Comparing adaptive coding of reward in bipolar I disorder and schizophrenia

Background: Deficits in neural processing of reward have been described in both bipolar disorder (BD) and schizophrenia (SZ), but it remains unclear to what extent these deficits are caused by similar mechanisms. Efficient reward processing relies on adaptive coding which allows representing large input spans by limited neuronal encoding ranges. Deficits in adaptive coding of reward have previously been observed across the SZ spectrum and correlated with total symptom severity. In the present work we sought to establish whether adaptive coding is similarly affected in patients with BD. Methods: 25 patients with BD, 27 patients with SZ and 25 healthy controls performed a variant of the Monetary Incentive Delay task during functional magnetic resonance imaging in two reward range conditions. Results: Adaptive coding was impaired in BD and SZ in the posterior part of the right caudate. In contrast, BD did not show impaired adaptive coding in the anterior caudate and right precentral gyrus/insula, where SZ showed deficits compared to healthy controls. Conclusions: BD patients show adaptive coding deficits, that are similar to those observed in SZ in the right posterior caudate. Adaptive coding in BD appeared more preserved as compared to SZ participants especially in the more anterior part of the right caudate and to a lesser extent also in the right precentral gyrus. Thus, dysfunctional adaptive coding could constitute a fundamental deficit in severe mental illnesses that extends beyond the schizophrenia spectrum.

Adaptive coding of pain prediction error in the anterior insula

Background: To reduce the computational demands of the task of determining values, the brain is thought to engage in adaptive coding, where the sensitivity of some neurons to value is modulated by contextual information. There is good behavioural evidence that pain is coded adaptively, but controversy regarding the underlying neural mechanism. Additionally, there is evidence that reward prediction errors are coded adaptively, but no parallel evidence regarding pain prediction errors. Methods: We tested the hypothesis that pain prediction errors are coded adaptively by scanning 19 healthy adults with fMRI while they performed a cued pain task. Our analysis followed an axiomatic approach. Results: We found that the left anterior insula was the only region which was sensitive both to predicted pain magnitudes and the unexpectedness of pain delivery, but not to the magnitude of delivered pain. Conclusions: This pattern suggests that the left anterior insula is part of a neural mechanism that serves the adaptive prediction error of pain.

ACO:lossless quality score compression based on adaptive coding order

Background: With the rapid development of high-throughput sequencing technology, the cost of whole genome sequencing drops rapidly, which leads to an exponential growth of genome data. Although the compression of DNA bases has achieved significant improvement in recent years, the compression of quality score is still challenging.Results: In this paper, by reinvestigating the inherent correlations between the quality score and the sequencing process, we propose a novel lossless quality score compressor based on adaptive coding order (ACO). The main objective of ACO is to traverse the quality score adaptively in the most correlative trajectory according to the sequencing process. By cooperating with the adaptive arithmetic coding and context modeling, ACO achieves the state-of-the-art quality score compression performances with moderate complexity.Conclusions: The competence enables ACO to serve as a candidate tool for quality score compression, ACO has been employed by AVS(Audio Video coding Standard Workgroup of China) and is freely available at https://github.com/Yoniming/code.

Two sides of the same coin: Beneficial and detrimental consequences of range adaptation in human reinforcement learning

Evidence suggests that economic values are rescaled as a function of the range of the available options. Although locally adaptive, range adaptation has been shown to lead to suboptimal choices, particularly notable in reinforcement learning (RL) situations when options are extrapolated from their original context to a new one. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counterintuitive prediction: Decreasing task difficulty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of participants performing variants of an RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing task difficulty. Last, we propose a range-adapting model and show that it is able to parsimoniously capture all the behavioral results.