Slow or sudden: Re-interpreting the learning curve for modern systems neuroscience

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.

Keywords

• Learning
• Instrumental learning
• Behavior
• Systems neuroscience
• Large-scale recordings
• Big data