ReviewIBRO neuroscience reports2022
Slow or sudden: Re-interpreting the learning curve for modern systems neuroscience.
Review in IBRO neuroscience reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- A shared predictive architecture in the sensory cortex for statistical and reward-based learning.Current opinion in neurobiology · 2026Review
- Article
- Revealing abrupt transitions from goal-directed to habitual behavior.Nature communications · 2026Article
- Distinct roles of cortical layer 5 subtypes in associative learning.Nature communications · 2026Article
- Infinite hidden Markov models can dissect the complexities of learning.Nature neuroscience · 2026Article
- Stimulus uncertainty and relative reward rates determine adaptive responding in perceptual decision-making.PLoS computational biology · 2025Article
- Flexible inference for animal learning rules using neural networks.Advances in neural information processing systems · 2025Article
- Pre-existing visual responses in a projection-defined dopamine population explain individual learning trajectories.Current biology : CB · 2024Article
- Rapid emergence of latent knowledge in the sensory cortex drives learning.bioRxiv : the preprint server for biology · 2024Article
- Inhibitory neurons control the consolidation of neural assemblies via adaptation to selective stimuli.Scientific reports · 2023Article
- Vocalization categorization behavior explained by a feature-based auditory categorization model.eLife · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Learning is fundamental to animal survival. Animals must learn to link sensory cues in the environment to actions that lead to reward or avoid punishment. Rapid learning can then be highly adaptive and the difference between life or death. To explore the neural dynamics and circuits that underlie learning, however, has typically required the use of laboratory paradigms with tight control of stimuli, action sets, and outcomes. Learning curves in such reward-based tasks are reported as slow and gradual, with animals often taking hundreds to thousands of trials to reach expert performance. The slow, highly variable, and incremental learning curve remains the largely unchallenged belief in modern systems neuroscience. Here, we provide historical and contemporary evidence that instrumental forms of reward-learning can be dissociated into two parallel processes: knowledge acquisition which is rapid with step-like improvements, and behavioral expression which is slower and more variable. We further propose that this conceptual distinction may allow us to isolate the associative (knowledge-related) and non-associative (performance-related) components that influence learning. We then discuss the implications that this revised understanding of the learning curve has for systems neuroscience.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.