Understanding the Neural Bases of Implicit and Statistical Learning

© 2019 Cognitive Science Society, Inc. Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of “implicit statistical learning.” However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit learning and statistical learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining learning according to experimental paradigm, with “implicit learning” reserved as a specific term to denote learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and statistical learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities

A coded aperture imaging system optimized for hard X-ray and gamma ray astronomy

A coded aperture imaging system was designed for the Gamma-Ray imaging spectrometer (GRIS). The system is optimized for imaging 511 keV positron-annihilation photons. For a galactic center 511-keV source strength of 0.001 sq/s, the source location accuracy is expected to be + or - 0.2 deg

Barrier-properties of Nup98 FG phases ruled by FG motif identity and inter-FG spacer length

Nup98 FG repeat domains comprise hydrophobic FG motifs linked through uncharged spacers. FG motifs capture nuclear transport receptors (NTRs) during nuclear pore complex (NPC) passage, confer inter-repeat cohesion, and condense the domains into a selective phase with NPC-typical barrier properties. We found that shortening inter-FG spacers enhances cohesion, increases phase density, and tightens such barrier – consistent with a sieve-like phase. Phase separation tolerated mutations of Nup98-typical GLFG motifs, provided the domain-hydrophobicity remained preserved. NTR-entry, however, was sensitive to (certain) deviations from canonical FG motifs, suggesting a co-evolutionary adaptation. Unexpectedly, we found that arginines promote efficient FG-phase entry also by means other than cation-π interactions. Although incompatible with NTR·cargo complexes, a YG phase displayed remarkable transport selectivity, particularly for evolved GFPNTR-variants. GLFG to FSFG mutations made the FG phase hypercohesive, precluding NTR-entry. Longer spacers relieved this hypercohesive phenotype. The antagonism between cohesion and NTR·FG interactions appears thus key to transport selectivity

AGL StimSelect: Software for automated selection of stimuli for artificial grammar learning

Artificial Grammar Learning (AGL) is an experimental paradigm that has been used extensively in cognitive research for many years to study implicit learning, associative learning, and generalization based either on similarity or rules. Without computer assistance it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or non-grammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated software for selecting AGL stimuli. The software allows users to specify a grammar of interest, and to manipulate characteristics of training and test sequences, and their relationship to each other. The user thus has direct control over stimulus features that may influence learning and generalization in AGL tasks. The software enables researchers to develop AGL designs that would not be feasible without automatic stimulus selection. It is implemented in Matlab