Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.     

Identification, sequencing and mutagenesis of the gene for a d-carbamoylase from Agrobacterium radiobacter

Abstract A clone positive for d-carbamoylase activity (2.7 kb Hin dIII- Bam H1 DNA fragment) was obtained by screening a genomic library of Agrobacterium radiobacter in Escherichia coli . This DNA fragment contains an open reading frame of 912 bp which is predicted to encode a peptide of 304 amino acids with a calculated molecular mass of 34247 Da. The d-carbamoylase gene. named cauA , was placed under the control of T7 RNA-dependent promoter and expressed in E. coli BL21 (DE3). After induction with isopropyl-thio-β-d-galactopyranoside, the synthesis of d-carbamoylase in E. coli reached about 40% of the total protein. The expressed protein was shown to possess a molecular mass, on SDS-PAGE, of 36 kDa and showed an enhanced allowed us to establish that a Pro¹⁴→Leu¹⁴ exchange leads to an inactive enzyme species, while a Cys²⁷⁹→Ser²⁷⁹ exchange did not impair the functional properties of the enxyme.