big problem, but I was an undergraduate myself and didn’t have the resources to try to tackle huge systematic problems in my field. I’m afraid an awful lot of decisions in science are made on the basis of what’s achievable with the time and resources we have, rather than what’s the best way to do things. We used a standardized questionnaire to assess anxiety levels in our volunteers and then set them a computer task in which they were shown a line with a smiley face at one end and a frowny face at the other. The line then disappeared and was replaced with a cross, and the students had a few seconds to decide which end of the line it was closest to. If they correctly identified te cross as being closest to te smiley, we “rewarded” them by showing a photograph of a cute animal; on the other hand, if they said the cross was closest to the frowny face and were wrong, we “punished” them with a picture of a scary predator like a shark baring its teeth. We did a fun preliminary study in which we used a skin conductance response machine - essentially a lie detector machine (although bear in mind that lie detection is bullshit pretend science) – to demonstrate that people did actually have a physiological response to the scary predators! The crucial thing about this odd task is that selecting the frowny face never results in either reward or punishment – either the cross really was closest to the frowny and you avoid your punishment, or it was closer to the smiley and you miss out on the cute, fluffy reward picture, but don’t get punished for your mistake. Choosing the smiley face is a high-risk, high-reward action: if you are right, you get the reward but if you are wrong you will be punished for it. In most cases, the cross was unambiguously closer to one end or the other, but every now and again we sneakily put a cross dead center. What we found was that volunteers who had higher anxiety scores were more likely to play it safe and report that the cross was closer to the frowny face, avoiding any risk of punishment but also ensuring they would go unrewarded. Less anxious students were more likely to expect to be rewarded and report that the cross was closer to the smiley face. (Incidentally, don’t worry too much about these anxious students; in fact, anxiety scores in our sample were lower than generally found in the population as a whole. They were relatively more anxious than their peers, but still not especially anxious). One of the leading theories about why emotions evolved is that they aid decision making, mediating physiological and behavioural responses to the complex web of threats and opportunities that animals face every day. In natural situations, it is rare to have full information about the risks involved in any given situation. An animal that is too bold and ignores dangers is likely to be quickly eaten, but one that is too cautious may avoid hazards but will also pass up opportunities to feed, mate and so on. Every individual much find a balance that allows them to take advantage of opportunities without putting them in too much danger. Experiments like ours suggest that a state of anxiety might play a role in temporarily altering that balance to favour caution – for example, in the wake of a close escape from a predator it is rational to err on the side of caution – and that it might do so by affecting an animal’s interpretation of ambiguous sensory experiences, making the animal more likely to anticipate danger and less likely to anticipate opportunity. Interestingly, this experiment was inspired by a study into rat behaviour and my colleague Cwyn Solvi more recently carried out a conceptually similar study on bumblebees , showing that they too might perceive ambiguous stimuli differently depending on recent experience. This is a slightly unusual, but welcome change to the more common pattern in which studies on humans are adapted to ask the often-unedifying question of whether animals can do what humans do. In this case, evidence seems to be accumulating to suggest that emotional states are common across the animal kingdom and probably serve similar roles in assisting decision making under conditions of uncertainty and risk.

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About Me Originally trained as a biologist, I now work at the intersection of neuroscience, animal behaviour and engineering. I am also an artist and photographer. Over a 16 year career as a researcher I have studied the behaviour of birds, ants and bees, using binoculars, microphones, cameras, computers and radar; all linked by a desire to understand how brains work. ​ I recently decided to leave academic research to seek new challenges and put the skills I have developed to use in new areas. My key skills are problem solving; experimental design and data collection; analysing and visualising data; writing for general and scientific audiences; editing and design; and spoken communication. Contact Me

The life story of a bumblebee We used harmonic radar technology to track every movement made by a worker bumblebee throughout her entire life. This video illustrates her whole life as a forager, from her first explorations of the world to her later dedication to collecting food for her nestmates.

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Woodgate & Chittka (2018) Encyclopedia of Animal Cognition and Behavior. Vonk, J., Shackelford, T. (eds) Central Place Foraging Definition: ​ A foraging strategy in which prey or resources are transported to a nest or other habitual base rather than being consumed in situ. ​ Introduction ​ Many animals use one or more habitual locations as nests, shelters or storage caches during all or part of their lives, and transport resources to this central place. Examples include birds bringing food to their nests to feed their chicks, bees storing honey at their nest to feed brood and provide food during periods when no flowers are available, male terns bringing food to females during courtship, chipmunks stockpiling seed for the winter or Eastern woodrats collecting nesting materials. A central place can also function as a store of information, as in ant colonies where pheromone trails radiating from the nest encode the sum of the colony’s knowledge about available food sources, or honeybees which dance inside the nest to communicate the position of flowers. Humans, too, are central place foragers, transporting everything from food to fuel, building materials and even status symbols or the spoils of war to their homes, often over great distances. ​ Modelling optimal strategies ​ Central place foraging theory is an offshoot of optimal foraging theory, making quantitative predictions about foraging behavior by assuming that animals attempt to optimize their net gain per unit of time or energy invested. The requirement to return to a central location imposes time and energy costs in addition to those incurred by searching for, capturing and handling prey items. These additional costs can alter the optimal strategy, which also depend on whether animals are single-prey loaders, which catch and transport a single item at a time, or multiple-prey loaders, which can acquire many items on a single trip (Orians & Pearson, 1979). ​ To optimize foraging performance, multiple-prey loaders ought to spend longer searching for and collecting food, and carry larger loads, when foraging at distant locations than at those closer to the nest. Single-prey loaders, unable to increase their load by collecting more items, are predicted to prefer larger prey items further from the nest, and to accept a smaller range of prey sizes. The logic underlying these predictions is illuminated by an analogy to human shopping: it can be worthwhile to visit a local shop to pick up a few items only. Conversely, if a shop is time-consuming to reach, it is only worthwhile if the produce is particularly good and if you stock up on everything you are likely to need for some time to come. ​ Choice of prey gives animals an opportunity to adjust the profitability of a trip. Foragers should be choosier and more likely to specialize on the most profitable food sources when foraging further from their central place, although the optimal prey choice strategy varies in complex ways depending on the relative profitability and abundance of different items and on the time taken to handle different types of prey (Houston, 1985). Smaller or less profitable items are more likely to be harvested when the best items are rarely encountered or require time-consuming handling, as well as when the round trip time is lower. In some species, foragers will consume small prey in the field but transport larger items to the nest. ​ Foragers should become less selective as costs associated with foraging increase. Within a foraging trip, animals should become progressively less choosy as time spent foraging increases. Thus, single-prey loaders may return from a long foraging trip with items less profitable than they rejected at the start of the trip (Houston & McNamara, 1985). ​ Many animals process their food before eating it. Processing prey at the point of capture will result in lighter loads to carry back to the nest, resulting in time and energy savings. Beyond a certain distance, these savings result in a lower round-trip time, and a forager should switch from processing at the central place to the point of capture. If processing can be done in stages, progressively greater levels of processing are expected at increasing distances from the central place (Rands, Houston, & Gasson, 2000). ​ A further prediction is that animals should choose nesting sites at the centers of profitable foraging areas, particularly when food supplies are unpredictable. Nest-searching bumblebee queens, for example, will spend weeks searching for suitable sites, and in the process may first sample foraging resources before deciding where to settle. ​ Empirical tests ​ Empirical tests show broad qualitative support for the major predictions of the theory. For example animals from birds to honeybees have been found to take larger loads when foraging further from their central place. Quantitatively, however, animals’ behavior often departs significantly from models’ predictions. Chipmunks collecting sunflowers seeds spend longer filling their cheek pouches and carry heavier loads when foraging at patches further from their nests, as predicted, but neither the actual sizes of the loads nor the function describing the relationship between travel distance and load size are accurately explained by theory (Giraldeau & Kramer, 1982). Although merlins switch from processing their prey at the nest to the point of capture as the distance to the nest increases, they do so at around 1/50 of the distance predicted by a model (Rands, Houston, & Gasson, 2000), suggesting that either the model used unrealistic inputs or that other factors influence the birds’ decision, such as ectoparasite removal or strategies to avoid kleptoparasitism. ​ Criticisms of central place foraging models ​ Models of central place foraging are vulnerable to the same criticisms that have been directed at optimal foraging models in general (Pyke, 1984), namely that they do not account for constraints on the ability of natural selection to optimize any particular function; that they require unrealistic simplifications of natural situations; that they ignore environmental stochasticity; that their proponents cannot determine what “currency” foraging animals ought to optimize; and that, because very different predictions can be arrived at by varying the model parameters, they can be used to explain any empirical result and so, by explaining too much, they fail to explain anything at all. ​ The real world is more complex and variable than that of simplified mathematical models and it is unlikely that natural selection could equip any organism with the optimal response to every possible scenario. Instead, evolution is likely to favor general behavioral rules that perform well on average, in the natural environment typically faced by a given species (McNamara & Houston, 2009). Two conclusions follow: perfectly optimized behavior may not occur under any specific set of conditions; and the optimal behavioral rules for a given species will depend on its biology and ecology. Understanding the needs of animals within their environment and the mechanisms by which they fulfil these needs is the key to a fuller understanding of how foraging strategies evolve. ​ What to optimize ​ The optimal behavior in a given situation depends on what you hope to optimize. Most models use the currency of net rate of energetic gain and zebra finches, for example, forage in ways consistent with such currency; but honeybees aim to maximize energy efficiency, the ratio of energy gained to energy spent. The choice of currency may depend on the biology of the species in question. For example, social species like ants share information on the location and quality of food sources. It can be beneficial for foragers discovering a high quality source to cut short their foraging trip to disseminate information, raising the long term rate of energetic return of the colony at the expense of their individual short term profitability (Dornhaus et al., 2006). Constraints, such as the need for small birds to acquire enough energy to survive the night, may mean that under certain conditions optimizing foraging efficiency is less important than simply getting enough to eat. ​ Spatial cognition and foraging ​ Many models assume an animal has perfect knowledge of the distribution of resources and how to reach them. In fact, organisms start their foraging career with no specific knowledge of their environment. This is significant because the mechanisms by which they acquire and use spatial information, along with constraints and limitations on those mechanisms, determine where and how they forage. The need to balance learning about the environment with the exploitation of known resources is an important factor in explaining why predictions of optimal foraging behavior seldom provide a perfect fit to empirical data. ​ Central place foragers must learn to navigate their environment and return successfully to their starting point. They must explore in search of food and develop efficient routes to get to and from foraging patches. A variety of navigational strategies are employed by central place foragers (Collett, Chittka, & Collett, 2013). Path integration involves keeping a constantly updated memory of one’s position relative to a central location, and can operate independently of the features of the environment. Other strategies involve learning and recognizing environmental features and include matching a visual scene to a memorized snapshot, or following an olfactory or chemical gradient. In rats, grid cells and place cells provide a neural architecture for the animal to keep track of its position in space (Moser, Kropff, & Moser, 2008). ​ Species from insects to primates visit multiple destinations on a single foraging trip. Multi-destination routes reveal several ways in which foraging behavior is richer than previous models have accounted for. One is that foraging decisions involve more than just determining when to leave a patch: visiting many patches in turn is efficient if no single patch is rich enough to gather a full load in a reasonable time-frame, and can also reduce the costs of competition. Another is that the length and geometry of an entire route are likely to be more important than simple distance from the nest in determining the optimal strategy. Bumblebees visit locations in repeatable sequences often converge on the most efficient route (Lihoreau et al., 2012), although experiments in which they do not find an optimal route have revealed that they use heuristic strategies that lead to good results over a range of situations. In addition to multiple destinations, primates like spider monkeys also use multiple sleeping sites, allowing reduced travel distances in large home ranges while retaining the benefits of a central place. ​ Conclusion ​ The features of the environment an animal attends to, and the navigational strategies it employs, are tailored to its habitat and lifestyle. These mechanisms determine what information is available for foraging, which will influence the optimal strategy. The animal’s ability to remember and follow routes will affect which patches are best to exploit while the mechanisms by which it finds and captures prey will influence the optimal choice of food. Such information must be integrated with central place foraging models to improve success in predicting real behavior. ​

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Woodgate et al. (2014) Evolution 68: 230-240 Environmental and genetic control of brain and song structure in the zebra finch Check back later for further details.