The Morris Water Maze is a widely used task in studies of spatial learning with rodents. Classical performance measures of animals in the Morris Water Maze include the escape latency, and the cumulative distance to the platform. Other methods focus on classifying trajectory patterns to stereotypical classes representing different animal strategies. However, these approaches typically consider trajectories as a whole, and as a consequence they assign one full trajectory to one class, whereas animals often switch between these strategies, and their corresponding classes, within a single trial. To this end, we take a different approach: we look for segments of diverse animal behaviour within one trial and employ a semi-automated classification method for identifying the various strategies exhibited by the animals within a trial. Our method allows us to reveal significant and systematic differences in the exploration strategies of two animal groups (stressed, non-stressed), that would be unobserved by earlier methods.

Individual behavioral performance during learning is known to be affected by modulatory factors, such as stress and motivation, and by genetic predispositions that influence sensitivity to these factors. Despite numerous studies, no integrative framework is available that could predict how a given animal would perform a certain learning task in a realistic situation. We found that a simple reinforcement learning model can predict mouse behavior in a hole-box conditioning task if model metaparameters are dynamically controlled on the basis of the mouse's genotype and phenotype, stress conditions, recent performance feedback and pharmacological manipulations of adrenergic alpha-2 receptors. We find that stress and motivation affect behavioral performance by altering the exploration-exploitation balance in a genotype-dependent manner. Our results also provide computational insights into how an inverted U-shape relation between stress/arousal/norepinephrine levels and behavioral performance could be explained through changes in task performance accuracy and future reward discounting.Animal behavior is guided by rewards that can be received in different situations and by modulatory factors such as stress and motivation. Acute stress can have positive or negative effects on learning and memory that depend on stressor properties (timing, duration and relation with the task) and on the predispositions of stressed individuals [1][2][3] . These effects are thought to be mediated through the modulation of synaptic plasticity by stress hormones and neuromodulators, such as glucocorticoids and norepinephrine [4][5][6][7] . However, their role in high-level processes such as learning, action selection and future reward discounting is not well understood. In addition to stress, genotype 8 , affective traits 9 , motivation 10 and recent performance feedback 11 also influence individual performance, but it may be difficult and inefficient to explicitly model each factor to accurately predict animal behavior.A number of models have related neuromodulatory systems to cognitive processes and to statistical quantities characterizing the environment 12,13 . Although such models provide insights into potential mechanisms, alone they are often unable to accurately predict animal behavior in a realistic situation as a result of the diversity of the modulatory factors affecting it. Here, we propose a method that can, in principle, quantify the influence of arbitrary modulatory factors on behavior as control parameters of a general behavioral model and that is exemplified here for the case of stress and genetic strain in mice.In modeling reward-based behavioral learning, approaches based on the theory of reinforcement learning 14 have been the most successful. They have been applied to explain experimental data in animal conditioning 10 , human decision-making 15 and addiction 16 . However, the modulatory role of stress and affective traits has not yet been considered. In reinforcement learning, modeled animals occupy different states cor...

Visual backward masking is a versatile tool for understanding principles and limitations of visual information processing in the human brain. However, the mechanisms underlying masking are still poorly understood. In the current contribution, the authors show that a structurally simple mathematical model can explain many spatial and temporal effects in visual masking, such as spatial layout effects on pattern masking and B-type masking. Specifically, the authors show that lateral excitation and inhibition on different length scales, in combination with the typical time scales, are capable of producing a rich, dynamic behavior that explains this multitude of masking phenomena in a single, biophysically motivated model.

Substantial evidence implicates the nucleus accumbens in motivated performance, but very little is known about the neurochemical underpinnings of individual differences in motivation. Here, we applied 1H magnetic resonance spectroscopy (1H-MRS) at ultra-high-field in the nucleus accumbens and inquired whether levels of glutamate (Glu), glutamine (Gln), GABA or their ratios predict interindividual differences in effort-based motivated task performance. Given the incentive value of social competition, we also examined differences in performance under self-motivated or competition settings. Our results indicate that higher accumbal Gln-to-Glu ratio predicts better overall performance and reduced effort perception. As performance is the outcome of multiple cognitive, motor and physiological processes, we applied computational modeling to estimate best-fitting individual parameters related to specific processes modeled with utility, effort and performance functions. This model-based analysis revealed that accumbal Gln-to-Glu ratio specifically relates to stamina; i.e., the capacity to maintain performance over long periods. It also indicated that competition boosts performance from task onset, particularly for low Gln-to-Glu individuals. In conclusion, our findings provide novel insights implicating accumbal Gln and Glu balance on the prediction of specific computational components of motivated performance. This approach and findings can help developing therapeutic strategies based on targeting metabolism to ameliorate deficits in effort engagement.