You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: CACNA1C gene regulates behavioral strategies in operant rule learning}
Article:
Abstract:
Behavioral experiments are usually designed to tap into a specific cognitive function, but animals may solve a given task through a variety of different and individual behavioral strategies, some of them not foreseen by the experimenter. Animal learning may therefore be seen more as the process of selecting among, and adapting, potential behavioral policies, rather than mere strengthening of associative links. Calcium influx through high-voltage-gated Ca2+ channels is central to synaptic plasticity, and altered expression of Cav1. 2 channels and the CACNA1C gene have been associated with severe learning deficits and psychiatric disorders. Given this, we were interested in how specifically a selective functional ablation of the Cacna1c gene would modulate the learning process. Using a detailed, individual-level analysis of learning on an operant cue discrimination task in terms of behavioral strategies, combined with Bayesian selection among computational models estimated from the empirical data, we show that a Cacna1c knockout does not impair learning in general but has a much more specific effect: the majority of Cacna1c knockout mice still managed to increase reward feedback across trials but did so by adapting an outcome-based strategy, while the majority of matched controls adopted the experimentally intended cue-association rule. Our results thus point to a quite specific role of a single gene in learning and highlight that much more mechanistic insight could be gained by examining response patterns in terms of a larger repertoire of potential behavioral strategies. The results may also have clinical implications for treating psychiatric disorders.
Introduction:
The ability to learn, to adapt one’s own behavior in order to optimize positive and avoid negative feedback, is central to all living beings. Animals like humans are constantly seeking to infer the( causal) structure of their environment and to predict the outcomes of their actions[1, 2], sometimes to a degree that “superstitial beliefs” about environmental contingencies may form[3]. However, since the environment is only partially observable, with an often infinite or exponentially exploding space of possibilities, inferring the optimal course of actions is, in general, a computationally intractable problem[4]. Hence, animals like humans rely on heuristics, strategies, and ecological biases that favor certain types of environmental contingencies over others and narrow down the search space[5, 6]. For instance, rodents are predisposed to employ ecologically sensible strategies like win-stay, lose-shift, or alternate[7–9], and these behaviors are also more readily acquired[10, 11]. Moreover, recent evidence suggests that animal learning, even on apparently simple conditioning tasks, may often engage active, evidence-driven choices among behavioral strategies, rather than mere passive strengthening of stimulus-response( SR) associations[12, 13]. The upshot here is that animal learning may rely on a large variety of different previously acquired or predisposed strategies, rather than on a uniform mechanism, like SR strengthening, that could either be enabled or disabled. Given this larger repertoire of potential a priori strategies and heuristics with which animals may enter any given experimental task context, there may be more than one way for increasing reward[14, 15], even though this may not have been experimentally intended. In such situations, more conventional analysis in terms of error counts or reaction times compared among experimental groups may perhaps( wrongly) infer that one group is simply diminished in its learning abilities compared to another, while the observed differences may actually be rooted in the different behavioral strategies applied. Individual differences in learning strategies have recently been approached within the framework of computational reinforcement learning( RL) and decision-making theories[e. g., 16, 17], which have been applied, for instance, to disentangle model-free and model-based forms of learning and their distinct neural substrates[18–20] or to reveal strategic interactions during( model-based) sequential planning[21]. Building on ideas like these, we show here how a genetic variation may determine an animal’s learning strategy and associated performance patterns. Specifically, we studied learning in terms of behavioral strategies on a 2-choice operant cue discrimination task in 2 groups of mice, a group with a genetic modification associated with severe learning deficits[22], namely, a selective knockout( KO) of the Cacna1c gene coding for the α subunit of Cav1. 2 L-type high-voltage-dependent calcium channels( Cav1. 2NesCre), and matched controls with intact gene expression( Cav1. 2fl/fl). Calcium influx through voltage-dependent calcium channels is essential for synaptic plasticity and other cellular adaptations[23] thought to underlie instrumental learning[24]. The CACNA1C gene has also been implicated in the pathophysiology of psychiatric disease[22, 25], rendering it an important target for understanding learning mechanisms not only in normal but also psychiatric conditions. By studying the detailed pattern of behavioral responses and rewards received across the learning process, combined with analyses based on behavioral( reinforcement) learning models, we found that Cav1. 2NesCre mice did not simply exhibit a general learning deficit but rather relied on different behavioral strategies than Cav1. 2fl/fl mice.
Discussion:
In a real ecological context, there is a multitude of different factors and contextual conditions that will or could influence whether a behavioral plan will ultimately lead to success. When an animal hits on a favorable outcome it had not expected, it is often difficult to discern which of the many foregoing sensory inputs and actions were indeed crucial for predicting and achieving this success and which are irrelevant and would better be ignored; also termed the “credit assignment problem”[32]. To parse out the crucial and predictive factors from limited experience, the small sample of reality an animal has access to in its lifetime, would be difficult enough if the world were rather deterministic. But on top of that, the world is highly uncertain and probabilistic, full of unforeseen events that complicate inference on environmental structure. To deal with this, animals have evolved or acquired a larger repertoire of general strategies, heuristics, shortcuts, and response preferences[5, 6] that could guide them through novel situations by biasing probability distributions toward certain subsets of situation/action/outcome triples and, potentially, are also designed to minimize an animal’s cognitive effort[33]. All these different response strategies and biases almost certainly come into play in any novel laboratory situation an animal faces. Animals are unlikely to behave completely randomly but, according to recent evidence, might actually actively probe and test out different such strategies and behavioral “hypotheses” whilst learning[12, 13]. In rodents, common a priori strategies are win-stay, win-shift, lose-shift, or alternate[7, 9, 10], which all could make sense in one environmental setting or another[8, 11, 34]. For instance, rats hoard food in their underground burrows—once the food is gone from one of the chambers or, more generally, a food source is depleted, it is certainly reasonable to win-shift. In other circumstances, food sources may be expected to refill( perhaps in certain temporal intervals), such that a win-stay strategy would be more appropriate. Hence, it seems reasonable that even the Cav1. 2fl/fl animals in our task reverted to one of these more basic strategies, namely win-stay, when the cue information became ambiguous on certain trials. In fact, some of the Cav1. 2fl/fl animals that did not show as high performance levels as their group mates appeared to have adopted an outcome-based strategy as well, further illustrating that this is not a behavioral pattern “outside the normal scope” but a sensible, although suboptimal, way to approach this task. Often, from an experimenter’s point of view, it might in fact not be that easy to construct experimental situations that clearly dissociate what the experimenter intended the animals to do from one of these basic, a priori strategies animals bring into the task. For instance, to probe working memory in rodents, delayed response or alternation tasks on a T-maze are often used. However, some studies have suggested that, rather than using working or short-term memory to encode correct responses as intended by the experimental design, rodents may rely on subtle external bodily cues like head orientation maintained throughout the delay phase of the task[14, 15]. Such behavioral alternatives or confounds with other previously acquired or prepared strategies may easily be overlooked or, indeed, sometimes hard to avoid. There could also be other hidden dependencies or predictable structure in a task design( especially in learning tasks that evolve across trials) that are not immediately obvious and that animals may attempt to exploit. Even if overall( given unlimited experience) everything is well balanced and controlled by design, the individual history of trial drawings, responses, and outcomes may be locally highly nonrandom and bias animals differentially toward certain strategies rather than others[35, see also 36, 37]. Thus, consideration of different possibly rewarding behavioral strategies may not only provide a lot more mechanistic insight by revealing the details of how an animal solves the task posed but may, in fact, prove important for drawing the right conclusions with respect to the involved cognitive and behavioral capacities. For these reasons, it is also important that the behavioral analysis takes the animals’ perspective by taking into account the exact same set or sequence of trial types as encountered by the animals in order to accurately infer what they could have possibly learned about different behavioral policies given their individual choice and reward histories[35]. In fact, it is a particular advantage of the bootstrap- and model-based analyses reported here that they adopt the animal’s point of view, incorporating the exact same sequence of stimulus events, behavioral responses, and reward returns as experienced by the animals, and thus yield day-by-day( or even trial-to-trial) predictions on an individual animal basis( e. g., Fig 5). Further note that the next trial type encountered by an animal is often a consequence of the animal’s own behavior, as in the case of correction trials, hence a reflection of the animal’s own choices that should be acknowledged in an analysis of behavioral strategies. With regards to the specific experiments conducted here, we therefore would like to emphasize that none of the analyses made any a priori assumptions about the distribution or occurrences of trial types but went exactly with those trials as empirically observed and that, of course, the experimental conditions for all animal strains were exactly the same( and hence all differences in experienced trial sequences a consequence of differences in the animals’ behavior). It may also be worth noting that the animals showed strategy-consistent responses across all 3 task stages, although these considerably differed in the composition of trials and trial transitions they harbored. Our RL model analysis was based on a Rescorla–Wagner rule augmented by a Pearce–Hall-type mechanism[27, 38], in which the learning rate is regulated by the recent history of successful outcome predictions, increasing as the mismatches between predicted and experienced rewards become more numerous( and thus indicating to the animal that its current response policies need to be altered). Although the results using this type of learning rule were completely consistent with those obtained with a pure Rescorla–Wagner rule( as well as with a simplified hybrid model; see Materials and methods), we adopted this learning mechanism, as it turned out to yield the best description of the behavioral data based on Bayesian model comparisons( see S8 Fig). Animal learning has also variously been described in terms of stimulus-response versus action-outcome associations[39, 40]. In light of this discussion, it may therefore be important to emphasize again that all animal strains studied here did indeed use the outcomes to regulate and advance their behavioral policies, i. e., were all able to utilize the reward feedback in adjusting their behavior in a profitable manner. The specific difference between the animal strains was that the Cav1. 2NesCre mice did not base their responses on the presented cue but on where the reward occurred on the previous trial( i. e., on the previous outcome), and the bootstrap- and model-based analyses, which specify the assumptions about the behavioral process in formal detail, confirmed that this provided a sufficient explanation for the animals’ behavior. Calcium( Ca2+) influx through N-methyl-D-aspartate( NMDA) and voltage-gated calcium channels is crucial for synaptic plasticity[24] and nuclear gene expression associated with plasticity[41] and cell survival[42]. Although there are several other sources of cellular Ca2+ influx, like NMDA or T- or N-type Ca2+ channels[43, 44], a cortex-wide, functional ablation of L-type Ca2+ channels through a knockout of the Cacna1c gene is therefore supposed to impair plasticity, and thus learning, in a variety of settings. Indeed, this selective knockout has been associated with deficits in spatial memory[45, 46] and observational fear learning[47]. Although the cortex-wide lack of Cav1. 2 in the present preparation prevents a more precise localization of the effects reported here, a recent study employing a similar touchscreen-based, two-choice stimulus discrimination task in mice revealed a specific dependence on dorsal striatum[48], in line with earlier studies linking this region to the mediation of SR associations[39, 49, 50]. Interestingly, this study also involved correction trials, which may facilitate outcome-based strategies, and found that performance scores in the lesioned animals settled around similar levels as observed for the Cav1. 2NesCre mice here, about 60%–65%. Although outcome-based strategies were not investigated by Delotterie and colleagues[48], these findings could indicate that the present effects may be more specifically rooted in L-type Ca2+ channel dysfunction in dorsal striatum. Findings like these may also open new perspectives on treatment options in psychiatry. Specifically, the CACNA1C gene has been associated with severe psychiatric illnesses such as depression[51], bipolar disorder[52], autism[53], and schizophrenia[51, 54], for which it has been listed as a risk gene[51, 55]. The present results suggest that for modifying a patient’s behavior, certain avenues, potentially resting on biologically more elementary behavioral options, may be fruitful to explore where other forms of behavioral therapy have failed. They at least suggest that a detailed scrutiny of how a patient solves a specific set of cognitive or emotional tasks, on top of standard clinical assessment, may provide valuable insights into how to best address the “behavioral malfunctioning” of a patient in terms of the specific behavioral interventions and rules the therapy should focus on( see also[56]). Hence, in future applications, the combination of standard cognitive test batteries with model-estimation techniques, as used here, could lead into the design of more specifically targeted, individualized behavioral therapies, on top of its potential use for diagnosis and prediction.
To deal with an uncertain and complex world , animals have developed a large repertoire of behavioral heuristics and default strategies that spring into action in unknown situations . Building on this a priori repertoire , animals may find various ways to succeed on a given behavioral task . Therefore , determining the exact behavioral strategy followed during a task may be essential for understanding the cognitive processes involved . Using computational models to analyze behavior , we examined how a genetic variation in a gene that encodes a calcium channel and has been associated with learning deficits influences the way in which animals acted on a task in which a reward was associated with a specific behavior . We found that a knockout of the relevant gene does not lead to a general learning impairment but rather led animals to adopt a behavioral strategy different from the one employed by the control animals . Specifically , knockout animals managed to increase their reward returns by basing their responses more on the previous reward location rather than on reward-indicating stimuli , like the controls did . These findings may prove useful for behavioral therapy in the context of psychiatric disorders associated with this specific gene variation .