The phenomenon at the heart of the study is called latent learning, a concept with a storied history in psychology. Unlike classical conditioning or trial-and-error learning, latent learning requires no immediate payoff. Information about the environment is acquired and stored quietly during mere exposure, remaining invisible until a relevant incentive appears and suddenly reveals that the animal had been paying attention all along. The idea was famously demonstrated in the early twentieth century by Edward Tolman and colleagues, who showed that rats allowed to wander a maze unrewarded later performed nearly as well as rats trained with food rewards once food was finally introduced.

Latent learning has since been documented in humans, fish, and other vertebrates, and modern neuroscientists often connect it to the construction of cognitive maps, internal representations of spatial relationships that allow flexible navigation. Insects, with brains containing far fewer neurons than a rat’s, have historically been viewed as less likely candidates for such sophisticated learning, although decades of research on bee and ant navigation have steadily eroded that assumption. Desert ants, for example, learn visual landmarks during elaborate learning flights and walks, and honeybees integrate multiple navigational cues into surprisingly robust guidance systems. Whether unrewarded exploration genuinely improves later foraging performance in ants, however, remained a question in need of direct experimental testing.

Bastien Wagner of Sorbonne Paris Nord University and the University of Strasbourg, working with Patrizia d’Ettorre and István E. Maák, designed an elegantly simple experiment to address that gap. Their subject, Aphaenogaster senilis, is a ground-dwelling species that forages in open, sunny habitats where food resources appear unpredictably in space and time. That ecological context matters: when a scout cannot rely on predictable resource locations, any mechanism that extracts useful information from routine exploration could confer a substantial survival advantage, turning aimless wandering into a form of low-cost reconnaissance.

The team compared two groups of ants navigating an artificial maze to reach food. One group, the experienced ants, had previously been allowed to explore the very same maze when it was completely empty, with no food anywhere in it and no reward waiting at the end. The other group, the controls, encountered the maze for the first time only when food was present. If the ants were learning nothing during their unrewarded exposure, both groups should have performed identically once food appeared. Instead, the experienced ants located the food significantly faster than their naive nestmates, exactly the pattern predicted if they had absorbed spatial information during their earlier, reward-free visits.

The differences did not end at food discovery. After finding food for the first time, experienced ants also returned to the nest more rapidly than control ants. This second result is particularly revealing, because it shows that the benefits of prior exposure extended beyond simply finding the reward. An ant that knows the maze’s layout does not need to retrace or stumble through it when the goal shifts from food to home. The knowledge acquired during unrewarded exploration, in other words, was later deployed for flexible, goal-directed behavior, which is precisely the functional signature that defines latent learning rather than simpler stimulus-response habits.

The researchers then asked a subtler question about the individual ants themselves. Among the ants placed in the maze, some solved it and some did not, and the team wanted to know whether these successful navigators differed in their general behavioral style. Using an open-field test, a standard assay originally developed to measure exploration and anxiety-like behavior, they characterized the exploratory activity of each ant. The outcome was counterintuitive: the ants that managed to solve the maze were actually less exploratory in the open field than those that failed. Rather than the boldest adventurers being the best navigators, the more cautious individuals appeared to be the ones that cracked the spatial puzzle.

The authors interpret this pattern as a potential example of a speed-accuracy trade-off, a well-known concept in behavioral ecology describing how fast decisions often come at the cost of precision, while careful, deliberate processing yields better accuracy. Highly exploratory ants may rush through environments, sampling widely but shallowly, whereas less exploratory individuals may attend more closely to spatial details as they move. Similar results have emerged in earlier work on social insects, including studies reporting that highly active explorer ants show poorer learning performance, suggesting that a trade-off between exploration and careful information acquisition may be a recurring theme in insect cognition.

The study also adds to a growing appreciation of inter-individual variability in insect societies. Ant colonies have long been treated as superorganisms in which workers are interchangeable cogs, but research over the past two decades has revealed consistent personality-like differences among individuals in exploration, boldness, sucrose responsiveness, and task specialization. These differences can matter at the colony level; diverse colonies have been shown to be more productive in some contexts. In the case of A. senilis, a colony containing a mixture of cautious spatial learners and restless explorers may be ideally configured, with one subset solving navigational problems efficiently while the other scouts broadly for novel opportunities.

The broader implications reach into one of the liveliest debates in animal cognition: whether insects possess cognitive maps, internal spatial representations that permit novel shortcuts and flexible route planning, or whether their navigation relies on collections of simpler guidance modules such as path integration, landmark matching, and scene familiarity. Proponents of the cognitive map hypothesis point to findings like these as evidence that unrewarded experience builds genuine spatial knowledge, while critics urge caution in attributing map-like representations without stronger tests of flexible shortcutting. What the new results establish firmly, independent of that debate, is that ants benefit cognitively from exploration alone, without reinforcement, and that this benefit translates directly into measurable foraging efficiency. For an animal whose fitness depends on shuttling calories back to a colony, the ability to bank spatial information during every uneventful walk may be one of evolution’s quietest but most valuable bargains, hidden in plain sight until the moment a reward appears and the memory shows its worth.

Subject of Research: Latent learning and foraging efficiency in the ant Aphaenogaster senilis

Article Title: Latent learning improves foraging efficiency in ants

Article References: Wagner, B., d’Ettorre, P., & Maák, I. E. (2026). Latent learning improves foraging efficiency in ants. The Science of Nature, 113(5), Article 112. https://doi.org/10.1007/s00114-026-02163-7

Image Credits: AI Generated

DOI: 10.1007/s00114-026-02163-7

Keywords: latent learning, ants, Aphaenogaster senilis, foraging, navigation, maze learning, spatial learning, insect cognition, behavioral ecology, exploratory activity, speed-accuracy trade-off, animal personality

For years, a dominant narrative in autism research has held that autistic people respond more cautiously, demanding more evidence before committing to a decision. That idea grew out of studies applying diffusion decision models, a class of cognitive models that decompose two-choice task performance into distinct components. The models describe noisy sensory evidence accumulating over time from a starting point toward one of two decision boundaries. The key parameters are drift rate, which reflects how efficiently task-relevant information is extracted; boundary separation, which captures response caution or how much evidence is required before responding; the starting point, which encodes any preexisting bias toward one option; and non-decision time, which covers sensory encoding and motor execution outside the decision itself.

Previous findings using this framework had been strikingly inconsistent. Several studies, including work by Pirrone and colleagues with adults and with children and adolescents, reported wider boundary separation in autistic participants during visual orientation tasks, suggesting a general preference for certainty. Similar patterns appeared in implicit association tasks, go/no-go tasks, numerical addition and flanker tasks. Yet other studies found no such difference, and at least one study of autistic adolescents during face processing reported narrower boundaries instead. The research team, led by Lou Thomas and Catherine Manning, set out to resolve this confusion by systematically testing three candidate explanations: that group differences depend on the task, that they depend on the modelling approach, or that they hinge on the instructions given to participants.

The answer came from two large, carefully matched studies involving 50 autistic and 50 non-autistic children aged 6 to 14 years. In the first study, children played a child-friendly game called InsectLand in which they judged the tilt of striped Gabor patches relative to a vertical reference line, distinguishing easy trials tilted 1.3 degrees from vertical from hard trials tilted only 0.6 degrees. Crucially, no explicit instructions about speed or accuracy were given. In the second study, children judged the direction of coherent motion among randomly moving dots, first under instructions to respond as accurately as possible and then under instructions to respond as quickly as possible, in a counterbalanced order.

The modelling was deliberately rigorous. Analyses were conducted by team members blind to group membership, who made all decisions about outlier removal and prior selection on a dataset in which diagnostic status and trait scores had been randomly permuted. The team fitted Bayesian hierarchical diffusion models, which preserve uncertainty at both the participant and group level, and followed up with conventional two-step non-hierarchical fits, extracting individual parameter estimates before running group statistics, mirroring the approach of earlier studies that reported group differences. Across both tasks, both modelling approaches, and all model variants that controlled for age and intelligence, modelled drift rate variability, or accounted for contaminant responses, the results were the same: no conclusive evidence of group differences in drift rate, boundary separation, starting point or non-decision time.

The headline result emerged from the motion task. When told to emphasise accuracy, all children widened their decision boundaries and lengthened their non-decision times; when told to emphasise speed, they narrowed boundaries and shortened non-decision times. The evidence for this within-participant modulation was overwhelming, with Bayes factors exceeding 100, and the effect appeared equally in the autistic group alone. Drift rates, by contrast, were untouched by instructions, exactly as theory predicts. This is the first demonstration that autistic children can strategically modulate the speed-accuracy trade-off in perceptual tasks on the basis of explicit instruction, and it directly challenges theoretical accounts that portray autistic cognition as inflexible, including executive dysfunction frameworks and predictive coding proposals of rigidly high precision weights.

The findings also complicate the popular idea that autistic people are uniformly more cautious decision-makers. Even without any speed or accuracy instructions in the orientation study, autistic children did not show the wider boundary separation previously reported. The researchers suggest that increased caution may emerge only under specific real-world conditions. A consultation with five autistic community members after data collection suggested that caution might depend on the number of choice options, the stakes of the decision, and contextual factors, and a recent narrative review similarly proposes that group differences in decision-making arise mainly in complex metacognitive and value-based tasks rather than simple perceptual ones.

Beyond the group comparisons, the team explored how individual differences relate to decision-making parameters, examining ADHD traits, sensory processing, coordination skills and reading ability. A few relationships surfaced, though they were task-specific and sensitive to modelling choices. In the motion task, children with stronger sight-word reading efficiency on the Test of Word Reading Efficiency showed narrower boundary separation and shorter non-decision times, echoing previous links between reading and motion processing consistent with magnocellular accounts of dyslexia. In the orientation task, sensory underresponsivity was negatively related to non-decision time once age and performance IQ were controlled. The hypothesised relationship between hyperactivity and impulsivity traits and drift rates in autistic children, based on earlier work, was not supported; in fact, there was conclusive evidence against it.

Perhaps the most sobering implication concerns methodology. The team found that results shifted between conclusive and inconclusive depending on whether hierarchical or two-step modelling was used, and depending on whether age and performance IQ were partialled out. Two-step approaches that ignore participant-level uncertainty may exaggerate evidence in either direction. The authors also note that many earlier studies failed to report the speed-accuracy instructions given to participants at all, an omission this work shows to be consequential. They call for future studies to use larger samples, preregistered and blinded analysis pipelines, clear reporting of task instructions, and shared experimental code, recommendations the current studies themselves modelled through open data on the UK Data Service and materials on the Open Science Framework.

For autistic children and their families, the practical message is quietly powerful: when instructions are clear, autistic children calibrate their decisions as responsively as anyone else. Cognitive flexibility, so often framed as a core deficit, appears intact in this domain, suggesting that differences reported in earlier studies may reflect experimental design rather than fundamental differences in how autistic minds weigh evidence, set thresholds and commit to a choice.

The diffusion decision model has a long pedigree in cognitive psychology, tracing back to work by Ratcliff and colleagues in the late twentieth century that formalised how noisy evidence accumulates toward a response threshold. Its appeal lies in separating what a simple accuracy score or average reaction time conflates: a slow response could reflect cautious threshold-setting, sluggish sensory encoding, or weak perceptual evidence, and these possibilities carry very different theoretical implications. Applying the model to children adds further complexity, since parameters such as boundary separation and drift rate change systematically over development, which is why the current studies controlled for age in several of their model variants.

The broader scientific backdrop also helps explain why the task choice mattered so much to the researchers. Psychophysical studies of autism have long produced a mixed picture, with some reporting enhanced orientation discrimination thresholds consistent with theories of heightened local perceptual processing, and others reporting reduced sensitivity to coherent motion, in line with proposals about dorsal stream vulnerability. Because orientation judgments and motion coherence judgments arguably tap different visual pathways, comparing the two tasks within a single modelling framework offered a way to test whether decision-making differences, if they existed, would follow these task-specific predictions. They did not, which itself is informative about the generality of any perceptual decision-making differences in autism.

Sensory processing differences also carry diagnostic weight in their own right. The current edition of the International Classification of Diseases defines autism partly through persistent hypersensitivity or hyposensitivity to sensory stimuli, making the observed link between sensory underresponsivity and non-decision time in the orientation task a potentially meaningful bridge between questionnaire-based sensory profiles and the latent stages of a decision model. Non-decision time encompasses processes such as sensory encoding and motor execution, so a relationship with underresponsivity invites speculation about how atypical early sensory processing might feed into the timing of overt responses, though the authors are careful to note the association was task-specific and modest.

The reading findings add another dimension. Links between reading ability and motion processing have been debated for decades under magnocellular accounts of dyslexia, and the observation that children with more efficient sight-word reading showed narrower boundaries and shorter non-decision times in the motion task is consistent with that tradition, even though the study was not designed as a test of reading theory. More broadly, the pattern of trait correlations underscores the authors’ argument that autism rarely presents in isolation: attentional traits, literacy, sensory processing and motor coordination all vary across children and may shape decision-making parameters in ways that a binary diagnostic comparison can obscure.

Finally, the open science infrastructure surrounding the work deserves note. De-identified data are archived with the UK Data Service, and experimental code, analysis scripts and preregistrations are publicly available, allowing other teams to reanalyse the same datasets under alternative modelling assumptions, which is precisely the kind of cross-lab scrutiny the authors argue the field needs.

Subject of Research: Perceptual decision-making and speed-accuracy adjustment in autistic and non-autistic children using diffusion decision models

Article Title: Autistic and non-autistic children’s perceptual decision-making in visual orientation and motion tasks and the effect of task instructions

Article References: Thomas, L., Scerif, G., Yusuf, H., Laird, M., Taylor, G., Evans, N. J., & Manning, C. (2026). Autistic and non-autistic children’s perceptual decision-making in visual orientation and motion tasks and the effect of task instructions. Attention, Perception, & Psychophysics, 88(7), Article 188. https://doi.org/10.3758/s13414-026-03330-8

Image Credits: AI Generated

DOI: 10.3758/s13414-026-03330-8

Keywords: autism, perceptual decision-making, diffusion decision model, speed-accuracy trade-off, visual orientation, motion coherence, Bayesian hierarchical modeling, cognitive flexibility, ADHD traits, response caution, drift rate, boundary separation