Lexical effects on compensation for coarticulation: the ghost of Christmash past

The question of when and how bottom‐up input is integrated with top‐down knowledge has been debated extensively within cognition and perception, and particularly within language processing. A long running debate about the architecture of the spoken‐word recognition system has centered on the locus of lexical effects on phonemic processing: does lexical knowledge influence phoneme perception through feedback, or post‐perceptually in a purely feedforward system? Elman and McClelland (1988) reported that lexically restored ambiguous phonemes influenced the perception of the following phoneme, supporting models with feedback from lexical to phonemic representations. Subsequently, several authors have argued that these results can be fully accounted for by diphone transitional probabilities in a feedforward system (Cairns et al., 1995; Pitt & McQueen, 1998). We report results strongly favoring the original lexical feedback explanation: lexical effects were present even when transitional probability biases were opposite to those of lexical biases.     

Powered-Two-Wheelers kinematic characteristics and interactions during filtering and overtaking in urban arterials

The present paper focuses on the Powered-Two-Wheelers (PTWs) kinematic characteristics and their interactions with the rest of traffic in urban arterials. The factors that may affect the likelihood of PTW drivers to accept critical spacing during filtering and overtaking are also investigated using trajectory data collected from video recordings. The distributional characteristics of the PTW kinematic parameters showed that the patterns of filtering and overtaking have several differences. Further results using Logit models show that PTW speed difference with the rest of traffic, spacing, the existence of heavy vehicles and the occurrence of platoon of moving PTWs (in which the leader is the reference PTW) are significant factors related to the probability of driving in critical spaces through traffic. The likelihood of accepting critical lateral distance from the vehicle being overtaken may be related to the adjacent lane spacing, the speed difference and the existence of a platoon of PTWs. A comparative study between Logit models and equivalent structures of neural networks showed that, in the specific application, neural networks were found to perform better than the Logit models in terms of the model’s discrimination power.     

Research Misconduct in the Age of Open Science: The Case of STAP Stem Cells

In early 2014, Dr Haruko Obokata and her co-authors caused a global media storm by publishing two scientific papers in Nature on stem cells. The papers proposed a surprisingly simple new method called stimulus-triggered acquisition of pluripotency (STAP) to generate pluripotent stem cells for research purposes in a fast and inexpensive way. To the dismay of many, both papers were retracted within six months of their release due to data fabrication and falsification. The rise and fall of the STAP research illustrates the impacts of open science practices of quality control and validation on new scientific claims. In the STAP case, collective action generated through two online forums—the Knoepfler Lab Stem Cell Blog and PubPeer—exposed not only data manipulation in the two papers but also the irreproducibility of the purported new cellular reprogramming method. The case brings to the fore the tentative possibilities as well as potential pitfalls of ‘accelerated virtual witnessing’ through crowdsourced post-publication peer reviews that value accessibility and inclusiveness. Indeed, it calls for empirically grounded discussion on the ongoing reconfiguration of the shared system of values and norms or the moral economy of science.     

Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks

Globally, motor vehicle crashes account for over 1.2 million fatalities per year and are the leading cause of death for people aged 15–29 years. The majority of road crashes are caused by human error, with risk heightened among young and novice drivers learning to negotiate the complexities of the road environment. Direct feedback has been shown to have a positive impact on driving behaviour. Methods that could detect behavioural changes and therefore, positively reinforce safer driving during the early stages of driver licensing could have considerable road safety benefit. A new methodology is presented combining in-vehicle telematics technology, providing measurements forming a personalised driver profile, with neural networks to identify changes in driving behaviour. Using Long Short-Term Memory (LSTM) recurrent neural networks, individual drivers are identified based on their pattern of acceleration, deceleration and exceeding the speed limit. After model calibration, new, real-time data of the driver is supplied to the LSTM and, by monitoring prediction performance, one can assess whether a (positive or negative) change in driving behaviour is occurring over time. The paper highlights that the approach is robust to different neural network structures, data selections, calibration settings, and methodologies to select benchmarks for safe and unsafe driving. Presented case studies show additional model applications for investigating changes in driving behaviour among individuals following or during specific events (e.g., receipt of insurance renewal letters) and time periods (e.g., driving during holiday periods). The application of the presented methodology shows potential to form the basis of timely provision of direct feedback to drivers by telematics-based insurers. Such feedback may prevent internalisation of new, risky driving habits contributing to crash risk, potentially reducing deaths and injuries among young drivers as a result.     

Cell phone usage relational regulation strategies of older South Africans

This study explored older South Africans’ relational regulation strategies in using their cell phones to address their social goals and psychosocial needs. Nineteen older South Africans (17 female; 2 male), aged 60 and older, who had access to a cell phone and represented different socio-economic levels based on the Living Standard Measurement (LSM) scale, participated in the Mmogo-method®, a visual data-collection method. Thematic and visual analysis revealed that the subjective assessment of cell phone users’ competence determined the type of relational regulation strategy they apply. Findings revealed older persons to adopt three relational regulation strategies in their cell phone use: (i) postponement of need satisfaction; (ii) asking directly; or (iii) negotiating for needs satisfaction. The findings might facilitate programmes that promote cell phone use in older persons to address their social needs and promote healthy intergenerational interactions.     

社会交流中人际神经同步的认知机制

近年来,许多研究者开始关注社会交流中的人际神经同步机制,并将人际神经同步作为研究社会交流的一个神经指标,这对于揭示社会交流的本质和规律具有重要意义。本文从心理理论和镜像神经系统的角度,分析社会交流中神经同步的认知机制及其影响因素。未来的研究应关注这两套机制是否因交流目的、对象、形式或内容的不同,而在不同的脑区表现出神经同步,进而引发了不同认知机制的争议;以及这两套机制各自或协同工作适用的情景和任务。     

Neuroevolution-based autonomous robot navigation: A comparative study

The field of neuroevolution has achieved much attention in recent years from both academia and industry. Numerous papers have reported its successful applications in different fields ranging from medical domain to autonomous systems. However, it is not clear which evolutionary optimization techniques lead to the best results. In this paper, multilayer perceptron (MLP) neural networks (NNs) are trained and optimized using four advanced bio-inspired evolutionary algorithms (EA). The algorithms are Multi-Verse Optimizer (MVO), Moth-flame optimization (MFO), Cuckoo Search (CS) and Particle Swarm Optimization (PSO). Each algorithm is equipped with two operators: evolutionary population dynamics and mutation, which impact on exploration and exploitation. Optimized MLPs are then used for the navigation of an autonomous robot. Accuracy and area under the curve metrics are used for the evaluation and comparison metrics. Moreover, two well-regarded gradient descent algorithms including Back propagation (BP) and Levenberg Marquardt (LM) are utilized to validate the results obtained by evolutionary-based MLP trainers. It is observed that MLPs developed using MFO are the most robust ones among MLPs trained using other evolutionary and gradient descent algorithms.     

On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.