Compared with reward seeking, punishment avoidance learning is less clearly understood at both the computational and neurobiological levels. Here we demonstrate, using computational modelling and fMRI in humans, that learning option values in a relative—context-dependent—scale offers a simple computational solution for avoidance learning. The context (or state) value sets the reference point to which an outcome should be compared before updating the option value. Consequently, in contexts with an overall negative expected value, successful punishment avoidance acquires a positive value, thus reinforcing the response. As revealed by post-learning assessment of options values, contextual influences are enhanced when subjects are informed about the result of the forgone alternative (counterfactual information). This is mirrored at the neural level by a shift in negative outcome encoding from the anterior insula to the ventral striatum, suggesting that value contextualization also limits the need to mobilize an opponent punishment learning system.

Simultaneous recordings of many single neurons reveals unique insights into network processing spanning the timescale from single spikes to global oscillations. Neurons dynamically self-organize in subgroups of coactivated elements referred to as cell assemblies. Furthermore, these cell assemblies are reactivated, or replayed, preferentially during subsequent rest or sleep episodes, a proposed mechanism for memory trace consolidation. Here we employ Principal Component Analysis to isolate such patterns of neural activity. In addition, a measure is developed to quantify the similarity of instantaneous activity with a template pattern, and we derive theoretical distributions for the null hypothesis of no correlation between spike trains, allowing one to evaluate the statistical significance of instantaneous coactivations. Hence, when applied in an epoch different from the one where the patterns were identified, (e.g. subsequent sleep) this measure allows to identify times and intensities of reactivation. The distribution of this measure provides information on the dynamics of reactivation events: in sleep these occur as transients rather than as a continuous process.

To explain the high level of flexibility in primate decision-making, theoretical models often invoke reinforcement-based mechanisms, performance monitoring functions, and core neural features within frontal cortical regions. However, the underlying biological mechanisms remain unknown. In recent models, part of the regulation of behavioral control is based on meta-learning principles, for example, driving exploratory actions by varying a meta-parameter, the inverse temperature, which regulates the contrast between competing action probabilities. Here we investigate how complementary processes between lateral prefrontal cortex (LPFC) and dorsal anterior cingulate cortex (dACC) implement decision regulation during exploratory and exploitative behaviors. Model-based analyses of unit activity recorded in these 2 areas in monkeys first revealed that adaptation of the decision function is reflected in a covariation between LPFC neural activity and the control level estimated from the animal's behavior. Second, dACC more prominently encoded a reflection of outcome uncertainty useful for control regulation based on task monitoring. Model-based analyses also revealed higher information integration before feedback in LPFC, and after feedback in dACC. Overall the data support a role of dACC in integrating reinforcement-based information to regulate decision functions in LPFC. Our results thus provide biological evidence on how prefrontal cortical subregions may cooperate to regulate decision-making.

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.