We studied the effect of a demonstrator on the learning of a novel foraging task in 12 groups of free-living cooperative breeding Arabian babblers (Argya squamiceps). We allowed naïve babblers to forage jointly on a foraging grid with a demonstrator previously trained to solve a task in one of 2 possible methods: lifting covers of 1 color or pecking through covers of another color. We found that most group members learned to solve the task using one of the methods, and persisted with it even when later tested with covers of a third (neutral) color that could be opened by both lifting and pecking. However, the method learned by group members did not necessarily follow the method used by the pre-trained demonstrator. Instead, learners within each group tended to use the same method (significantly more than expected by chance), and the extent to which groups differed from the demonstrator was correlated with the extent to which the demonstrator occasionally (and quite rarely) exhibited also the alternative method. These results, together with further analysis of the sequence of events in each group, suggest that both naïve birds and demonstrators learn socially from each other, as well as through individual trial-and-error learning, which enables naïve individuals to become demonstrators themselves and influence the pattern of social transmission. This process mostly leads to a homogenous group behavior, but one that cannot be predicted by the seeded demonstration.

Identifying the factors that drive individuals in a population to conform to the same behavior or to exhibit behavioral diversity is crucial for understanding collective decision-making, information transmission, and culture, yet remains challenging. Here, we propose and test a conceptual model predicting a population's position along a conformity-diversity (CD) spectrum based on two key axes shaping the CD landscape: an ecological axis, representing competition over depleted resources, which favors diversity over conformity (since resources are depleted sooner if all individuals conform to the same resource type), and a cognitive axis reflecting the relative ease of learning a behavior socially versus individually. Our experiments show that manipulating these two primary factors is sufficient to produce the full CD spectrum within a single species. Specifically, studying socially foraging house sparrows, we manipulated competition using depleted or nondepleted feeding wells and manipulated the relative ease of learning a task individually by manipulating levels of experience and task difficulty. As predicted by the model, sparrows exhibited strong conformity under the combination of "no food depletion" and "difficult individual learning" conditions, strong diversity under the combination of "food depletion" and "easy individual learning" conditions, and intermediate levels of conformity under mixed conditions. The results show that a population's position along the CD spectrum is highly flexible and sensitive to ecological and task-related conditions. Accordingly, the demonstrated CD surface may be viewed as an evolving reaction norm, and hence as a baseline over which additional factors shaping social conformity and differentiation can be tested.

The evolution of cognition is frequently discussed as the evolution of cognitive abilities or the evolution of some neuronal structures in the brain. However, since such traits or abilities are often highly complex, understanding their evolution requires explaining how they could have gradually evolved through selection acting on heritable variations in simpler cognitive mechanisms. With this in mind, making use of a previously proposed theory, here, we show how the evolution of cognitive abilities can be captured by the fine-tuning of basic learning mechanisms and, in particular, chunking mechanisms. We use the term chunking broadly for all types of non-elemental learning, claiming that the process by which elements are combined into chunks and associated with other chunks, or elements, is critical for what the brain can do, and that it must be fine-tuned to ecological conditions. We discuss the relevance of this approach to studies in animal cognition, using examples from animal foraging and decision-making, problem-solving and cognitive flexibility. Finally, we explain how even the apparent human-animal gap in sequence learning ability can be explained in terms of different fine-tunings of a similar chunking process.This article is part of the Theo Murphy meeting issue 'Selection shapes diverse animal minds'.

We studied how different types of social demonstration improve house sparrows' (Passer domesticus) success in solving a foraging task that requires both operant learning (opening covers) and discrimination learning (preferring covers of the rewarding colour). We provided learners with either paired demonstration (of both cover opening and colour preference), action-only demonstration (of opening white covers only), or no demonstration (a companion bird eating without covers). We found that sparrows failed to learn the two tasks with no demonstration, and learned them best with a paired demonstration. Interestingly, the action of cover opening was learned faster with paired rather than action-only demonstration despite being equally demonstrated in both. We also found that only with paired demonstration, the speed of operant (action) learning was related to the demonstrator’s level of activity. Colour preference (i.e. discrimination learning) was eventually acquired by all sparrows that learned to open covers, even without social demonstration of colour preference. Thus, adding a demonstration of colour preference was actually more important for operant learning, possibly as a result of increasing the similarity between the demonstrated and the learned tasks, thereby increasing the learner’s attention to the actions of the demonstrator. Giving more attention to individuals in similar settings may be an adaptive strategy directing social learners to focus on ecologically relevant behaviours and on tasks that are likely to be learned successfully.

The extent to which animal societies exhibit social conformity as opposed to behavioural diversity is commonly attributed to adaptive learning strategies. Less attention is given to the possibility that the relative difficulty of learning a task socially as opposed to individually can be critical for social learning dynamics. Here we show that by raising initial task difficulty, house sparrows previously shown to exhibit adaptive social diversity become predominantly conformists. The task we used required opening feeding well covers (easier to learn socially) and to choose the covers with the rewarding cues (easy to learn individually). We replicated a previous study where sparrows exhibited adaptive diversity, but did not pre-train the naive sparrows to open covers, making the task initially more difficult. In sharp contrast to the previous study results, most sparrows continued to conform to the demonstrated cue even after experiencing greater success with the alternative rewarding cue for which competition was less intense. Thus, our study shows that a task's cognitive demands, such as the initial dependency on social demonstration, can change the entire learning dynamics, causing social animals to exhibit sub-optimal social conformity rather than adaptive diversity under otherwise identical conditions.

Abstract Gene–culture coevolution in the cognitive domain is expected to occur whenever cultural phenomena select for genes that affect cognition. Such cultural selection would occur, for example, if the culture of making certain tools leads to selection that favours genetic variants that somehow make one better at learning to make these tools. While these coevolutionary processes seem probable and important, they are difficult to study because the genetic underpinnings of cognitive traits are often poorly understood. Indeed, most evidence for cognitive gene–culture coevolution are circumstantial or indirect, and the role of such processes is often a topic of debate. This chapter suggests, however, that a strong case for cognitive gene–culture coevolution can be made based on theoretical considerations, which can also guide future work and put current evidence in context. Using a process-based (mechanistic) approach to cognitive evolution, the authors distinguish between culturally selected genetic changes in innate knowledge, culturally selected genetic changes in attentional and learning mechanisms, and culturally selected genetic changes in the neuroanatomical substrate. In this light, the authors suggest a limited role to hypothesized culturally selected modules requiring complex innate knowledge. On the other hand, culturally selected modifications of attentional and learning parameters may be significant because they can jointly handle the computational challenges involved in the construction of complex cognitive representations. In turn, the development of such representations (in the form of associative networks), sets the demand for culturally selected changes in size and structure of neuroanatomical substrate, for which some evidence is accumulating.

What makes cognition “advanced” is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (“chaining”) to representing various cue combinations as “chunks.” Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fishLabroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral–ephemeral and permanent–permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning.

Lifjeld's comment provides an opportunity to illustrate the intricacies of the "regression to the mean" (RTM) effect, to clarify the difficulty in teasing apart RTM from allocation bias, and to re-examine our results in relation to RTM and in the context of related evidence. Here, we show that (a) the correlations between paternity change and initial paternity are mathematically expected and can equally be produced when changes are caused by the experimental manipulation itself. (b) The approach taken by Lifjeld to control for RTM is overly conservative because it is based on the unrealistic assumption of zero correlation between individuals' repeated measurements. Yet, even when using this conservative method, the main effects we originally reported are still detectable. (c) The combined effect of color darkening and tail elongation in Israel is additionally supported by an increase in the number of extra-pair young in other nests and by three independent studies of this population. (d) The experimental effect of color darkening in North America has been replicated successfully and is consistent with multiple correlative studies. Thus, divergent sexual selection in barn swallow populations is supported by both a conservative reanalysis and multiple, independent analyses of experimental and observational datasets.

Based on past experience, food-related-cues can help foragers to predict the presence and the expected quality of food. However, when the food is already visible there is no need to predict its presence or its other visible attributes, but only those that are still cryptic, such as expected handling time or taste. Optimal foragers should therefore use only knowledge that is relevant to the current setting. Nevertheless, the extent to which they do so is not clear. In a set of experiments, we examined how a change in setting, from hidden to visible reward, affects the reliance of house sparrows ( Passer domesticus ) on three previously learned attributes of food-related cues (sand colors): the setting of the cue (e.g., whether the food was hidden or exposed), the expected amount of the reward (number of seeds), and the expected handling time. We found that sparrows used all three attributes when the rewards were hidden but reached decisions mainly based on handling time when the rewards were visible. This selective use of cue-related information suggests that animals do not simply associate cues with their average expected value but rather learn different attributes of a cue and use all, or only some of them, in a context-appropriate manner.

Uchiyama et al. emphasize that culture evolves directionally and differentially as a function of selective pressures in different populations. Extending these principles to the level of families, lineages, and individuals exposes additional challenges to estimating heritability. Cultural traits expressed differentially as a function of the genetics whose influence they mask or unmask render inseparable the influences of culture and genetics.

Assortative social interactions based on (sub)species recognition can be a driving force in speciation processes. To determine whether breeding Barn Swallows Hirundo rustica transitiva in Israel behave differentially towards members of their own subspecies, relative to a different, transient subspecies H. r. rustica and two sympatrically breeding species (Sand Martin Riparia riparia and House Sparrow Passer domesticus), we conducted a territory intrusion experiment near active nests using taxidermy models. Females responded less to the models than males, and the patterns of the recorded behavioral response traits co-varied statistically with sub- or species identity of the models, but none showed patterns of response selectivity for con(sub)specific model types only. These results do not support a role for subspecies recognition in the territorial intrusion responses of H. r. transitiva.

What makes cognition ‘advanced’ is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (‘chaining’) to representing various cue combinations as ‘chunks’. Here we develop a weighted-graph model to study the conditions for the evolution of chunking ability, based on the ecology of the cleaner fish Labroides dimidiatus . Cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral-reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving the laboratory task, that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral-ephemeral and permanent-permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine tuning of this ability which may be the major target of selection during the evolution of advanced associative learning.

Cognitive flexibility may be necessary for animals facing changing conditions and has long been tested by the reversal learning paradigm. However, while this paradigm is typically based on training animals to discriminate between a rewarding and nonrewarding stimulus and then reversing their roles, under natural conditions animals usually face more than one set of two stimuli. Here, we addressed these potential intricacies by studying the reversal learning of house sparrows, Passer domesticus, in a two-set task. Sparrows previously trained as either colour or shape specialists exhibited different reversal dynamics: While colour discrimination was acquired faster than shape discrimination, shape specialists, including those reaching perfect preference of the rewarding shape, reversed faster than colour specialists. The reversal success of shape specialists was also less variable than that of colour specialists. Additionally, despite being slower, during the reversal, colour specialists sampled shapes more than shape specialists sampled colours. These results suggest that (1) when comparing different reversal tests, significant differences in reversal behaviour may be explained by the type of learned stimulus (e.g. shape versus colour) rather than by differences in cognitive flexibility and (2) under realistic conditions of multiple foraging options, preferences that are difficult to reverse (and typically viewed as indicating low cognitive flexibility) may nevertheless facilitate an ecologically flexible shift to novel food types. Finally, our results also demonstrate that a two-set (multiple-cue) experimental design may help to tease apart some of the different processes underlying reversal behaviour and cognitive flexibility.

We offer a general theoretical framework for brain and behavior that is evolutionarily and computationally plausible. The brain in our abstract model is a network of nodes and edges. Although it has some similarities to standard neural network models, as we show, there are some significant differences. Both nodes and edges in our network have weights and activation levels. They act as probabilistic transducers that use a set of relatively simple rules to determine how activation levels and weights are affected by input, generate output, and affect each other. We show that these simple rules enable a learning process that allows the network to represent increasingly complex knowledge, and simultaneously to act as a computing device that facilitates planning, decision-making, and the execution of behavior. By specifying the innate (genetic) components of the network, we show how evolution could endow the network with initial adaptive rules and goals that are then enriched through learning. We demonstrate how the developing structure of the network (which determines what the brain can do and how well) is critically affected by the co-evolved coordination between the mechanisms affecting the distribution of data input and those determining the learning parameters (used in the programs run by nodes and edges). Finally, we consider how the model accounts for various findings in the field of learning and decision making, how it can address some challenging problems in mind and behavior, such as those related to setting goals and self-control, and how it can help understand some cognitive disorders.

Recent studies have emphasized the role of social learning and cultural transmission in promoting conformity and uniformity in animal groups, but little attention has been given to the role of negative frequency-dependent learning in impeding conformity and promoting diversity instead. Here, we show experimentally that under competitive conditions that are common in nature, social foragers (although capable of social learning) are likely to develop diversity in foraging specialization rather than uniformity. Naive house sparrows that were introduced into groups of foraging specialists did not conform to the behaviour of the specialists, but rather learned to use the alternative food-related cues, thus forming groups of complementary specialists. We further show that individuals in such groups may forage more effectively in diverse environments. Our results suggest that when the benefit from socially acquired skills diminishes through competition in a negative frequency-dependent manner, animal societies will become behaviourally diverse rather than uniform.

Learning is an adaptation that allows individuals to respond to environmental stimuli in ways that improve their reproductive outcomes. The degree of sophistication in learning mechanisms potentially explains variation in behavioral responses. Here, we present a model of learning that is inspired by documented intra- and interspecific variation in the performance of a simultaneous two-choice task, the biological market task. The task presents a problem that cleaner fish often face in nature: choosing between two client types, one that is willing to wait for inspection and one that may leave if ignored. The cleaner's choice hence influences the future availability of clients (i.e., it influences food availability). We show that learning the preference that maximizes food intake requires subjects to represent in their memory different combinations of pairs of client types rather than just individual client types. In addition, subjects need to account for future consequences of actions, either by estimating expected long-term reward or by experiencing a client leaving as a penalty (negative reward). Finally, learning is influenced by the absolute and relative abundance of client types. Thus, cognitive mechanisms and ecological conditions jointly explain intra- and interspecific variation in the ability to learn the adaptive response.

Costly signaling theory was developed in both economics and biology and has been used to explain a wide range of phenomena. However, the theory's prediction that signal cost can enforce information quality in the design of new communication systems has never been put to an empirical test. Here we show that imposing time costs on reporting extreme scores can improve crowd wisdom in a previously cost-free rating system. We developed an online game where individuals interacted repeatedly with simulated services and rated them for satisfaction. We associated ratings with differential time costs by endowing the graphical user interface that solicited ratings from the users with "physics," including an initial (default) slider position and friction. When ratings were not associated with differential cost (all scores from 0 to 100 could be given by an equally low-cost click on the screen), scores correlated only weakly with objective service quality. However, introducing differential time costs, proportional to the deviation from the mean score, improved correlations between subjective rating scores and objective service performance and lowered the sample size required for obtaining reliable, averaged crowd estimates. Boosting time costs for reporting extreme scores further facilitated the detection of top performances. Thus, human collective online behavior, which is typically cost-free, can be made more informative by applying costly signaling via the virtual physics of rating devices.

We propose that food-related uncertainty is but one of multiple cues that predicts harsh conditions and may activate "incentive hope." An evolutionarily adaptive response to these would have been to shift to a behavioral-metabolic phenotype geared toward facing hardship. In modernity, this phenotype may lead to pathologies such as obesity and hoarding. Our perspective suggests a novel therapeutic approach.

Social learning is widespread but the causes for variation in the use of social versus private information are not always clear. Alongside adaptive explanations, suggesting that animals learn socially only when it is indeed adaptive to do so, it is also possible that the use of social learning is limited by mechanistic constraints. A common, but frequently overlooked challenge for social learning mechanisms is the need to allow learners to solve a problem through watching it being solved by others. This requires animals to be able to shift between contexts: from the context of the observed solution, to the context of the unsolved problem. For instance, for the social learning of cues associated with hidden food, an individual that merely sees a conspecific exploiting the food must, in the later absence of demonstrators or visible rewards, also learn to explore the cue for itself. Here, we show that this shift in context can indeed be difficult. In 2 experiments involving sand colors, house sparrows trained with hidden seeds learned to search for hidden seeds (based on food-color association) better than sparrows trained with exposed seeds. However, the latter showed color preference when tested with seeds exposed on both sand colors. These results demonstrate that context-specific learning makes it difficult to generalize reward-cue association from "exposed" to "hidden" conditions, which may explain why social learning is often more effective when it is based on socially facilitated active search (for hidden food), similar to that used in the context of independent foraging.

Understanding how humans and other animals learn to perform an act from seeing it done has been a major challenge in the study of social learning. To determine whether this ability is based on 'true imitation', many studies have applied the two-action experimental paradigm, examining whether subjects learn to perform the specific action demonstrated to them. Here, we show that the insights gained from animals' success in two-action experiments may be limited, and that a better understanding is achieved by monitoring subjects' entire behavioural repertoire. Hand-reared house sparrows that followed a model of a mother demonstrator were successful in learning to find seeds hidden under a leaf, using the action demonstrated by the mother (either pushing the leaf or pecking it). However, they also produced behaviours that had not been demonstrated but were nevertheless related to the demonstrated act. This finding suggests that while the learners were clearly influenced by the demonstrator, they did not accurately imitate her. Rather, they used their own behavioural repertoire, gradually fitting it to the demonstrated task solution through trial and error. This process is consistent with recent views on how animals learn to imitate, and may contribute to a unified process-level analysis of social learning mechanisms.