When external feedback about decision outcomes is lacking, agents need to adapt their decision policies based on an internal estimate of the correctness of their choices (i.e., decision confidence). We hypothesized that agents use confidence to continuously update the tradeoff between the speed and accuracy of their decisions: When confidence is low in one decision, the agent needs more evidence before committing to a choice in the next decision, leading to slower but more accurate decisions. We tested this hypothesis by fitting a bounded accumulation decision model to behavioral data from three different perceptual choice tasks. Decision bounds indeed depended on the reported confidence on the previous trial, independent of objective accuracy. This increase in decision bound was predicted by a centro-parietal EEG component sensitive to confidence. We conclude that internally computed neural signals of confidence predict the ongoing adjustment of decision policies.
All data has been deposited online and can be freely accessed (https://osf.io/83x7c/ and https://github.com/AnnikaBoldt/Boldt_Yeung_2015). All analysis code is available on GitHub.
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: Written informed consent and consent to publish was obtained prior to participiation. All procedures were approved by the local ethics committee of the University Medical Center, Hamburg-Eppendorf (PV5512).
© 2019, Desender et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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