Stimulus Reliability Automatically Biases Temporal Integration of Discrete Perceptual Targets in the Human Brain.

Many decisions, from crossing a busy street to choosing a profession, require integration of discrete sensory events. Previous studies have shown that integrative decision-making favors more reliable stimuli, mimicking statistically optimal integration. It remains unclear, however, whether reliability biases operate even when they lead to suboptimal performance. To address this issue, we asked human observers to reproduce the average motion direction of two suprathreshold coherent motion signals presented successively and with varying levels of reliability, while simultaneously recording whole-brain activity using electroencephalography. By definition, the averaging task should engender equal weighting of the two component motion signals, but instead we found robust behavioral biases in participants' average decisions that favored the more reliable stimulus. Using population-tuning modeling of brain activity we characterized tuning to the average motion direction. In keeping with the behavioral biases, the neural tuning profiles also exhibited reliability biases. A control experiment revealed that observers were able to reproduce motion directions of low and high reliability with equal precision, suggesting that unbiased integration in this task was not only theoretically optimal but demonstrably possible. Our findings reveal that temporal integration of discrete sensory events in the brain is automatically and suboptimally weighted according to stimulus reliability. Many everyday decisions require integration of several sources of information. To safely cross a busy road, for example, one must consider the movement of vehicles with different speeds and trajectories. Previous research has shown that individual stimuli are weighted according to their reliability. Whereas reliability biases typically yield performance that closely mimics statistically optimal integration, it remains unknown whether such biases arise even when they lead to suboptimal performance. Here we combined a novel integrative decision-making task with concurrent brain recording and modeling to address this question. While unbiased decisions were optimal in the task, observers nevertheless exhibited robust reliability biases in both behavior and brain activity, suggesting that reliability-weighted integration is automatic and dissociable from statistically optimal integration.