LPP Seminar: Alexandre Pouget, Université de Genève
January 06 2014, 11h
Not noisy, just wrong: the computational and neural causes of behavioral variability
Behavior varies from trial to trial even when the stimulus is maintained as constant as possible. In many models, this variability is attributed to noise in the brain. Here, we propose that there is another major cause of variability: suboptimal inference. Importantly, we argue that in most tasks of interest, and particularly complex ones, suboptimal inference is likely to be the dominant component of behavioral variability. This perspective explains a variety of intriguing observations, including why variability appears to be larger on the sensory than on the motor side, why our sensors are sometimes surprisingly unreliable and why behavioral thresholds often follow Weber’s law. It also predicts specific patterns of correlations among neurons, which are markedly different from the ones that are currently assumed to exist in cortical circuits.