Rationale According to psychological theories, cognitive distortions play a pivotal role in the aetiology and recurrence of mood disorders. Although clinical evidence for the coexistence of depression and altered sensitivity to performance feedback is relatively coherent, we still do not know whether increased or decreased sensitivity to positive or negative feedback is associated with ‘pro-depressive’ profile in healthy subjects. Objective Our research has been designed to answer this question, and here, we present the first steps in that direction. Methods Using a rat version of the probabilistic reversal-learning (PRL) paradigm, we evaluated how sensitivity to negative and positive feedback influences other cognitive processes associated with mood disorders, such as motivation in the progressive ratio schedule of reinforcement (PRSR) paradigm, hedonic status in the sucrose preference (SP) test, locomotor and exploratory activity in the open field (OF) test, and anxiety in the light/dark box (LDB) test. Results The results of our study demonstrated for the first time that in rodents, sensitivity to negative and positive feedback could be considered a stable and enduring behavioural trait. Importantly, we also showed that these traits are independent of each other and that trait sensitivity to positive feedback is associated with cognitive flexibility in the PRL test. The computational modelling results also revealed that in animals classified as sensitive to positive feedback, the α learning rates for both positive and negative reward prediction errors were higher than those in animals classified as insensitive. We observed no statistically significant interactions between sensitivity to negative or positive feedback and the parameters measured in the PRSR, SP, OF or LDB tests. Conclusions Further studies using animal models of depression based on chronic stress should reveal whether sensitivity to feedback is a latent trait that when interacts with stressful life events, could produce correlates of depressive symptoms in rats.

Reinforcement learning causes an action that yields a positive outcome more likely to be taken in the future. Here, we investigate how the time elapsed from an action affects subsequent decisions. Groups of C57BL6/J mice were housed in IntelliCages with access to water and chow ad libitum; they also had access to bottles with a reward: saccharin solution, alcohol, or a mixture of the two. The probability of receiving a reward in two of the cage corners changed between 0.9 and 0.3 every 48 hr over a period of ~33 days. As expected, in most animals, the odds of repeating a corner choice were increased if that choice was previously rewarded. Interestingly, the time elapsed from the previous choice also influenced the probability of repeating the choice, and this effect was independent of previous outcome. Behavioral data were fitted to a series of reinforcement learning models. Best fits were achieved when the reward prediction update was coupled with separate learning rates from positive and negative outcomes and additionally a “fictitious” update of the expected value of the nonselected choice. Additional inclusion of a time‐dependent decay of the expected values improved the fit marginally in some cases.

Parkinson's disease (PD) is characterized by three main motor symptoms: bradykinesia, rigidity and tremor. PD is also associated with diverse nonmotor symptoms that may develop in parallel or precede motor dysfunctions, ranging from autonomic system dysfunctions and impaired sensory perception to cognitive deficits and depression. Here, we examine the role of the progressive loss of dopaminergic transmission in behaviors related to the nonmotor symptoms of PD in a mouse model of the disease (the TIF IADATCreERT2 strain). We found that in the period from 5 to 12 weeks after the induction of a gradual loss of dopaminergic neurons, mild motor symptoms became detectable, including changes in the distance between paws while standing as well as the step cadence and sequence. Male mutant mice showed no apparent changes in olfactory acuity, no anhedonia-like behaviors, and normal learning in an instrumental task; however, a pronounced increase in the number of operant responses performed was noted. Similarly, female mice with progressive dopaminergic neuron degeneration showed normal learning in the probabilistic reversal learning task and no loss of sweet-taste preference, but again, a robustly higher number of choices were performed in the task. In both males and females, the higher number of instrumental responses did not affect the accuracy or the fraction of rewarded responses. Taken together, these data reveal discrete, dopamine-dependent nonmotor symptoms that emerge in the early stages of dopaminergic neuron degeneration.

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Reinforcement learning makes an action that yielded a positive outcome more likely to be taken in the future. Here, we investigate how the time elapsed from an action affects subsequent decisions. Groups of C57BL6/J mice were housed in IntelliCages with access to water and chow ad libitum, and they were able to access bottles with a reward in the form of a saccharin solution (0.1% w/v), alcohol (4% w/v) or a mixture of the two. The probability of receiving a reward in two of the cage corners changed between 0.9 and 0.3 every 48 h over a period of ~33 days. We observed that in most animals, the odds of repeating the choice of a corner were increased if that choice was previously rewarded. Interestingly, in many cases, the time elapsed from the previous choice also increased the probability of repeating the choice, irrespective of the previous outcome. Behavioral data were fitted with a series of reinforcement learning models based on Q-learning. Then, extensions were introduced to account for attention or task complexity components or to directly include the interval length in order to simulate the decay of the expected value or to increase the probability of repeating the same choice. We find that introducing an interval-dependent adjustment to repeating the same choice allowed for the best prediction of the observed behavior, and the size of this effect may differ depending on the type of reward offered.

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