Transitional and steady-state choice behavior under an adjusting-delay schedule

Twelve rats made repeated choices on an adjusting-delay schedule between a smaller reinforcer (A) that was delivered immediately after a response and a larger reinforcer (B) that was delivered after a delay which increased or decreased by 20% depending on the subject's choices in successive blocks of trials. In two phases of the experiment (100 sessions and 40 sessions), reinforcer sizes were selected which enabled theoretical parameters expressing the rate of delay discounting and sensitivity to reinforcer size to be estimated from the ratio of the indifference delays obtained in the two phases. Indifference delays, calculated from adjusting delays in the last 10 sessions of each phase, were shorter when the sizes of A and B were 14 and 25 μl of a 0.6 M sucrose solution than when they were 25 and 100 μl of the same solution. The ratio of the indifference delays was significantly smaller than that predicted on the basis of an assumed linear relation between reinforcer size and instantaneous reinforcer value, consistent with a previous proposal that this relation may be hyperbolic in form. Estimates of the rate of delay discounting based on the ratio of the two indifference delays (mean, 0.08 s(-1)) were similar to values obtained previously using different intertemporal choice protocols. Estimates of the size-sensitivity parameter (mean 113 μl) were similar to estimates recently derived from performance on progressive-ratio schedules. In both phases of the experiment, adjusting delays in successive blocks of trials were analyzed using the Fourier transform. The power spectrum obtained from individual rats had a dominant frequency that corresponded to a period of oscillation of the adjusting delay between 30 and 100 trial blocks (mean, 78). Power in the dominant frequency band was highest in the early sessions of the first phase and declined with extended training. It is suggested that this experimental protocol may have utility in neurobehavioral studies of intertemporal choice.     

傅伟勋堪当现代新儒家论

傅伟勋一直被看作现代新儒家之外的儒家批评者,文章则从三个方面证明他完全有资格归入现代新儒家阵营。首先论证他是一个具有高度文化使命感的学者,在中国现代三大主流思潮(自由主义、文化保守主义、马克思主义)中,他必然要选择文化保守主义,从而走向现代新儒家。其次就他的四个学术思想倾向来论述他只能归属现代新儒家。最后论述他具有第三代新儒家共通的思想特征,即:①推崇儒学,但不将儒学价值绝对化;②虽不认同马克思主义,却能客观地评价马克思主义;③坚持多元价值观,虽以中国文化为本位,但从不拒斥西方文化,虽强调儒家精神的现代意义,但决不轻率断言认同儒学乃人类未来文化发展的惟一方向。     

Brainwave classification without the help of limb movement and any stimulus for character-writing application

Recently, Brain-Computer Interfaces (BCIs) have been extensively popular for employing Electroencephalography (EEG) signals to control devices with different applications. The use of BCIs currently involves for lots of applications to help the disabilities who cannot communicate with other people, as it is an alternative way for communication by passing the need of speech. Although the applications to spell the character with BCI systems (e.g., P300-speller, SSVEP-speller, Hex-O-spell) have been already developed, but these techniques are not flexible in the real scenarios because they require the stimulus all the time or stopping the activity to focus on the limb movement in order to provide the accuracy of brain responses. In this paper, the feasibility of brainwave classification for the applications of character-writing by considering only the EEG signals without the need of stimulus unlike the literature is newly introduced. This paper adopts a classification technique named Artificial Neural Network (ANN) and focuses on two different characters; straight line and circle. From the experimental results, the suitable position of electrodes are the pair of electrodes (F3 and F4) at the frontal lobe, which provide the best result as compared to other areas due to its important role in perception, maintenance and revival of the information. The experimental results indicate that the classification accuracy of the proposed technique is about 70%, which in turn leads to a significant achievement for the development of character-writing applications.     

Gender differences in synchrony: Females in sync during unstructured dyadic conversation

Although previous studies have reported that individual prosocial traits generally enhance synchrony, gender as a social factor was not the subject of focus. The present study examined gender differences in synchrony by conducting three experiments on dyadic unstructured conversations with same-gender strangers. Synchrony was assessed by calculating the cross-wavelet coherence and determining the relative phase. As expected, through the three experiments, the female dyads showed a higher degree of synchrony, compared to the male dyads. Moreover, the conversation type (i.e., structured monologue vs. unstructured dialogue) did not moderate the gender effect (Experiment 2), while synchrony in the female dyads was influenced by the preceding conversation (Experiment 3). In contrast, through the three experiments, the role of the relative phase was unclear. The findings indicate that the social factor, gender, has an impact on synchrony, and that interpersonal sensitivity contributes to synchrony.     

Classification of gait muscle activation patterns according to knee injury history using a support vector machine approach

Abnormal muscle activation patterns during gait following knee injury that persist past the acute injury and rehabilitation phase (>three years) are not well characterized but may be related to post-traumatic knee osteoarthritis. The aim was to characterize the abnormal muscle activity from electromyograms of five leg muscles that were recorded during treadmill walking for young adults with and without a previous knee injury 3–12 years prior. The wavelet transformed and amplitude normalized electromyograms yielded intensity patterns that reflect the muscle activity of these muscles resolved in time and frequency. Patterns belonging to the affected or unaffected leg in previously injured participants and patterns belonging to a previously injured vs. uninjured participant were grouped and then classified using a principal component analysis followed by a support vector machine. A leave-one-out cross-validation was used to test the model significance and generalization. The results showed that trained classifiers could successfully recognize whether muscle activation patterns belonged to the affected or unaffected leg of previously injured individuals. Classification rates of 83% were obtained for all subjects, 100% for females only, indicating sex-specific knee injury effects. In contrast, it was not possible to discriminate between patterns belonging to the previously injured legs or dominant legs of control subjects. For females, the injured leg showed a stronger muscle activity for hamstring muscles and a lower activity for the vastus lateralis. In conclusion, systematic knee injury effects on the neuromuscular control of the knee during gait were present 3–12 years later.     

Kernel smoothing approaches to nonparametric item characteristic curve estimation

The option characteristic curve, the relation between ability and probability of choosing a particular option for a test item, can be estimated by nonparametric smoothing techniques. What is smoothed is the relation between some function of estimated examinee ability rankings and the binary variable indicating whether or not the option was chosen. This paper explores the use of kernel smoothing, which is particularly well suited to this application. Examples show that, with some help from the fast Fourier transform, estimates can be computed about 500 times as rapidly as when using commonly used parametric approaches such as maximum marginal likelihood estimation using the three-parameter logistic distribution. Simulations suggest that there is no loss of efficiency even when the population curves are three-parameter logistic. The approach lends itself to several interesting extensions.The author wishes to acknowledge the support of the National Sciences and Engineering Research Council of Canada through grant A320 and the support of Educational Testing Service during his leave there.     

Monotonic weighted power transformations to additivity

A class of monotonic transformations which generalize the power transformation is fit to the independent and dependent variables in multiple regression so that the resulting additive relationship is optimized. This is achieved by minimizing a quadratic fitting criterion with linear inequality constraints on the parameters. A quadratic programming technique which works reliably and quickly in this application is outlined. Some examples of the analysis of artificial and real data are offered.This work was supported by National Research Council of Canada Grant APA 320 to the author.     

Stability of heartbeat interval distributions in chronic high altitude hypoxia

Recent studies of nonlinear dynamics of the long-term variability of heart rate have identified nontrivial long-range correlations and scale-invariant power-law characteristics (1/f noise) that were remarkably consistent between individuals and were unrelated to external or environmental stimuli (Meyer et al., 1998a). The present analysis of complex nonstationary heartbeat patterns is based on the sequential application of the wavelet transform for elimination of local polynomial nonstationary behavior and an analytic signal approach by use of the Hilbert transform (Cumulative Variation Amplitude Analysis). The effects of chronic high altitude hypoxia on the distributions and scaling functions of cardiac intervals over 24 hr epochs and 4 hr day/nighttime subepochs were determined from serial heartbeat interval time series of digitized 24 hr ambulatory ECGs recorded in 9 healthy subjects (mean age 34 yrs) at sea level and during a sojourn at high altitude (5,050 m) for 34 days (Ev-K2-CNR Pyramid Laboratory, Sagarmatha National Park, Nepal). The results suggest that there exists a hidden, potentially universal, common structure in the heterogeneous time series. A common scaling function with a stable Gamma distribution defines the probability density of the amplitudes of the fluctuations in the heartbeat interval time series of individual subjects. The appropriately rescaled distributions of normal subjects at sea level demonstrated stable Gamma scaling consistent with a single scaled plot (data collapse). Longitudinal assessment of the rescaled distributions of the 24 hr recordings of individual subjects showed that the stability of the distributions was unaffected by the subject’s exposure to a hypobaric (hypoxic) environment. The rescaled distributions of 4 hr subepochs showed similar scaling behavior with a stable Gamma distribution indicating that the common structure was unequivocally applicable to both day and night phases and, furthermore, did not undergo systematic changes in response to high altitude. In contrast, a single function stable over a wide range of time scales was not observed in patients with congestive heart failure or patients after cardiac transplantation. The functional form of the scaling in normal subjects would seem to be attributable to the underlying nonlinear dynamics of cardiac control. The results suggest that the observed Gamma scaling of the distributions in healthy subjects constitutes an intrinsic dynamical property of normal heart function that would not undergo early readjustment or late acclimatization to extrinsic environmental physiological stress, e.g., chronic hypoxia.     

Resting EEG in alpha and beta bands predicts individual differences in attentional blink magnitude

Accuracy for a second target (T2) is reduced when it is presented within 500 ms of a first target (T1) in a rapid serial visual presentation (RSVP) - an attentional blink (AB). There are reliable individual differences in the magnitude of the AB. Recent evidence has shown that the attentional approach that an individual typically adopts during a task or in anticipation of a task, as indicated by various measures, predicts individual differences in the AB deficit. It has yet to be observed whether indices of attentional approach when not engaged in a goal-directed task are also relevant to individual differences in the AB. The current studies investigated individual differences in the AB by examining their relationship with attention at rest using quantitative measures of EEG. Greater levels of alpha at rest were associated with larger AB magnitudes, where greater levels of beta at rest were associated with smaller AB magnitudes. Furthermore, individuals with more beta than alpha demonstrated a smaller AB effect than individuals with more alpha than beta. Our results suggest that greater attentional engagement at rest, when not engaged in a goal-directed task, is associated with smaller AB magnitudes.     

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

ObjectivesLiver cancer is one of the leading cause of death in all over the world. Detecting the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided diagnosis (CAD) is used in decision making process for accurate detection for appropriate therapy. Therefore the main objective of this work is to detect the liver cancer accurately using automated method.MethodsIn this work, we have proposed a new system called as watershed Gaussian based deep learning (WGDL) technique for effective delineate the cancer lesion in computed tomography (CT) images of the liver. A total of 225 images were used in this work to develop the proposed model. Initially, the liver was separated using marker controlled watershed segmentation process and finally the cancer affected lesion was segmented using the Gaussian mixture model (GMM) algorithm. After tumor segmentation, various texture features were extracted from the segmented region. These segmented features were fed to deep neural network (DNN) classifier for automated classification of three types of liver cancer i.e. hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET).ResultsWe have achieved a classification accuracy of 99.38%, Jaccard index of 98.18%, at 200 epochs using DNN classifier with a negligible validation loss of 0.062 during the classification process.ConclusionsOur developed system is ready to be tested with huge database and can aid the radiologist in detecting the liver cancer using CT images.