Cortical neurons comprising an output pathway form a specialized population code that enhances the propagation of information to a downstream target, potentially improving the accuracy of decision-mak...

Animals must exploit environmental regularities to make adaptive decisions, yet the learning algorithms that enabels this flexibility remain unclear. A central question across neuroscience, cognitive science, and machine learning, is whether learning relies on generative or discriminative strategies. Generative learners build internal models the sensory world itself, capturing its statistical structure; discriminative learners map stimuli directly onto choices, ignoring input statistics. These strategies rely on fundamentally different internal representations and entail distinct computational trade-offs: generative learning supports flexible generalisation and transfer, whereas discriminative learning is efficient but task-specific. We compared humans, rats, and mice performing the same auditory categorisation task, where category boundaries and rewards were fixed but sensory statistics varied. All species adapted their behaviour near-optimally, consistent with a normative observer constrained by sensory and decision noise. Yet their underlying algorithms diverged: humans predominantly relied on generative representations, mice on discriminative boundary-tracking, and rats spanned both regimes. Crucially, end-point performance concealed these differences, only learning trajectories and trial-to-trial updates revealed the divergence. These results show that similar near-optimal behaviour can mask fundamentally different internal representations, establishing a comparative framework for uncovering the hidden strategies that support statistical learning. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust, https://ror.org/029chgv08, 219880/Z/19/Z, 225438/Z/22/Z, 219627/Z/19/Z Gatsby Charitable Foundation, GAT3755 UK Research and Innovation, https://ror.org/001aqnf71, EP/Z000599/1

Spatially aligned faces presented in a continuous stream in the periphery appear distorted and grotesque. This flashed face distortion effect (“FFDE”) was first...

Internal and external selection processes can codevelop in time to yield efficient search behavior.

Nature - Experiments with human volunteers and macaques show that expectations produced by probabilistic cueing of future sensory inputs shape motor circuit dynamics in order to increase the...

Working memory improves during adolescent brain development. Zhu et al. tracked monkeys through adolescence, revealing that maturation of white matter tracts and refinement of neural firing patterns s...

In visually complex and dynamically changing environments, humans often face the challenge of filtering out salient stimuli that are presently irrelevant to their tasks. Recent evidence suggests that ...

A core function of visual working memory (WM) is to sustain mental representations of recent visual inputs, thereby bridging moments of experience. Th…

Slow change blindness, when attentive observers fail to notice large changes that happen gradually, raises questions about how visual information is combined across time. One plausible integration str...

Working memory (WM) representations that are distributed across the brain can be flexibly recruited to best guide behavior[1][1]–[4][2]. For instance, information may be represented relatively more st...

Two EEG studies in healthy human adults suggest that choice history and stimulus probability-induced biases in somatosensory perception are reflected in distinct prestimulus beta power modulations across distinct cortical regions.

Prior work on visual memory has suggested that humans have a high-capacity but imperfect memory: image representations accumulate noise over time, whi…

Neurons that respond emergently to illusory contours drive pattern completion in V1. Pattern completion in lower cortical areas may therefore mediate perceptual inference by selectively reinforcing ac...

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