Methods sections are often missing critical details needed to reproduce an experiment. Methodological shortcut citations, in which authors cite previous papers instead of describing the method in detail, may contribute to this problem. This meta-research study used three approaches to systematically examine the use of shortcut citations in neuroscience, biology and psychiatry. First, we examined papers to determine why authors use citations in the methods section and to assess how often shortcut citations were used. Common reasons for using citations in the methods section included explaining how something was done by citing a previous resource that used the method (methodological shortcut citation), giving credit or specifying what was used (who or what citation), and providing context or a justification (why citation). Next, we reviewed 15 papers to determine what can happen when readers follow shortcut citations to find methodological details. While shortcut citations can be used effectively, problems encountered included difficulty identifying or accessing the cited materials, missing or insufficient descriptions of the cited method, and chains of shortcut citations. Third, we examined journal policies. Fewer than one quarter of journals had policies describing how authors should report methods that have been described previously or asking authors to explain modifications of previously described methods. We propose that methodological shortcut citations should meet three criteria; cited resources should describe a method very similar to the authors' method, provide enough detail to allow others to implement the method, and be open access. We outline actions that authors and journals can take to use shortcut citations responsibly, while fostering a culture of open and reproducible methods reporting.

Reinforcement learning algorithms have a long-standing success story in explaining the dynamics of instrumental conditioning in humans and other species. While normative reinforcement learning models are critically dependent on external feedback, recent findings in the field of perceptual learning point to a crucial role of internally-generated reinforcement signals based on subjective confidence, when external feedback is not available. Here, we investigated the existence of such confidence-based learning signals in a key domain of reinforcement-based learning: instrumental conditioning. We conducted a value-based decision making experiment which included phases with and without external feedback and in which participants reported their confidence in addition to choices. Behaviorally, we found signatures of self-reinforcement in phases without feedback, reflected in an increase of subjective confidence and choice consistency. To clarify the mechanistic role of confidence in value-based learning, we compared a family of confidence-based learning models with more standard models predicting either no change in value estimates or a devaluation over time when no external reward is provided. We found that confidence-based models indeed outperformed these reference models, whereby the learning signal of the winning model was based on the prediction error between current confidence and a stimulus-unspecific average of previous confidence levels. Interestingly, individuals with more volatile reward-based value updates in the presence of feedback also showed more volatile confidence-based value updates when feedback was not available. Together, our results provide evidence that confidence-based learning signals affect instrumentally learned subjective values in the absence of external feedback.