Effects of inconsistencies in eyewitness testimony on mock-juror decisionmaking

In attempting to impeach eyewitnesses, attorney's often highlight inconsistencies in the eyewitness's recall. This study examined the differential impact of types of inconsistent testimony on mock-juror decisions. Each of 100 community members and 200 undergraduates viewed one of four versions of a videotaped trial in which the primary evidence against the defendant was the testimony of the eyewitness. I manipulated the types of inconsistent statements given by the eyewitness in the four versions: (1) consistent testimony, (2) information given on-the-stand but not given during the pre-trial investigation, (3) contradictions between on-the-stand and pre-trial statements, and (4) contradictions made on the witness stand. Subjects exposed to any form of inconsistent testimony were less likely to convict and found the defendant less culpable and the eyewitness less effective. These effects were larger for contradictions than for information given on the stand but not during pre-trial investigations.

Operant and Respondent Procedures to Establish Social Stimuli as Reinforcers in Children with Autism

According to the DSM-IV- TR (American Psychiatric Association, 2000), one of the core deficits in autism is in the impairment of social interaction. Some have suggested that underlying these deficits is the reality that individuals with autism do not find social stimuli to be as reinforcing as other types of stimuli (Dawson, 2008). An interesting and growing body of literature supports the notion that symptoms in autism may be caused by a general reduction in social motivation (Chevallier et al., 2012). A review of the literature suggests that social orienting and social motivation are low in individuals with autism, and including social motivation as a target for therapeutic intervention should be pursued (Helt et al., 2008). Through our understanding of learning processes, researchers in behavior analysis and related fields have been able to use conditioning procedures to change the function of neutral or ineffective stimuli, including tokens (Ayllon & Azrin, 1968), facial expressions (Gewirtz & Pelaez-Nogueras, 1992) and praise (Dozier et al., 2012). The current study aimed to use operant and respondent procedures to condition social stimuli that were empirically shown to not be reinforcing prior to conditioning. Further, this study aimed to compare the two procedures in their effectiveness to condition social stimuli to function as reinforcers, and in their maintenance of effects over time. Using a multiple-baseline, multi-element design, one social stimulus was conditioned under each procedure to compare the different response rates following conditioning. Finally, the study sought to determine if conditioning social stimuli to function as reinforcers had any effect on the social functioning of young children with autism. Six children diagnosed with autism between the ages of 18 months and 3 years participated. Results show that the respondent procedure (pairing) resulted in more robust and enduring effects than the operant procedure (Sd procedure). Results of a social communication assessment (ESCS, Mundy et al., 2003) before and after conditioning demonstrate gains in all areas of social communication, particularly in the areas of initiating and responding to joint attention.

Modificación de conducta en la educación especial: Diagnóstico y programas

Behavioral procedures for diagnostic and training of persons with intellectual disability. Applied programs.

El retardo en el desarrollo

Lectures on Applied Behavior Analysis techniques for diagnosis and rehabilitation of children with SEN. Theoretical, social and technical aspects.

A psicologia aplicada no treino de crianças em risco

This paper presents the answers of Psychology to three urgent tasks related to children at risk, namely, a theoretical comprehension on the origins of disorders, diagnosis procedures and treatment procedures for children with disorders. To attain these goals, the paper explains the concept of deviated development and the contributions of Applied Behavior Analysis (ABA) for diagnosis and training of children at risk, as well as the work made by the author with slum children; some cases are shown. It is concluded that ABA techniques can be applied in the rehabilitation, training and special education of children with intellectual, sensorial or motor disabilities; these techniques are efficient and can be applied successfully by non professional persons. Finally, the paper proposes a model to teach Psychology in the special conditions of developing countries, uniting teaching, research and community service.

Analysis of elastic functionally graded materials under large displacements via high-order tetrahedral elements

The purpose of this study is to present a position based tetrahedral finite element method of any order to accurately predict the mechanical behavior of solids constituted by functionally graded elastic materials and subjected to large displacements. The application of high-order elements makes it possible to overcome the volumetric and shear locking that appears in usual homogeneous isotropic situations or even in non-homogeneous cases developing small or large displacements. The use of parallel processing to improve the computational efficiency, allows employing high-order elements instead of low-order ones with reduced integration techniques or strain enhancements. The Green-Lagrange strain is adopted and the constitutive relation is the functionally graded Saint Venant-Kirchhoff law. The equilibrium is achieved by the minimum total potential energy principle. Examples of large displacement problems are presented and results confirm the locking free behavior of high-order elements for non-homogeneous materials. (C) 2011 Elsevier B.V. All rights reserved.

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

Multivariate variance-components analysis in DTI

Twin studies are a major research direction in imaging genetics, a new field, which combines algorithms from quantitative genetics and neuroimaging to assess genetic effects on the brain. In twin imaging studies, it is common to estimate the intraclass correlation (ICC), which measures the resemblance between twin pairs for a given phenotype. In this paper, we extend the commonly used Pearson correlation to a more appropriate definition, which uses restricted maximum likelihood methods (REML). We computed proportion of phenotypic variance due to additive (A) genetic factors, common (C) and unique (E) environmental factors using a new definition of the variance components in the diffusion tensor-valued signals. We applied our analysis to a dataset of Diffusion Tensor Images (DTI) from 25 identical and 25 fraternal twin pairs. Differences between the REML and Pearson estimators were plotted for different sample sizes, showing that the REML approach avoids severe biases when samples are smaller. Measures of genetic effects were computed for scalar and multivariate diffusion tensor derived measures including the geodesic anisotropy (tGA) and the full diffusion tensors (DT), revealing voxel-wise genetic contributions to brain fiber microstructure.

Wastewater analysis of Census day samples to investigate per capita input of organophosphorus flame retardants and plasticizers into wastewater

The use of organophosphate esters (PFRs) as flame retardants and plasticizers has increased due to the ban of some brominated flame retardants. There is however some concern regarding the toxicity, particularly carcinogenicity and neurotoxicity, of some of the PFRs. In this study we applied wastewater analysis to assess use of PFRs by the Australian population. Influent samples were collected from eleven wastewater treatment plants (STPs) in Australia on Census day and analysed for PFRs using gas chromatography coupled with mass spectrometry (GC-MS). Per capita mass loads of PFRs were calculated using the accurate Census head counts. The results indicate that tris(2-butoxyethyl) phosphate (TBOEP) has the highest per capita input into wastewater followed by tris(2-chloroisopropyl) phosphate (TCIPP), tris(isobutyl) phosphate (TIBP), tris(2-chloroethyl) phosphate (TCEP) and tris(1,3-dichloroisopropyl) phosphate (TDCIPP). Similar PFR profiles were observed across the Australian STPs and a comparison with European and U.S. STPs indicated similar PFR concentrations. We estimate that approximately 2.1 mg person−1 day−1 of PFRs are input into Australian wastewater which equates to 16 tonnes per annum.

Mixed pixel analysis and assessment for flood mapping

The most difficult operation in flood inundation mapping using optical flood images is to map the ‘wet’ areas where trees and houses are partly covered by water. This can be referred to as a typical problem of the presence of mixed pixels in the images. A number of automatic information extracting image classification algorithms have been developed over the years for flood mapping using optical remote sensing images, with most labelling a pixel as a particular class. However, they often fail to generate reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve this problem, spectral unmixing methods have been developed. In this thesis, methods for selecting endmembers and the method to model the primary classes for unmixing, the two most important issues in spectral unmixing, are investigated. We conduct comparative studies of three typical spectral unmixing algorithms, Partial Constrained Linear Spectral unmixing, Multiple Endmember Selection Mixture Analysis and spectral unmixing using the Extended Support Vector Machine method. They are analysed and assessed by error analysis in flood mapping using MODIS, Landsat and World View-2 images. The Conventional Root Mean Square Error Assessment is applied to obtain errors for estimated fractions of each primary class. Moreover, a newly developed Fuzzy Error Matrix is used to obtain a clear picture of error distributions at the pixel level. This thesis shows that the Extended Support Vector Machine method is able to provide a more reliable estimation of fractional abundances and allows the use of a complete set of training samples to model a defined pure class. Furthermore, it can be applied to analysis of both pure and mixed pixels to provide integrated hard-soft classification results. Our research also identifies and explores a serious drawback in relation to endmember selections in current spectral unmixing methods which apply fixed sets of endmember classes or pure classes for mixture analysis of every pixel in an entire image. However, as it is not accurate to assume that every pixel in an image must contain all endmember classes, these methods usually cause an over-estimation of the fractional abundances in a particular pixel. In this thesis, a subset of adaptive endmembers in every pixel is derived using the proposed methods to form an endmember index matrix. The experimental results show that using the pixel-dependent endmembers in unmixing significantly improves performance.

Analysis-aware design of embedded systems software

In the past many different methodologies have been devised to support software development and different sets of methodologies have been developed to support the analysis of software artefacts. We have identified this mismatch as one of the causes of the poor reliability of embedded systems software. The issue with software development styles is that they are ``analysis-agnostic.'' They do not try to structure the code in a way that lends itself to analysis. The analysis is usually applied post-mortem after the software was developed and it requires a large amount of effort. The issue with software analysis methodologies is that they do not exploit available information about the system being analyzed.

In this thesis we address the above issues by developing a new methodology, called "analysis-aware" design, that links software development styles with the capabilities of analysis tools. This methodology forms the basis of a framework for interactive software development. The framework consists of an executable specification language and a set of analysis tools based on static analysis, testing, and model checking. The language enforces an analysis-friendly code structure and offers primitives that allow users to implement their own testers and model checkers directly in the language. We introduce a new approach to static analysis that takes advantage of the capabilities of a rule-based engine. We have applied the analysis-aware methodology to the development of a smart home application.

Optimal Data Distributions in Machine Learning

In the first part of the thesis we explore three fundamental questions that arise naturally when we conceive a machine learning scenario where the training and test distributions can differ. Contrary to conventional wisdom, we show that in fact mismatched training and test distribution can yield better out-of-sample performance. This optimal performance can be obtained by training with the dual distribution. This optimal training distribution depends on the test distribution set by the problem, but not on the target function that we want to learn. We show how to obtain this distribution in both discrete and continuous input spaces, as well as how to approximate it in a practical scenario. Benefits of using this distribution are exemplified in both synthetic and real data sets.

In order to apply the dual distribution in the supervised learning scenario where the training data set is fixed, it is necessary to use weights to make the sample appear as if it came from the dual distribution. We explore the negative effect that weighting a sample can have. The theoretical decomposition of the use of weights regarding its effect on the out-of-sample error is easy to understand but not actionable in practice, as the quantities involved cannot be computed. Hence, we propose the Targeted Weighting algorithm that determines if, for a given set of weights, the out-of-sample performance will improve or not in a practical setting. This is necessary as the setting assumes there are no labeled points distributed according to the test distribution, only unlabeled samples.

Finally, we propose a new class of matching algorithms that can be used to match the training set to a desired distribution, such as the dual distribution (or the test distribution). These algorithms can be applied to very large datasets, and we show how they lead to improved performance in a large real dataset such as the Netflix dataset. Their computational complexity is the main reason for their advantage over previous algorithms proposed in the covariate shift literature.

In the second part of the thesis we apply Machine Learning to the problem of behavior recognition. We develop a specific behavior classifier to study fly aggression, and we develop a system that allows analyzing behavior in videos of animals, with minimal supervision. The system, which we call CUBA (Caltech Unsupervised Behavior Analysis), allows detecting movemes, actions, and stories from time series describing the position of animals in videos. The method summarizes the data, as well as it provides biologists with a mathematical tool to test new hypotheses. Other benefits of CUBA include finding classifiers for specific behaviors without the need for annotation, as well as providing means to discriminate groups of animals, for example, according to their genetic line.

个体人格类型与气味偏好的关系

So far the studies on personality are based on indirect way due to the complexity and limitation. Present research attempt to find another route to approach personality through olfaction. The framework of present research is on account of a modern personality theory model produced by the British psychologist Eysenck. The aim of present research is to prove a correlation between olfaction and personality because sensation and perception of the olfaction on special part of brain closely related to emotional arousal. This research was carefully designed and performed by both psychological questionnaire and olfaction examination. The results indicated some good correlation between smell sense and personal character, which can by applied into personality and behavior analysis.