ABSTRACT
Animals learn multiple spatiotemporal contingencies and organize their anticipatory responses accordingly. The representational/computational capacity that underlies such spatiotemporally guided behaviors is not fully understood. To this end, we investigated whether mice make temporal inferences of novel locations based on previously learned spatiotemporal contingencies. We trained 18 C57BL/6J mice to anticipate reward after three different intervals at three different locations and tested their temporal expectations of a reward at five locations simultaneously, including two locations that were not previously associated with reward delivery but adjacent to the previously trained locations. If mice made spatiotemporal inferences, they were expected to interpolate between duration pairs associated with previously reinforced hoppers surrounding the novel hopper. We found that the maximal response rate at the novel locations indeed fell between the two intervals reinforced at the surrounding hoppers. We argue that this pattern of responding might be underlain by spatially constrained Bayesian computations.
Subject(s)
Learning , Reward , Mice , Animals , Bayes Theorem , Mice, Inbred C57BLABSTRACT
Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.
