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Over the past two decades, Ginsburg and Jablonka have developed a novel approach to studying the evolutionary origins of consciousness: the Unlimited Associative Learning (UAL) framework. The central idea is that there is a distinctive type of learning that can serve as a transition marker for the evolutionary transition from non-conscious to conscious life. The goal of this paper is to stimulate discussion of the framework by providing a primer on its key claims (Part I) and a clear statement of its main empirical predictions (Part II).

Animals must effectively balance the time they spend exploring the environment for new resources and exploiting them. One way that social animals accomplish this balance is by allocating these two tasks to different individuals. In honeybees, foraging is divided between scouts, which tend to explore the landscape for novel resources, and recruits, which tend to exploit these resources. Exploring the variation in cognitive and physiological mechanisms of foraging behaviour will provide a deeper understanding of how the division of labour is regulated in social insect societies. Here, we uncover how honeybee foraging behaviour may be shaped by predispositions in performance of latent inhibition (LI), which is a form of non‐associative learning by which individuals learn to ignore familiar information. We compared LI between scouts and recruits, hypothesizing that differences in learning would correlate with differences in foraging behaviour. Scouts seek out and encounter many new odours while locating novel resources, while recruits continuously forage from the same resource, even as its quality degrades. We found that scouts show stronger LI than recruits, possibly reflecting their need to discriminate forage quality. We also found that scouts have significantly elevated tyramine compared to recruits. Furthermore, after associative odour training, recruits have significantly diminished octopamine in their brains compared to scouts. These results suggest that individual variation in learning behaviour shapes the phenotypic behavioural differences between different types of honeybee foragers. These differences in turn have important consequences for how honeybee colonies interact with their environment. Uncovering the proximate mechanisms that influence individual variation in foraging behaviour is crucial for understanding the ecological context in which societies evolve. Honeybees have mastered the exploration–exploitation trade‐off by dividing foraging labour. Here, the authors report that underlying this division of labour is variation in learning ability: scouts ignore familiar odours while recruits readily learn novel and familiar odours. Further, scouts and recruits have different quantities of biogenic amines in their brains, influencing their behaviour.

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.

Anorexia nervosa (AN) is associated with altered sensitivity to reward and punishment. Few studies have investigated whether this results in aberrant learning. The ability to learn from rewarding and aversive experiences is essential for flexibly adapting to changing environments, yet individuals with AN tend to demonstrate cognitive inflexibility, difficulty set-shifting and altered decision-making. Deficient reinforcement learning may contribute to repeated engagement in maladaptive behavior. This study investigated learning in AN using a probabilistic associative learning task that separated learning of stimuli via reward from learning via punishment. Forty-two individuals with Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 restricting-type AN were compared to 38 healthy controls (HCs). We applied computational models of reinforcement learning to assess group differences in learning, thought to be driven by violations in expectations, or prediction errors (PEs). Linear regression analyses examined whether learning parameters predicted BMI at discharge. AN had lower learning rates than HC following both positive and negative PE (p < .02), and were less likely to exploit what they had learned. Negative PE on punishment trials predicted lower discharge BMI (p < .001), suggesting individuals with more negative expectancies about avoiding punishment had the poorest outcome. This is the first study to show lower rates of learning in AN following both positive and negative outcomes, with worse punishment learning predicting less weight gain. An inability to modify expectations about avoiding punishment might explain persistence of restricted eating despite negative consequences, and suggests that treatments that modify negative expectancy might be effective in reducing food avoidance in AN.

Altered reinforcement learning (RL) and decision-making have been implicated in the pathophysiology of anorexia nervosa. To determine whether deficits observed in symptomatic anorexia nervosa are also present in remission, we investigated RL in women remitted from anorexia nervosa (rAN). Participants performed a probabilistic associative learning task that involved learning from rewarding or punishing outcomes across consecutive sets of stimuli to examine generalization of learning to new stimuli over extended task exposure. We fit a hybrid RL and drift diffusion model of associative learning to model learning and decision-making processes in 24 rAN and 20 female community controls (cCN). rAN showed better learning from negative outcomes than cCN and this was greater over extended task exposure ( < .001, = .30). rAN demonstrated a reduction in accuracy of optimal choices ( = .007, = .16) and rate of information extraction on reward trials from set 1 to set 2 ( = .012, = .14), and a larger reduction of response threshold separation from set 1 to set 2 than cCN ( = .036, = .10). rAN extracted less information from rewarding stimuli and their learning became increasingly sensitive to negative outcomes over learning trials. This suggests rAN shifted attention to learning from negative feedback while slowing down extraction of information from rewarding stimuli. Better learning from negative over positive feedback in rAN might reflect a marker of recovery.

Nicotine produces robust stimulus effects that can be conditioned to form associations with reinforcing nondrug stimuli. We examine how established associations to the nicotine stimulus may be weakened via the overexpectation effect. In two experiments, we separately conditioned sucrose associations to the interoceptive nicotine stimulus (0.4 mg/kg, SC) and to a “noisy” exteroceptive contextual stimulus (oscillating houselight and white noise) via the discriminated goal-tracking task. Thereafter, we presented additional sucrose pairings with the nicotine and noisy stimuli, now in compound. Testing of the conditioned goal-tracking evoked by the nicotine and noisy stimuli in isolation—before versus after compound conditioning (Experiment 1 ) or between treatment and control groups (Experiment 2 )—demonstrated an attenuation of conditioned responding via the overexpectation effect. We suggest that applications of the overexpectation effect may provide some promise for treatments seeking to attenuate drug-evoked conditioned responses in situations where extinction-based interventions are not suitable.

Humans continuously learn new information. Here, we examined the temporal brain dynamics of explicit verbal associative learning between unfamiliar items. In the first experiment, 25 adults learned object-pseudoword associations during a 5-day training program allowing us to track the N400 dynamics across learning blocks within and across days. Successful learning was accompanied by an initial frontal N400 that decreased in amplitude across blocks during the first day and shifted to parietal sites during the last training day. In Experiment 2, we replicated our findings with 38 new participants randomly assigned to a consistent learning or an inconsistent learning group. The N400 amplitude modulations that we found, both within and between learning sessions, are taken to reflect the emergence of novel lexical traces even when learning concerns items for which no semantic information is provided. The shift in N400 topography suggests that different N400 neural generators may contribute to specific word learning steps through a balance between domain-general and language-specific mechanisms.

Defensive behavior changes based on threat intensity, proximity, and context of exposure, and learning about danger-predicting stimuli is critical for survival. However, most Pavlovian fear conditioning paradigms focus only on freezing behavior, obscuring the contributions of associative and non-associative mechanisms to dynamic defensive responses. To thoroughly investigate defensive ethograms, we subjected male and female adult C57BL/6 J mice to a Pavlovian conditioning paradigm that paired footshock with a serial compound stimulus (SCS) consisting of distinct tone and white noise (WN) stimulus periods. To investigate how associative and non-associative mechanisms affect defensive responses, we compared this paired SCS-footshock group with four control groups that were conditioned with either pseudorandom unpaired presentations of SCS and footshock, shock only, or reversed SCS presentations with inverted tone-WN order, with paired or unpaired presentations. On day 2 of conditioning, the paired group exhibited robust freezing during the tone period with switching to explosive jumping and darting behaviors during the WN period. Comparatively, the unpaired and both reverse SCS groups expressed less tone-induced freezing and rarely showed jumping or darting during WN. Following the second day of conditioning, we observed how defensive behavior changed over two extinction sessions. During extinction, the tone-induced freezing decreased in the paired group, and mice rapidly shifted from escape jumping during WN to a combination of freezing and darting. The unpaired, unpaired reverse, and shock-only groups displayed defensive tail rattling and darting during the SCS, with minimal freezing and jumping. Interestingly, the paired reverse group did not jump to WN, and tone-evoked freezing was resistant to extinction. These findings demonstrate that non-associative factors promote some defensive responsiveness, but associative factors are required for robust cue-induced freezing and high-intensity flight expression. Post-traumatic stress disorder (or PTSD for short) is a condition that can cause people to overreact to harmless cues, vividly re-experience a traumatic event, or freeze in place. To understand why this happens, researchers often study fear responses using an approach called fear conditioning, where laboratory animals learn to associate the sound of a tone with a mild electric shock. This conditioning causes animals to freeze with fear when they hear the tone. However, focusing on freezing overlooks the range of defensive actions animals may carry out, such as escaping or fighting. Capturing this complexity in experiments is important for understanding the dynamic nature of fear responses that occur in PTSD. Previous work showed that conditioning mice with a two-part cue, such as a tone followed by white noise, caused mice to freeze during the first cue and jump during the second cue. However, whether the mice learned this behaviour through conditioning or if it was an instinctive response to the cues remained unclear. To investigate this phenomenon, Le et al. – including some of the researchers involved in the previous work – conditioned mice with a variety of different cue combinations and monitored how they responded. As before, mice conditioned to associate a tone followed by white noise with an electric shock froze when they heard the tone and transitioned to jumping during the white noise. However, if during conditioning the sounds and shocks occurred at unpredictable times, the mice did not associate the sounds with the shock and therefore they froze less and rarely jumped. Similarly, reversing the order of the sounds so that the white noise happened before the tone also reduced jumping but not freezing. To investigate whether the mice could unlearn this fear response, Le et al. exposed the fear-conditioned mice to the cues without an accompanying electric shock. The mice that had been conditioned with a tone followed by white noise showed a weaker response to the cues, only freezing and not jumping. However, the mice with the reversed cues still froze even after this exposure, and the mice with the non-associated cues maintained very little freezing and jumping. Taken together, the findings suggest that while fear responses can be influenced by the association between certain noises and an electric shock, other factors such as the timing and the order of the sound cues can also impact the intensity of the fear response. The experiments also showed that this method of fear conditioning can be used for both learning and unlearning fear responses, revealing an approach for future studies into how fear responses change over time. Combining this more complex approach with other experimental techniques could help researchers identify the brain regions that drive fear responses, which may eventually benefit people with PTSD and other fear disorders.

The scientific study of consciousness or subjective experiencing is a rapidly expanding research program engaging philosophers of mind, psychologists, cognitive scientists, neurobiologists, evolutionary biologists and biosemioticians. Here we outline an evolutionary approach that we have developed over the last two decades, focusing on the evolutionary transition from non-conscious to minimally conscious, subjectively experiencing organisms. We propose that the evolution of subjective experiencing was driven by the evolution of learning and we identify an open-ended, representational, generative and recursive form of associative learning, which we call Unlimited Associative Learning (UAL), as an evolutionary transition marker of minimal consciousness. This evolutionary marker provides evidence that the evolutionary transition to consciousness has gone to completion and allows reverse-engineering from this learning capacity to the system that enables it – making possible the construction of a toy model of UAL. The model allows us to identify some of the key processes and structures that constitute minimal consciousness, points its taxonomic distribution and the ecological context in which it first emerged, highlights its function and suggests a framework for exploring developmental and evolutionary modifications of consciousness. We point to ways of experimentally testing the relationship between UAL and consciousness in human and in non-human animals and discuss the theoretical and ethical implications of our approach. The framework we offer allows the exploration of the evolutionary changes in agency, value systems, selective processes and goals that were involved in the transition to subjective experiencing from a perspective that resonates with the approaches of bio-semioticians.

Bees’ remarkable visual learning abilities make them ideal for studying active information acquisition and representation. Here, we develop a biologically inspired model to examine how flight behaviours during visual scanning shape neural representation in the insect brain, exploring the interplay between scanning behaviour, neural connectivity, and visual encoding efficiency. Incorporating non-associative learning—adaptive changes without reinforcement—and exposing the model to sequential natural images during scanning, we obtain results that closely match neurobiological observations. Active scanning and non-associative learning dynamically shape neural activity, optimising information flow and representation. Lobula neurons, crucial for visual integration, self-organise into orientation-selective cells with sparse, decorrelated responses to orthogonal bar movements. They encode a range of orientations, biased by input speed and contrast, suggesting co-evolution with scanning behaviour to enhance visual representation and support efficient coding. To assess the significance of this spatiotemporal coding, we extend the model with circuitry analogous to the mushroom body, a region linked to associative learning. The model demonstrates robust performance in pattern recognition, implying a similar encoding mechanism in insects. Integrating behavioural, neurobiological, and computational insights, this study highlights how spatiotemporal coding in the lobula efficiently compresses visual features, offering broader insights into active vision strategies and bio-inspired automation.