Neural correlates of implicit knowledge about statistical regularities

In this study, we examined the neural correlates of implicit knowledge about statistical regularities of temporal order and item chunks using functional magnetic resonance imaging (fMRI). In a familiarization scan, participants viewed a stream of scenes consisting of structured (i.e., three scenes were always presented in the same order) and random triplets. In the subsequent test scan, participants were required to detect a target scene. Test sequences included both forward order of scenes presented during the familiarization scan and backward order of scenes (i.e., reverse order of forward scenes). Behavioral results showed a learning effect of temporal order in the forward condition and scene chunks in the backward condition. fMRI data from the familiarization scan showed the difference of activations between the structured and random blocks in the left posterior cingulate cortex including the retrosplenial cortex. More important, in the test scan, we observed brain activities in the left parietal lobe when participants detected target scenes on temporal order information. In contrast, the left precuneus activated when participants detected target scenes based on scene chunks. Our findings help clarify the brain mechanisms of implicit knowledge about acquired regularities

Analyzing street crimes in Kobe city using PRISM

Purpose: In a previous research, the authors proposed a security information service, called Personalized Real-time Information with Security Map (PRISM), which personalizes the incident information based on living area of individual users. The purpose of this paper is to extend PRISM to conduct sophisticated analysis of street crimes. The extended features enable to look back on past incident information and perform statistical analysis. Design/methodology/approach: To analyze street crimes around living area in more detail, the authors add three new features to PRISM: showing a past heat map, showing a heat map focused on specified type of incidents and showing statistics of incidents for every type. Using these features, the authors visualize the dynamic transition of street crimes in a specific area and the whole region within Kobe city. They also compare different districts by statistics of street crimes. Findings: Dynamical visualization clarifies when, where and what kind of incident occurs frequently. Most incidents occurred along three train lines in Kobe city. Wild boars are only witnessed in a certain region. Statistics shows that the characteristics of street crimes is completely different depending on living area. Originality/value: Previously, many studies have been conducted to clarify factors relevant to street crimes. However, these previous studies mainly focus on interesting regions as a whole, but do not consider individual’s living area. In this paper, the authors analyze street crimes according to users’ living area using personalized security information service PRISM

Visual statistical learning produces implicit and explicit knowledge about temporal order information and scene chunks: Evidence from direct and indirect measures

We examined whether visual statistical learning (VSL) produced implicit and/or explicit knowledge about temporal order information and scene chunks, using a rapid serial visual presentation target detection task and a two-alternative forced-choice (2AFC) familiarity test as indirect and direct measures of VSL, respectively. In the familiarization phase, participants viewed a visual stream of natural scenes consisting of four triplets (i.e., three images that always appeared in the same order). In the subsequent target detection task, participants were required to detect target items embedded in a stream of 12 images or 12 words representing each natural scene. In the final 2AFC familiarity test, participants observed two test sequences (statistically related triplets vs. unrelated foils) and decided whether the first or second sequence was more familiar based on the familiarization phase. In both test phases, we included the same (forward) and reverse (backward) order of scenes as presented during the familiarization phase to examine whether the expression of VSL was based on temporal order of scenes or scene chunks. The results of the target detection task showed a learning effect for both temporal order in the forward condition and chunks in the backward condition, irrespective of whether stimuli were images or words; in contrast, we did not observe a learning effect in the backward condition for scene images in the familiarity test. Our findings are compatible with a learning mechanism that has both implicit and explicit components based on temporal order information and scene chunks

An improved mu-law proportionate NLMS algorithm

In this paper, we propose an algorithm to improve the perfor-mance of the mu-law PNLMS algorithm (MPNLMS) for non-sparse impulse responses. Although the existing MPNLMS algorithm was recently proposed to achieve optimal propor-tionate step size for both large and small tap weights, it con-verges even slower than conventional NLMS algorithm for dispersive channels. The proposed approach adaptively esti-mates the sparsity of the impulse response to be identified. Then the estimation of this sparsity is incorporated into the IPNLMS algorithm to accordingly adjust its parameters. Sim-ulation results verify the effectiveness of the proposed algo-rithm. Index Terms — Adaptive signal processing, adaptive fil-ters, NLMS algorithm, PNLMS algorithm, MPNLMS algo-rithm, acoustic signal processing 1