Fig. 1.

Methodological drivers of a slow learning curve. A) The effect of group averaging across animals. Left, schematic of individual animal learning curves (gray lines), defined learning criterion (dotted line), and threshold crossings (red circles). Middle, averaging individual learning curves aligned to the start of training creates the appearance of a slow and gradual process. Right, aligning learning curves to a defined learning criterion identifies a more rapid, and shared, dynamic across animals (within the red dotted box) and may provide better group averaging for use in neural data analysis. B) The effect of session averaging within an animal. Schematic of learning curve across training sessions shows a smooth gradual increase in performance. Early (left inset) and late (right inset) in learning, the session averaged performance provides a reasonable description of the behavior. At the ‘slope’ of the learning curve, however, the within day change (middle inset) can be dramatic with fast transitions in performance that are obscured by session-based averaging. C) The effect of motivation on within day performance. Expert performance can be influenced by an animals’ internal state. Motivation can change over the course of an expert session, driving errors typically ascribed to perceptual judgements. Early in the session (1), over motivation might be the driver of a high false alarm rate, while by the end, satiety might drive an animal to miss. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)