Relation between centro-parietal positivity and diffusion model parameters in both perceptual and memory-based decision making

Several studies have suggested that the centro-parietal positivity (CPP), an EEG potential occurring approximately 500 ms post- stimulus, reflects the accumulation of evidence for making a decision. Yet, most previous studies of the CPP focused exclusively on perceptual decisions with very simple stimuli. In this study, we examined how the dynamics of the CPP depended on the type of decision being made, and whether its slope was related to parameters of an accumulator model of decision making. We show initial evidence that memory- and perceptual decisions about carefully-controlled face stimuli exhibit similar dynamics, but offset by a time difference in decision onset. Importantly, the individual-trial slopes of the CPP are related to the accumulator model's drift parameter. These findings help to further understand the role of the CPP across different kinds of decisions

Is There Neural Evidence for an Evidence Accumulation Process in Memory Decisions?

Models of evidence accumulation have been very successful at describing human decision making behavior. Recent years have also seen the first reports of neural correlates of this accumulation process. However, these studies have mostly focused on perceptual decision making tasks, ignoring the role of additional cognitive processes like memory retrieval that are crucial in real-world decisions. In this study, we tried to find a neural signature of evidence accumulation during a recognition memory task. To do this, we applied a method we have successfully used to localize evidence accumulation in scalp EEG during a perceptual decision making task. This time, however, we applied it to intracranial EEG recordings, which provide a much higher spatial resolution. We identified several brain areas where activity ramps up over time, but these neural patterns do not appear to be modulated by behavioral variables such as the amount of available evidence or response time. This casts doubt on the idea of evidence accumulation as a general decision-making mechanism underlying different types of decisions

All Politics is Local: The Renminbi's Prospects as a Future Global Currency

. In this article we describe methods for improving the RWTH German speech recognizer used within the VERBMOBIL project. In particular, we present acceleration methods for the search based on both within-word and across-word phoneme models. We also study incremental methods to reduce the response time of the online speech recognizer. Finally, we present experimental off-line results for the three VERBMOBIL scenarios. We report on word error rates and real-time factors for both speaker independent and speaker dependent recognition. 1 Introduction The goal of the VERBMOBIL project is to develop a speech-to-speech translation system that performs close to real-time. In this system, speech recognition is followed by subsequent VERBMOBIL modules (like syntactic analysis and translation) which depend on the recognition result. Therefore, in this application it is particularly important to keep the recognition time as short as possible. There are VERBMOBIL modules which are capable to work ..

Automatic Question Generation For Decision Tree Based State Tying

Decision tree based state tying uses so-called phonetic questions to assign triphone states to reasonable acoustic models. These phonetic questions are in fact phonetic categories such as vowels, plosives or fricatives. The assumption behind this is that context phonemes which belong to the same phonetic class have a similar influence on the pronunciation of a phoneme. For a new phoneme set, which has to be used e.g. when switching to a different corpus, a phonetic expert is needed to define proper phonetic questions. In this paper a new method is presented which automatically defines good phonetic questions for a phoneme set. This method uses the intermediate clusters from a phoneme clustering algorithm which are reduced to an appropriate number afterwards. Recognition results on the Wall Street Journal data for within-word and acrossword phoneme models show competitive performance of the automatically generated questions with our best handcrafted question set

The Rwth Speech Recognition System And Spoken Document Retrieval

In this paper, we present an overview of the RWTH Aachen large vocabulary continuous speech recognizer. The recognizer is based on continuous density hidden Markov models and a time-synchronous left-to-right beam search strategy. Experimental results on the ARPA Wall Street Journal (WSJ) corpus verify the effects of several system components, namely linear discriminant analysis, vocal tract normalization, pronunciation lexicon and cross-word triphones, on the recognition performance. Finally, the extension of the recognition system towards spoken document retrieval is discussed. 1. INTRODUCTION This paper describes the large vocabulary continuous speech recognizer developed at RWTH Aachen. The recognizer employs a cepstrum front-end that includes linear discriminant analysis (LDA) and vocal tract normalization (VTN). For acoustic modelling, continuous density hidden Markov models along with decision-tree based state tying are used. A time-synchronous left-to-right beam search strateg..

Pronunciation Modelling In The Rwth Large Vocabulary Speech Recognizer

this paper we describe the application of pronunciation variants for our large vocabulary continuous speech recognizer. We will explain how the pronunciation variants were used in training and recognition and give some recognition results on three different corpora. The recognition tests were performed on the Wall Street Journal (WSJ) November 92 development and evaluation corpora (5 000 words), the North American Business (NAB) H1 development corpus (20 000 words) and on the Verbmobil 1996 evaluation corpus (5 000 words). For the WSJ and NAB corpora, a slight improvement in recognition accuracy can be observed, while for the Verbmobil corpus the error rate remains unchange

Validation of two-channel sequencing-by-synthesis for noninvasive prenatal testing of fetal whole and partial chromosome aberrations

OBJECTIVE: To validate Illumina's two-channel NextSeq 500 sequencing system for noninvasive prenatal testing (NIPT) of fetal whole chromosome and partial aberrations. METHODS: A total of 162 plasma samples, previously sequenced for NIPT on a SOLiD 5500xl platform, were sequenced on the NextSeq 500 using 75-bp single-end sequencing, followed by analysis using the WISECONDOR algorithm. RESULTS: For whole chromosome aneuploidy detection, all samples were classified correctly (in total 3x T13, 3x T18, 8x T21 and 145x euploid). Three partial aberrations (36-Mb terminal loss of 5p, 14-Mb gain on 18p and 33-Mb terminal loss of 13q) were also correctly identified. Fetal fractions in 34 male samples sequenced on both the SOLiD 5500xl and NextSeq 500 platform showed no significant difference. To test robustness, two sample sets, containing both euploid and aneuploid samples, were sequenced on different NextSeq 500 machines, revealing identical results. With unchanged laboratory flow, the NIPT turnaround time could be reduced from 15-16 calendar days to 7-8 calendar days, after switching from the SOLiD 5500xl to the NextSeq 500 platform. CONCLUSIONS: The NextSeq 500 platform can be used for NIPT to detect both whole and partial chromosome aberrations. It has fast turnaround times and is suitable for mid-sized laboratories. (c) 2016 John Wiley & Sons, Ltd