Ina recent study, Wasserman, Kain, and O'Donoghue (Current Biology, 33(6),1112–1116, 2023) set out to resolve the associative learning paradox by showing that pigeons can solve a complex categorylearning task through associative learning. The present Outlook paperpresents their findings, expands on this paradox, and discusses implications oftheir results.

Identifying cognitive capacities underlying the human evolutionary transition is challenging, and many hypotheses exist for what makes humans capable of, for example, producing and understanding language, preparing meals, and having culture on a grand scale. Instead of describing processes whereby information is processed, recent studies have suggested that there are key differences between humans and other animals in how information is recognized and remembered. Such constraints may act as a bottleneck for subsequent information processing and behavior, proving important for understanding differences between humans and other animals. We briefly discuss different sequential aspects of cognition and behavior and the importance of distinguishing between simultaneous and sequential input, and conclude that explicit tests on non-human great apes have been lacking. Here, we test the memory for stimulus sequences-hypothesis by carrying out three tests on bonobos and one test on humans. Our results show that bonobos’ general working memory decays rapidly and that they fail to learn the difference between the order of two stimuli even after more than 2,000 trials, corroborating earlier findings in other animals. However, as expected, humans solve the same sequence discrimination almost immediately. The explicit test on whether bonobos represent stimulus sequences as an unstructured collection of memory traces was not informative as no differences were found between responses to the different probe tests. However, overall, this first empirical study of sequence discrimination on non-human great apes supports the idea that non-human animals, including the closest relatives to humans, lack a memory for stimulus sequences. This may be an ability that sets humans apart from other animals and could be one reason behind the origin of human culture.

Abnormal behaviours are common in captive animals, and despite a lot of research, the development, maintenance and alleviation of these behaviours are not fully understood. Here, we suggest that conditioned reinforcement can induce sequential dependencies in behaviour that are difficult to infer from direct observation. We develop this hypothesis using recent models of associative learning that include conditioned reinforcement and inborn facets of behaviour, such as predisposed responses and motivational systems. We explore three scenarios in which abnormal behaviour emerges from a combination of associative learning and a mismatch between the captive environment and inborn predispositions. The first model considers how abnormal behaviours, such as locomotor stereotypies, may arise from certain spatial locations acquiring conditioned reinforcement value. The second model shows that conditioned reinforcement can give rise to abnormal behaviour in response to stimuli that regularly precede food or other reinforcers. The third model shows that abnormal behaviour can result from motivational systems being adapted to natural environments that have different temporal structures than the captive environment. We conclude that models including conditioned reinforcement offer an important theoretical insight regarding the complex relationships between captive environments, inborn predispositions, and learning. In the future, this general framework could allow us to further understand and possibly alleviate abnormal behaviours.

The general process- and adaptive specialization hypotheses represent two contrasting explanations for understanding intelligence in non-human animals. The general process hypothesis proposes that associative learning underlies all learning, whereas the adaptive specialization hypothesis suggests additional distinct learning processes required for intelligent behavior. Here, we use a selection of experimental paradigms commonly used in comparative cognition to explore these hypotheses. We tested if a novel computational model of associative learning — A-learning — could solve the problems presented in these tests. Results show that this formulation of associative learning suffices as a mechanism for general animal intelligence, without the need for adaptive specialization, as long as adequate motor- and perceptual systems are there to support learning. In one of the tests, however, the addition of a short-term trace memory was required for A-learning to solve that particular task. We further provide a case study showcasing the flexibility, and lack thereof, of associative learning, when looking into potential learning of self-control and the development of behavior sequences. From these simulations we conclude that the challenges do not so much involve the complexity of a learning mechanism, but instead lie in the development of motor- and perceptual systems, and internal factors that motivate agents to explore environments with some precision, characteristics of animals that have been fine-tuned by evolution for million of years.Author summary What causes animal intelligence? One hypothesis is that, among vertebrates, intelligence relies upon the same general processes for both memory and learning. A contrasting hypothesis states that important aspects of animal intelligence come from species- and problem specific cognitive adaptations. Here, we use a recently formulated model of associative learning and subject it, through computer simulations, to a battery of tests designed to probe cognitive abilities in animals. Our computer simulations show that this associative learning model can account well for how animals learn these various tasks. We conclude that a major challenge in understanding animal and machine intelligence lies in describing behavior systems. Specifically, how motor flexibility and perceptual systems together with internal factors allow animals and machines to navigate the world. As a consequence of our results, together with current progress in both animal- and machine learning, we cannot reject the idea that associative learning provides a general process for animal intelligence.Competing Interest StatementThe authors have declared no competing interest.

A widespread and popular belief posits that humans possess a cognitive capacity that is limited to keeping track of and maintaining stable relationships with approximately 150 people. This influential number, ‘Dunbar's number’, originates from an extrapolation of a regression line describing the relationship between relative neocortex size and group size in primates. Here, we test if there is statistical support for this idea. Our analyses on complementary datasets using different methods yield wildly different numbers. Bayesian and generalized least-squares phylogenetic methods generate approximations of average group sizes between 69–109 and 16–42, respectively. However, enormous 95% confidence intervals (4–520 and 2–336, respectively) imply that specifying any one number is futile. A cognitive limit on human group size cannot be derived in this manner.

We present a new mathematical formulation of associative learning focused on non-human animals, which we call A-learning. Building on current animal learning theory and machine learning, A-learning is composed of two learning equations, one for stimulus-response values and one for stimulus values (conditioned reinforcement). A third equation implements decision-making by mapping stimulus-response values to response probabilities. We show that A-learning can reproduce the main features of: instrumental acquisition, including the effects of signaled and unsignaled non-contingent reinforcement; Pavlovian acquisition, including higher-order conditioning, omission training, autoshaping, and differences in form between conditioned and unconditioned responses; acquisition of avoidance responses; acquisition and extinction of instrumental chains and Pavlovian higher-order conditioning; Pavlovian-to-instrumental transfer; Pavlovian and instrumental outcome revaluation effects, including insight into why these effects vary greatly with training procedures and with the proximity of a response to the reinforcer. We discuss the differences between current theory and A-learning, such as its lack of stimulus-stimulus and response-stimulus associations, and compare A-learning with other temporal-difference models from machine learning, such as Q-learning, SARSA, and the actor-critic model. We conclude that A-learning may offer a more convenient view of associative learning than current mathematical models, and point out areas that need further development.

Social transmission of information is a key phenomenon in the evolution of behaviour and in the establishment of traditions and culture. The diversity of social learning phenomena has engendered a diverse terminology and numerous ideas about underlying learning mechanisms, at the same time that some researchers have called for a unitary analysis of social learning in terms of associative processes. Leveraging previous attempts and a recent computational formulation of associative learning, we analyse the following learning scenarios in some generality: learning responses to social stimuli, including learning to imitate; learning responses to non-social stimuli; learning sequences of actions; learning to avoid danger. We conceptualize social learning as situations in which stimuli that arise from other individuals have an important role in learning. This role is supported by genetic predispositions that either cause responses to social stimuli or enable social stimuli to reinforce specific responses. Simulations were performed using a new learning simulator program. The simulator is publicly available and can be used for further theoretical investigations and to guide empirical research of learning and behaviour. Our explorations show that, when guided by genetic predispositions, associative processes can give rise to a wide variety of social learning phenomena, such as stimulus and local enhancement, contextual imitation and simple production imitation, observational conditioning, and social and response facilitation. In addition, we clarify how associative mechanisms can result in transfer of information and behaviour from experienced to naive individuals.

Primate brains differ in size and architecture. Hypotheses to explain this variation are numerous and many tests have been carried out. However, after body size has been accounted for there is little left to explain. The proposed explanatory variables for the residual variation are many and covary, both with each other and with body size. Further, the data sets used in analyses have been small, especially in light of the many proposed predictors. Here we report the complete list of models that results from exhaustively combining six commonly used predictors of brain and neocortex size. This provides an overview of how the output from standard statistical analyses changes when the inclusion of different predictors is altered. By using both the most commonly tested brain data set and the inclusion of new data we show that the choice of included variables fundamentally changes the conclusions as to what drives primate brain evolution. Our analyses thus reveal why studies have had troubles replicating earlier results and instead have come to such different conclusions. Although our results are somewhat disheartening, they highlight the importance of scientific rigor when trying to answer difficult questions. It is our position that there is currently no empirical justification to highlight any particular hypotheses, of those adaptive hypotheses we have examined here, as the main determinant of primate brain evolution.

There is a new associative learning paradox. The power of associative learning for producing flexible behaviour in non-human animals is downplayed or ignored by researchers in animal cognition, whereas artificial intelligence research shows that associative learning models can beat humans in chess. One phenomenon in which associative learning often is ruled out as an explanation for animal behaviour is flexible planning. However, planning studies have been criticized and questions have been raised regarding both methodological validity and interpretations of results. Due to the power of associative learning and the uncertainty of what causes planning behaviour in non-human animals, I explored what associative learning can do for planning. A previously published sequence learning model which combines Pavlovian and instrumental conditioning was used to simulate two planning studies, namely Mulcahy & Call 2006 ‘Apes save tools for future use.’ Science 312, 1038–1040 and Kabadayi & Osvath 2017 ‘Ravens parallel great apes in flexible planning for tool-use and bartering.’ Science 357, 202–204. Simulations show that behaviour matching current definitions of flexible planning can emerge through associative learning. Through conditioned reinforcement, the learning model gives rise to planning behaviour by learning that a behaviour towards a current stimulus will produce high value food at a later stage; it can make decisions about future states not within current sensory scope. The simulations tracked key patterns both between and within studies. It is concluded that one cannot rule out that these studies of flexible planning in apes and corvids can be completely accounted for by associative learning. Future empirical studies of flexible planning in non-human animals can benefit from theoretical developments within artificial intelligence and animal learning.

Experiments inspired by Aesop's fable The crow and the pitcher have been suggested to show that some birds (rooks, Corvus frugilegus, New Caledonian crows, Corvus moneduloides, and Eurasian jays, Garrulus glandarius) understand cause–effect relationships pertaining to water displacement. For example, the birds may prefer to drop stones in water rather than in sand in order to retrieve a floating food morsel, suggesting that they understand that only the level of water can be so raised. Here we re-evaluate the evidence for causal understanding in all published experiments (23 928 choices by 36 individuals). We first show that commonly employed statistical methods cannot disentangle the birds' initial performance on a task (which is taken as an indicator of causal understanding) from trial-and-error learning that may occur during the course of the experiment. We overcome this shortcoming with a new statistical analysis that quantifies initial performance and learning effects separately. We present robust evidence of trial-and-error learning in many tasks, and of an initial preference in a few. We also show that both seeming demonstrations of causal understanding and of lack of it can be understood based on established properties of instrumental learning. We conclude that Aesop's fable experiments have not yet produced evidence of causal understanding, and we suggest how the experimental designs can be modified to yield better tests of causal cognition.