Although time is a fundamental dimension of life, we do not know how the brain encodes the temporal information. Several brain areas underlie the temporal information, such as the hippocampus, prefrontal cortex, and striatum, but evidence of how they cooperate to process temporal information is scarce. Notably, the analysis of neural activity during learning are rare, mainly because timing tasks usually take a long time to train. Here we investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the roles of the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter become more engaged as animals become more proficient in the task.

Interval timing is fundamental for humans and non-human animals to interact with their environment. Several studies that investigate temporal processing combine behavioural tasks with neurophysiological methods, such as electrophysiological recordings (EEG). However, in the majority of these studies, it is hard to dissociate whether EEG activity reflects temporal or decisional information. In the present study, we investigated how time and decision is encoded in the EEG signal while human participants performed a temporal categorisation task with two different temporal references. Using a combination of evoked potentials and multivariate pattern analysis, we show that: (1) During the interval to-be-timed, both temporal and decisional information are encoded;(2) Activity evoked by the end of the interval encodes almost exclusively decisional information. These results suggest that decisional aspects of the task better explain EEG activity commonly related to temporal processing. The interplay between the encoding of time and decision is consistent with recent proposals that approximate temporal processing with decisional models.