STUDYING TRAVEL-RELATED INDIVIDUAL ASSESSMENTS AND DESIRES BY COMBINING HIERARCHICALLY STRUCTURED ORDINAL VARIABLES

Ordinal measures are frequently encountered in travel behavior research. This paper presents a new method for combining them when a hierarchical structure of the data can be presumed. This method is applied to study the subjective assessment of the amount of travel by different transportation modes among a group of French clerical workers, along with the desire to increase or decrease the use of such modes. Some advantages of this approach over traditional data reduction technique such as factor analysis when applied to ordinal data are then illustrated. In this study, combining evidence from several variables sheds light on the observed moderately negative relationship between the personal assessment of the amount of travel and the desire to increase or decrease it, thus integrating previous partial (univariate) results. We find a latent demand for travel, thus contributing to clarify the behavioral mechanisms behind the induced traffic phenomenon. Categorizing the above relationship by transportation mode shows a desire for a less environmental-friendly mix of modes (i.e. a greater desire to use heavy motorized modes and a lower desire to use two-wheeled modes), whenever the respondents do not feel to travel extensively. This result, combined with previous theoretical investigations concerning the determinants of the desire to alter trips consumption levels, shows the importance of making people aware of how much they travel.

Confirmatory Factor Analysis and Multidimensional Scaling for Construct Validation of Cognitive Abilities

Although factor analysis is the most commonly-used method for examining the structure of cognitive variable interrelations, multidimensional scaling (MDS) can provide visual representations highlighting the continuous nature of interrelations among variables. Using data (N = 8,813; ages 17-97 years) aggregated across 38 separate studies, MDS was applied to 16 cognitive variables representative of five well-established cognitive abilities. Parallel to confirmatory factor analytic solutions, and consistent with past MDS applications, the results for young (18-39 years), middle (40-65 years), and old (66-97 years) adult age groups consistently revealed a two-dimensional radex disk, with variables from fluid reasoning tests located at the center. Using a new method, target measures hypothesized to reflect three aspects of cognitive control (updating, storage-plus-processing, and executive functioning) were projected onto the radex disk. Parallel to factor analytic results, these variables were also found to be centrally located in the cognitive ability space. The advantages and limitations of the radex representation are discussed.

Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies

The recent explosion in procurement and availability of high-dimensional gene- and protein-expression profile datasets for cancer diagnostics has necessitated the development of sophisticated machine learning tools with which to analyze them. A major limitation in the ability to accurate classify these high-dimensional datasets stems from the 'curse of dimensionality', occurring in situations where the number of genes or peptides significantly exceeds the total number of patient samples. Previous attempts at dealing with this issue have mostly centered on the use of a dimensionality reduction (DR) scheme, Principal Component Analysis (PCA), to obtain a low-dimensional projection of the high-dimensional data. However, linear PCA and other linear DR methods, which rely on Euclidean distances to estimate object similarity, do not account for the inherent underlying nonlinear structure associated with most biomedical data. The motivation behind this work is to identify the appropriate DR methods for analysis of high-dimensional gene- and protein-expression studies. Towards this end, we empirically and rigorously compare three nonlinear (Isomap, Locally Linear Embedding, Laplacian Eigenmaps) and three linear DR schemes (PCA, Linear Discriminant Analysis, Multidimensional Scaling) with the intent of determining a reduced subspace representation in which the individual object classes are more easily discriminable.

Location and longing: the nicotine craving experience in virtual reality

Considerable research suggests that cigarette craving is complex, with psychological, emotional, cognitive, and behavioral aspects that are inadequately captured by typical craving assessments that focus on level of severity. That is, the experience of craving, for cigarette smokers, remains poorly understood. This study immersed smokers in different virtual reality (VR) scenarios (with and without cigarette cues present), collected detailed craving assessments, and analyzed the data using a multidimensional analytic approach. Non-treatment-seeking, nicotine dependent smokers (N=22) experienced two different virtual reality scenarios, one with cigarette cues and one without, and rated 24 descriptors related to craving. Multidimensional scaling (MDS) models demonstrate that smokers' experience of craving is qualitatively, structurally different under VR smoking cue conditions versus neutral conditions. This finding sheds new light on the complexity of craving as well as implications for its measurement.

Neuroplasticity in the processing of pitch dimensions: a multidimensional scaling analysis of the mismatch negativity

PURPOSE

An auditory electrophysiological study was conducted to explore the influence of language experience on the saliency of dimensions underlying cortical pitch processing.

METHODS

Mismatch negativity (MMN) responses to Mandarin tones were recorded in Chinese and English participants (n=10 per group) using a passive oddball paradigm. Stimuli consisted of three tones (T1: high level; T2: high rising; T3: low falling-rising). There were three oddball conditions (standard/deviant): T1/T2, T1/T3, T2/T3. In the T1/T2 and T1/T3 conditions, each tonal pair represented a contrast between a level and a contour tone; the T2/T3 condition, a contrast between two contour tones. Twenty dissimilarity matrices were created using the MMN mean amplitude measured from the Fz location for each condition per participant, and analyzed by an individual differences multidimensional scaling model.

RESULTS

Two pitch dimensions were revealed, interpretively labeled as 'height' and 'contour'. The latter was found to be more important for Chinese than English subjects. Using individual weights on the contour dimension, a discriminant function showed that 17 out of 20 participants were correctly classified into their respective language groups.

CONCLUSIONS

The MMN can serve as an index of pitch features that are differentially weighted depending on a listener's experience with lexical tones and their acoustic correlates within a particular tone space.

Exploring the nonlinear geometry of protein homology

The explosion of biological data resulting from genomic and proteomic research has created a pressing need for data analysis techniques that work effectively on a large scale. An area of particular interest is the organization and visualization of large families of protein sequences. An increasingly popular approach is to embed the sequences into a low-dimensional Euclidean space in a way that preserves some predefined measure of sequence similarity. This method has been shown to produce maps that exhibit global order and continuity and reveal important evolutionary, structural, and functional relationships between the embedded proteins. However, protein sequences are related by evolutionary pathways that exhibit highly nonlinear geometry, which is invisible to classical embedding procedures such as multidimensional scaling (MDS) and nonlinear mapping (NLM). Here, we describe the use of stochastic proximity embedding (SPE) for producing Euclidean maps that preserve the intrinsic dimensionality and metric structure of the data. SPE extends previous approaches in two important ways: (1) It preserves only local relationships between closely related sequences, thus allowing the map to unfold and reveal its intrinsic dimension, and (2) it scales linearly with the number of sequences and therefore can be applied to very large protein families. The merits of the algorithm are illustrated using examples from the protein kinase and nuclear hormone receptor superfamilies.