Distinguishing learning asymmetry from perseveration in behavioral models
A statistical test that distinguishes genuine learning asymmetries from spurious effects is introduced and it is found that some previously reported confirmation biases are fragile, albeit others remain robust even at an across-study level.
Juan M. Vidal-Perez, Raymond J. Dolan, Rani Moran
· Nature Communications · 0 citations