Evidence integration and decision-confidence are modulated by stimulus consistency

Abstract

Evidence-integration is a normative algorithm for choosing between alternatives with noisy evidence, which has been successful in accounting for a vast amount of behavioral and neural data. However, this mechanism has been challenged as tracking integration boundaries sub-serving choice has proven elusive. Here we first show that the decision boundary can be monitored using a novel, model-free behavioral method, termed Decision-Classification Boundary. This method allowed us to both provide direct support for evidence-integration contributions and to identify a novel integration-bias, whereby incoming evidence is modulated based on its consistency with evidence from preceding time-frames. This consistency bias was supported in three cross-domain experiments, involving decisions with perceptual and numerical evidence, which showed that choice-accuracy and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, this bias fosters performance by enhancing robustness to integration noise. We argue this bias constitutes a new form of micro-level, within-trial, confirmation bias and discuss implications to broad aspects of decision making.