Rodents monitor their error in self-generated duration on a single trial basis

A fundamental question in neuroscience is what type of internal representation leads to complex, adaptive behavior. When faced with a deadline, individuals’ behavior suggests that they represent the mean and the uncertainty of an internal timer to make near-optimal, time-dependent decisions. Whether this ability relies on simple trial-and-error adjustments or whether it involves richer representations is unknown. Richer representations suggest a possibility of error monitoring, that is, the ability for an individual to assess its internal representation of the world and estimate discrepancy in the absence of external feedback. While rodents show timing behavior, whether they can represent and report temporal errors in their own produced duration on a single-trial basis is unknown. We designed a paradigm requiring rats to produce a target time interval and, subsequently, evaluate its error. Rats received a reward in a given location depending on the magnitude of their timing errors. During the test trials, rats had to choose a port corresponding to the error magnitude of their just-produced duration to receive a reward. High-choice accuracy demonstrates that rats kept track of the values of the timing variables on which they based their decision. Additionally, the rats kept a representation of the mapping between those timing values and the target value, as well as the history of the reinforcements. These findings demonstrate error-monitoring abilities in evaluating self-generated timing in rodents. Together, these findings suggest an explicit representation of produced duration and the possibility to evaluate its relation to the desired target duration.

In neuroscience, a fundamental question is how rich the internal representation of an individual’s experience must be to yield adaptive behavior. Let us consider a hungry individual in need of finding food fast: The individual may adopt a trial-and-error foraging strategy to maximize reward but may also, to maximize its efficiency, represent rich experiential variables, such as how much time it takes to reach a source of food. Both representing elapsed time and monitoring its inherent uncertainty plays an important role in adaptive behavior, learning, and decision making (1). When representing these variables, the sources of uncertainty are both exogenous (stimuli driven) and endogenous (neural implementation). The mapping of exogenous sources of temporal uncertainty has been well described in timing behavior: For instance, mice can adjust their behaviors to the width of the distribution of temporal intervals provided through external stimuli (2). On the other hand, the endogenous sources of uncertainty for time perception are less understood and more difficult to address.Evidence that animals are sensitive and have access to the internal uncertainty of elapsed time comes from a task in which the individual must produce a required target duration using a lever press or a key press (1, 3, 4). In a task in which individuals must produce an interval of fixed duration to obtain a reward (Fig. 1A), a plausible strategy to maximize reward would be to set the produced duration to be longer than the required target duration so as to allow a margin of error [internal target duration; (5)]. This is because the larger an individual’s representational uncertainty, the larger the margin of error to maximize the reward. Consistent with this, studies have shown that the magnitude of error in produced intervals varies with the magnitude of temporal uncertainties (6, 7), and participants with larger temporal uncertainty set larger margins of errors [Fig. 1B and SI Appendix, Fig. S2; (1, 7)]. The observed optimization of timing behavior begs the question of how rich the representation of elapsed time must be.Open in a separate windowFig. 1.The TP task and error-monitoring protocol. (A) Schematic of a box arrangement with a lever available in the middle of the panel and reward ports on the left and right side of the lever. Reward availability was signaled by the port lit, depicted by the lightbulbs. Reward delivery was triggered by rats’ nose poke in the reward port. Depending on the group assignment, rats had to either hold the lever pressed for a minimum of 3.2 s (HOLD group) or press the lever twice with a minimal delay (3.2 s) between two presses (PRESS group). (B) TP performance, in error-monitoring test sessions, follows Weber’s law for both groups, with signatures of optimality. (Upper) Probability density functions over TPs for each individual rat in HOLD (blue) and PRESS (red) groups. Thresholds Θ (blue and red dashed lines for HOLD and PRESS groups, respectively) are plotted for each individual. (Bottom Left) Average probability density functions over TPs for HOLD and PRESS groups superimposed. Note the distribution shift and width shrinkage for HOLD group. (Bottom Right) For each rat, µ(TP) is plotted against σ(TP). Both at the individual and at the group level the PRESS rats showed larger µ(TP) and σ(TP), visible as an upward right shift of the red curve. This pattern indicates that rats make their choices optimally, taking into account their level of TP variability. The results hold within each rat and across sessions (SI Appendix, Fig. S3). (C) Schematic depiction of how rewards were assigned to specific parts of TP distribution. Green color is used for “small error” (SE) trials and orange color for “large error” (LE) trials. Red color indicates TPs that were out of reward range. The arrows indicate probabilistic assignment of TP type (SE or LE) to left and right ports, on training trials. On test trials, the food–port assignments remained, but both ports were available and, thus, the amount of reward was driven by the rat’s choice. (D) Schematic of a trial structure. From the top to bottom, the succession of task events is depicted. They alternate along TP axis (color bar with red, green, and orange) and show different scenarios that are determined by the rats’ performance on TP in single trials. ITI is the last event in a single-trial sequence.A trial-and-error strategy would predict that near-optimal behavior can be parsimoniously explained by adaptation so that timing behavior would fluctuate around the required duration. The representational view would predict that uncertainty and trial-to-trial errors are experiential variables used by the animals to monitor their timing behavior.To settle the question of whether rodents can monitor their timing errors relative to their target on a trial-by-trial basis, we developed a task inspired by human work. Humans required to generate a time interval can also reliably report the magnitude of their errors and their sign (8) (i.e., they can evaluate by how much [magnitude] their generated duration was too short or too long [sign], with respect to the target duration). Humans can also report how confident they are in their timing behavior (9). We tested here these temporal cognitive abilities in rats, which were required to produce a time interval and correctly report, in order to obtain a reward, the magnitude of their timing errors on some test trials. We show that rats correctly reported the magnitude of their timing error, suggesting that their timing behavior uses explicit representations of time intervals together with their uncertainty around the internal target duration.