Topic: confirmation bias

Sensory inputs available to animals are often open to multiple interpretations. When evaluating the likelihood of a certain interpretation, it is important that they consider not only evidence confirming that interpretation, but also evidence against it. Human reasoning often fails to appropriately weigh negative evidence, a phenomenon termed confirmation bias. However, since behaving based on false beliefs is costly, animals may detect negative evidence better in the context of innate, naturalistic behaviors. An example of such behaviors is visual course stabilization. Existing models assume

Committing to a decision tends to bias future behaviors, a phenomenon dubbed “confirmation bias”. Daily life requires us to make decisions on remembered past experiences; still, the understanding of confirmation bias in working memory remains sparse. Here, we show that the stimulus-specific drift dynamics should be incorporated into any viable account of confirmation bias in working memory. We crafted a paradigm where participants view a grating briefly, make a binary decision on it, and estimate its orientation, with varying delays between those epochs. We developed a dynamical model wherein

Perception is influenced by past choices. In combined discrimination-estimation experiments a categorical choice leads to two biases: (i) a choice-dependent confirmation bias in the continuous estimate of the stimulus average, and (ii) an overall decrease in the sensitivity to subsequent sensory evidence. It remains unknown what neural mechanisms give rise to these post-decision biases and whether continuous estimates and categorical choices are computed in a common cortical circuit. Here, we develop a neural network model that addresses these questions. We study the integration of continuous