Regularized brain reading with shrinkage and smoothing

Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high dimensional. Hence, direct estimates are inherently imprecise and call for regularization. We compare a suite of approaches which regularize via shrinkage: ridge regression, the elastic net (a generalization of ridge regression and the lasso), and a hierarchical Bayesian model based on small area estimation (SAE). We contrast regularization with spatial smoothing and combinations of smoothing and shrinkage. All methods are tested on functional magnetic resonance imaging (fMRI) data from multiple subjects participating in two different experiments related to reading, for both predicting neural response to stimuli and decoding stimuli from responses. Interestingly, when the regularization parameters are chosen by cross-validation independently for every voxel, low/high regularization is chosen in voxels where the classification accuracy is high/low, indicating that the regularization intensity is a good tool for identification of relevant voxels for the cognitive task. Surprisingly, all the regularization methods work about equally well, suggesting that beating basic smoothing and shrinkage will take not only clever methods, but also careful modeling.Comment: Published at http://dx.doi.org/10.1214/15-AOAS837 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Are there nonverbal cues to commitment? An exploratory study using the zero-acquaintance video presentation paradigm

Altruism is difficult to explain evolutionarily if subtle cheaters exist in a population (Trivers, 1971). A pathway to the evolutionary maintenance of cooperation is nonverbal altruist-detection. One adaptive advantage of nonverbal altruist-detection is the formation of trustworthy division of labour partnerships (Frank, 1988). Three studies were designed to test a fundamental assumption behind altruistic partner preference models. In the first experiment perceivers (blind with respect to target altruism level) made assessments of video-clips depicting self-reported altruists and self-reported non-altruists. Video-clips were designed with attempts to control for attractiveness, expressiveness, role-playing ability, and verbal content. Overall perceivers rated altruists as more “helpful” than non-altruists. In a second experiment manipulating the payoffs for cooperation, perceivers (blind with respect to payoff condition and altruism level) assessed altruists who were helping others as more “concerned” and “attentive” than non-altruists. However perceivers assessed the same altruists as less “concerned” and “attentive” than non-altruists when the payoffs were for self. This finding suggests that perceivers are sensitive to nonverbal indicators of selfishness. Indeed the self-reported non-altruists were more likely than self-reported altruists to retain resources for themselves in an objective measure of cooperative tendencies (i.e. a dictator game). In a third study altruists and non-altruists’ facial expressions were analyzed. The smile emerged as a consistent cue to altruism. In addition, altruists exhibited more expressions that are under involuntary control (e.g., orbicularis oculi) compared to non-altruists. Findings Are there nonverbal cues to commitment? suggest that likelihood to cooperate is signaled nonverbally and the putative cues may be under involuntary control as predicted by Frank (1988)

Adolescent Literacy and the Achievement Gap: What Do We Know and Where Do We Go From Here?

Reviews research and program initiatives focused on improving adolescent academic achievement by targeting literacy. Provides ideas for collaboration and coordination of funding efforts to improve the literacy achievement of under-performing adolescents

The Effects of the Read 180 Program on Oral Reading Fluency, Linguistic Comprehension, and Reading Comprehension with Secondary Special Education Students

There is great concern about secondary special education students reading achievement in decoding, listening comprehension, and reading comprehension. The READ 180 Program is an evidence and scientific based reading program that includes direct instruction, computer aided instruction, and reading materials that are high interest and implement the common core. The purpose of this study was to see the differences in oral reading fluency, linguistic comprehension, and reading comprehension in a pretest posttest model over a fourteen-week testing period. Ten ninth grade secondary students who were reading below the 25th percentile were instructed with the READ 180 Program with fidelity (90 minutes a day, four days a week, for fourteen weeks). The students were pretested and posttested with the Listening Comprehension Adolescent and the Gate MacGinitie Reading Comprehension Test. The students oral reading fluency was progressed monitored weekly with one minuet timed eighth grade reading probes from easyCBM that tracked total words read correctly, and the total number of miscues (words mispronounced, or omitted). The results showed that the students increased in the number or words read correctly and had a statistically significant decrease in miscues. In addition, on the Listening Comprehension pretest and posttest, the students realized a statistically significant increase on their posttest scores. The reading comprehension pretest and posttest scores did not see any change over the fourteen-week testing period. The results of the study conclude that the READ 180 Program had an effect on the student\u27s oral reading fluency and listening comprehension posttest scores

A toolbox for representational similarity analysis.

Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/)

Stealth Dyslexia: Cognitive and Achievement Profiles Of Gifted Students With Dyslexia

Identifying students who are gifted with dyslexia (GWD) has presented a host of challenges to practitioners in school and clinic settings because these individuals possess both qualities of giftedness and learning difficulties, yet do not ‘fit’ in either category. The term “stealth dyslexia” was coined to indicate the presence of high abilities that may mask dyslexia traits, complicate diagnostic accuracy, and allow individuals to compensate for their weaknesses. The masking of reading difficulties can cause dyslexia to remain undetected in gifted children for a prolonged period of time which may leave them prone to academic disengagement. The present study provided an empirical examination of the patterns of academic strengths and weaknesses students with GWD. Using data from 98 clients from a private clinic, the scores of three different identified groups were compared: GWD, Gifted-only, and Dyslexiaonly. A profile analysis, followed by post-hoc one-way ANOVAs, compared the groups across cognitive (WISC-V) and achievement (WIAT-III) measures. Results indicated that the cognitive scores of the groups varied from each other in the predicted patterns (i.e., higher verbal, abstract, and visual spatial reasoning) for Gifted-only and GWD, and lower cognitive efficiency (i.e., working memory and processing speed) for GWD and Dyslexia-only groups. Across achievement subtest variables, GWD scores were significantly above the Dyslexia-only students on all measures with the exception of Pseudoword Decoding, and below the Gifted-only students on all measures with the exception of Reading Comprehension and Listening Comprehension. Across achievement composite scores, the GWD scores were in between the Dyslexia-only and Gifted-only groups Total Reading and Reading Comprehension & Fluency, no different from Dyslexia-only on Basic Reading, and no different from Gifted-only on Oral Language. The GWD group displayed greater variability, as measured by the difference between highest and lowest subtest scores, in reading performance than the comparison groups. Finally, overall cognitive scores were significantly lower than the index score that omits working memory and processing speed among participants in the GWD group. The implications from this study regarding the nature, magnitude, and range of cognitive and achievement strengths and weaknesses of GWD students will help educators and psychologists accurately recognize and advocate for these students

Automated generation of computationally hard feature models using evolutionary algorithms

This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.European Commission (FEDER), the Spanish Government and the Andalusian Government

The Relationship between Reading Fluency, Writing Fluency, and Reading Comprehension in Suburban Third-Grade Students

The topic of reading fluency is of great importance in education today. Research has shown a significant positive relationship between reading fluency and reading comprehension. However, little is known about writing fluency and its connection with reading comprehension. The purpose of this study was to examine the relationships between reading fluency, writing fluency, and reading comprehension. First, using the principles of assessing reading fluency, I designed a writing assessment and measured the writing fluency of 54 3rd graders. I examined the writing assessments as they related to the students\u27 reading fluency and reading comprehension scores. Secondly, I performed a quasi-experimental scientific study with 3rd grade students. The control group (n=36) were taught the board-adopted language arts curriculum, while the experimental group (n=18) had systematic direct instruction in reading and writing fluency in addition to the regular language arts curriculum. The research questions were: What is the relationship between students\u27 reading comprehension and reading fluency among a group of third graders? What is the relationship between their reading comprehension and writing fluency? What is the relationship between their reading fluency and writing fluency? Will the experimental group of students with direct instruction in reading and writing fluency outperform the control group in reading comprehension? What other factors are involved in increasing reading comprehension? Pearson\u27s correlation statistic, paired t-tests, independent samples t-tests, and multiple linear regression analysis were used to analyze the data. All statistical analyses were performed using PASW (formerly SPSS) for Windows. Consistent with reading research, the results showed there was a strongly positive correlation between reading comprehension and reading fluency. This study also found a correlation between reading comprehension and writing as well as a correlation between reading comprehension and writing fluency. However, the link between reading comprehension and writing fluency was not found in pretest measurement, or the posttest-pretest measurement. The ANOVA results showed that reading and writing fluency explained a statistically significant 50% of the total variance in reading comprehension scores. This study also showed a strong positive correlation between reading fluency and writing fluency in the posttest measurement. In the quasi-experimental study, the experimental group did not outperform the experimental group: both groups made significant progress. The major implication of this study is that writing could help increase reading comprehension, which results in another tool for teachers to use in teaching reading comprehension. This could result in an additional emphasis in teaching writing skills in the classroom