Comparison of brain mechanisms underlying the processing of Chinese characters and pseudo-characters: an event-related potential study

Most Chinese characters are composed of a semantic radical on the left and a phonetic radical on the right. The semantic radical provides the semantic information; the phonetic radical provides information concerning the pronunciation of the whole character. The pseudo‐characters in the study consisted of different sub‐lexical parts of real Chinese characters and consequently they also had the semantic radical and the phonetic radical. But they were not readable and had no actual meaning. In order to investigate the spatiotemporal cortical activation patterns underlying the orthographic, phonological and semantic processing of Chinese characters, we used event‐related brain potentials (ERPs) to explore the processing of Chinese characters and pseudo‐characters when 14 healthy Chinese college students viewed the characters passively. Results showed that both Chinese characters and pseudo‐characters elicited an evident negative potential peaking around 120 ms (N120), which appeared to reflect initial orthographic distinction and evaluation. Then, Chinese pseudo‐characters elicited a more positive ERP deflection (P220) than did Chinese characters 200–250 ms after onset of the stimuli. It was similar to the recognition potential (RP) and might reflect the integration processes of phonological and semantic processing on the basis of early orthographic information. Dipole source analysis of the difference wave (pseudo‐characters minus characters) indicated that a generator localized in the left temporal‐occipital junction contributed to this effect, which was possibly related to phonological and perceptual–semantic information integration. Between 350–450 ms, a greater negativity (N360) in pseudo‐characters as compared to characters was found over midline fronto‐central scalp regions. Dipole analysis localized the generator of N360 in the right parahippocampal cortex. Therefore, the N360 might be an N400 component and reflect the higher‐level semantic activation on the basis of orthographic, phonological and perceptual–semantic processing.     

Estimation and visualization of confusability matrices from adaptive measurement data

We present a simple but effective method based on Luce’s choice axiom [Luce, R.D. (1959). Individual choice behavior: A theoretical analysis. New York: John Wiley & Sons] for consistent estimation of the pairwise confusabilities of items in a multiple-choice recognition task with arbitrarily chosen choice-sets. The method combines the exact (non-asymptotic) Bayesian way of assessing uncertainty with the unbiasedness emphasized in the classical frequentist approach.We apply the method to data collected using an adaptive computer game designed for prevention of reading disability. A player’s estimated confusability of phonemes (or more accurately, phoneme-grapheme connections) and larger units of language is visualized in an easily understood way with color cues and explicit indication of the accuracy of the estimates. Visualization of learning-related changes in the player’s performance is considered.The empirical validity of the choice axiom is evaluated using the game data itself. The axiom appears to hold reasonably well although a small systematic violation is observable for the smallest choice-set sizes.     

Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task

The purpose of the popular Iowa gambling task is to study decision making deficits in clinical populations by mimicking real-life decision making in an experimental context. Busemeyer and Stout [Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14, 253-262] proposed an “Expectancy Valence” reinforcement learning model that estimates three latent components which are assumed to jointly determine choice behavior in the Iowa gambling task: weighing of wins versus losses, memory for past payoffs, and response consistency. In this article we explore the statistical properties of the Expectancy Valence model. We first demonstrate the difficulty of applying the model on the level of a single participant, we then propose and implement a Bayesian hierarchical estimation procedure to coherently combine information from different participants, and we finally apply the Bayesian estimation procedure to data from an experiment designed to provide a test of specific influence.     

Distribution-free tests of stochastic dominance for small samples

One variable is said to “stochastically dominate” another if the probability of observations smaller than x is greater for one variable than the other, for all x. Inferring stochastic dominance from data samples is important for many applications of econometrics and experimental psychology, but little is known about the performance of existing inferential methods. Through simulation, we show that three of the most widely used inferential methods are inadequate for use in small samples of the size commonly encountered in many applications (up to 400 observations from each distribution). We develop two new inferential methods that perform very well in a limited, but practically important, case where the two variables are guaranteed not to be equal in distribution. We also show that extensions of these new methods, and an improved version of an existing method, perform quite well in the original, unlimited case.     

Beta-MPT: Multinomial processing tree models for addressing individual differences

Traditionally, multinomial processing tree (MPT) models are applied to groups of homogeneous participants, where all participants within a group are assumed to have identical MPT model parameter values. This assumption is unreasonable when MPT models are used for clinical assessment, and it often may be suspect for applications to ordinary psychological experiments. One method for dealing with parameter variability is to incorporate random effects assumptions into a model. This is achieved by assuming that participants’ parameters are drawn independently from some specified multivariate hyperdistribution. In this paper we explore the assumption that the hyperdistribution consists of independent beta distributions, one for each MPT model parameter. These beta-MPT models are ‘hierarchical models’, and their statistical inference is different from the usual approaches based on data aggregated over participants. The paper provides both classical (frequentist) and hierarchical Bayesian approaches to statistical inference for beta-MPT models. In simple cases the likelihood function can be obtained analytically; however, for more complex cases, Markov Chain Monte Carlo algorithms are constructed to assist both approaches to inference. Examples based on clinical assessment studies are provided to demonstrate the advantages of hierarchical MPT models over aggregate analysis in the presence of individual differences.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.     

Effects of Accelerated Reading Rate on Processing Words' Syntactic Functions by Normal and Dyslexic Readers: Event Related Potentials Evidence

In the present study, the authors examined differences in brain activity, as measured by amplitudes and latencies of event related potentials (ERP) components, in Hebrew-speaking adult dyslexic and normal readers when processing sentence components with different grammatical functions. Participants were 20 dyslexic and 20 normally reading male college students aged 18–27 years. The authors examined the processing of normal word strings in word–by-word reading of sentences having subject-verb-object (SVO) syntactic structure in self- and fast-paced conditions. Data revealed that in both reading conditions, the N100 and P300 ERP components were sensitive to internal processes such as recognition of words' grammatical functions. However, the results revealed that fast-paced reading rate might affect this process, as was reflected in the systematic changes of amplitudes and latencies of both ERP components. In accelerated reading, a significant decrease of latencies and increase of amplitudes in dyslexics were shown. It was also found that influence of fast-paced reading rate was realized in the full usage of the word-order strategy in sentence processing. In turn, this fact confirmed the hypothesis concerning a syntactic processing “weakness” in dyslexia.     

Prior distributions for random choice structures

We study various axioms of discrete probabilistic choice, measuring how restrictive they are, both alone and in the presence of other axioms, given a specific class of prior distributions over a complete collection of finite choice probabilities. We do this by using Monte Carlo simulation to compute, for a range of prior distributions, probabilities that various simple and compound axioms hold. For example, the probability of the triangle inequality is usually many orders of magnitude higher than the probability of random utility. While neither the triangle inequality nor weak stochastic transitivity imply the other, the conditional probability that one holds given the other holds is greater than the marginal probability, for all priors in the class we consider. The reciprocal of the prior probability that an axiom holds is an upper bound on the Bayes factor in favor of a restricted model, in which the axiom holds, against an unrestricted model. The relatively high prior probability of the triangle inequality limits the degree of support that data from a single decision maker can provide in its favor. The much lower probability of random utility implies that the Bayes factor in favor of it can be much higher, for suitable data.     

Hierarchical models for relational event sequences

Interaction within small groups can often be represented as a sequence of events, each event involving a sender and a recipient. Recent methods for modeling network data in continuous time model the rate at which individuals interact conditioned on the previous history of events as well as actor covariates. We present a hierarchical extension for modeling multiple such sequences, facilitating inferences about event-level dynamics and their variation across sequences. The hierarchical approach allows one to share information across sequences in a principled manner—we illustrate the efficacy of such sharing through a set of prediction experiments. After discussing methods for adequacy checking and model selection for this class of models, the method is illustrated with an analysis of high school classroom dynamics from 297 sessions.     

Executive functions and social skills in survivors of pediatric brain tumor

Medical advances have resulted in increased survival rates for children with brain tumors. Consequently, issues related to survivorship have become more critical. The use of multimodal treatment, in particular cranial radiation therapy, has been associated with subsequent cognitive decline. Specifically, deficits in executive functions have been reported in survivors of various types of pediatric brain tumor. Survivors are left with difficulties, particularly in self-monitoring, initiation, inhibition, and planning, to name a few. Another domain in which survivors of pediatric brain tumor have been reported to show difficulty is that of social skills. Parents, teachers, and survivors themselves have reported decreased social functioning following treatment. Deficits in executive functions and social skills are likely interrelated in this population, as executive skills are needed to navigate various aspects of social interaction; however, this has yet to be studied empirically. Twenty-four survivors of pediatric brain tumor were assessed using a computerized task of executive functions, as well as paper-and-pencil measures of social skills and real-world executive skills. Social functioning was related to a specific aspect of executive functions, that is, the survivors' variability in response time, such that inconsistent responding was associated with better parent-reported and survivor-reported social skills, independent of intellectual abilities. Additionally, parent-reported real-world global executive abilities predicted parent-reported social skills. The implications of these findings for social skills interventions and future research are discussed.