Antonio Calcagnì

Analyzing spatial data from mouse tracker methodology: An entropic approach

Mouse tracker methodology has recently been advocated to explore the motor components of the cognitive dynamics involved in experimental tasks like categorization, decision-making, and language comprehension. This methodology relies on the analysis of computer-mouse trajectories, by evaluating whether they significantly differ in terms of direction, amplitude, and location when a given experimental factor is manipulated. In this kind of study, a descriptive geometric approach is usually adopted in the analysis of raw trajectories, where they are summarized with several measures, such as maximum-deviation and area under the curve. However, using raw trajectories to extract spatial descriptors of the movements is problematic due to the noisy and irregular nature of empirical movement paths. Moreover, other significant components of the movement, such as motor pauses, are disregarded. To overcome these drawbacks, we present a novel approach (EMOT) to analyze computer-mouse trajectories that quantifies movement features in terms of entropy while modeling trajectories as composed by fast movements and motor pauses. A dedicated entropy decomposition analysis is additionally developed for the model parameters estimation. Two real case studies from categorization tasks are finally used to test and evaluate the characteristics of the new approach.

Dynamic Fuzzy Rating Tracker (DYFRAT)

Graphical abstractDisplay Omitted HighlightsThe article presents an original methodology for measuring fuzziness in human rating data.It avoids traditional problems of standard rating scales and other types of fuzzy scales.It models fuzziness by measuring some real-time biometric information during the cognitive process of rating.Some real applications support the usefulness of the proposed novel methodology. Rating scales (such as, Likert scales, Guttman scales, Feelings thermometers, etc.) represent simple tools for measuring attitudes, judgements and subjective preferences in human rating contexts. Because rating scales show some useful properties (e.g., measurement uniformity, considerable flexibility, statistically appealing), they represent popular and reliable instruments in socio-behavioral sciences. However, standard rating scales suffer also from some relevant limitations. For example, they fail in measuring vague and imprecise information and, above all, they are only able to capture the final outcome of the cognitive process of rating (i.e., the raters response). To overcome these limitations, some fuzzy versions of these scales (e.g., fuzzy conversion scales, fuzzy rating scales) have been proposed over the years. However, also these more sophisticated scales show some important shortcomings (e.g., difficulty in fuzzy variables construction and potential lack of ecological validity). In this paper, we propose a novel methodology (DYFRAT) for modeling human rating evaluations from a fuzzy-set perspective. In particular, DYFRAT captures the fuzziness of human ratings by modeling some real-time biometric events that occur during the cognitive process of rating in an ecological measurement setting. Moreover, in order to show some important characteristics of the proposed methodology, we apply DYFRAT to some empirical rating situations concerning decision making and risk assessment scenarios.

Generalized cross entropy method for analysing the SERVQUAL model

The aim of this paper is to define a new approach for the analysis of data collected by means of SERVQUAL questionnaires which is based on the generalized cross entropy (GCE) approach. In this respect, we firstly give a short review about the important role that SERVQUAL plays in the analysis of service quality as well as in the assessment of the competitiveness of public and private organizations. Secondly, we provide a formal definition of GCE approach together with a brief discussion about its features and usefulness. Finally, we show the application of GCE for a SERVQUAL model, based on a patients’ satisfaction case study and we discuss the results obtained by using the proposed GCE-SERVQUAL methodology.

Non-convex fuzzy data and fuzzy statistics: a first descriptive approach to data analysis

LR-fuzzy numbers are widely used in Fuzzy Set Theory applications based on the standard definition of convex fuzzy sets. However, in some empirical contexts such as, for example, human decision making and ratings, convex representations might not be capable to capture more complex structures in the data. Moreover, non-convexity seems to arise as a natural property in many applications based on fuzzy systems (e.g., fuzzy scales of measurement). In these contexts, the usage of standard fuzzy statistical techniques could be questionable. A possible way out consists in adopting ad-hoc data manipulation procedures to transform non-convex data into standard convex representations. However, these procedures can artificially mask relevant information carried out by the non-convexity property. To overcome this problem, in this article we introduce a novel computational definition of non-convex fuzzy number which extends the traditional definition of LR-fuzzy number. Moreover, we also present a new fuzzy regression model for crisp input/non-convex fuzzy output data based on the fuzzy least squares approach. In order to better highlight some important characteristics of the model, we applied the fuzzy regression model to some datasets characterized by convex as well as non-convex features. Finally, some critical points are outlined in the final section of the article together with suggestions about future extensions of this work.

A dimension reduction technique for two-mode non-convex fuzzy data

Fuzzy modeling and fuzzy statistics provide useful tools for handling empirical situations affected by vagueness and imprecision in the data. Several fuzzy statistical models and methods (e.g., fuzzy regression, fuzzy principal component analysis, fuzzy clustering) have been developed over the years. Generally the standard LR-fuzzy data representation has been used in these methods. However, several empirical contexts, such as human ratings and decision making, may show more complex fuzzy structures which cannot be successfully modeled by the LR representation. In all these cases another type of fuzzy data representation, the so-called LHIR representation, should be preferred instead. In particular, this novel representation allows to handle with fuzzy data which are characterized by non-convex membership functions. In this paper, we address the problem of summarizing large datasets characterized by two-mode non-convex fuzzy data. We introduce a novel dimension reduction technique (NCFCA) based on the framework of Component Analysis and Least squares programming. Finally, to better highlight some important characteristics of the proposed model, we apply NCFCA to three empirical datasets concerning behavioral and socio-economic issues.

A Generalized Maximum Entropy (GME) estimation approach to fuzzy regression model

Graphical abstractDisplay Omitted HighlightsWe consider two fuzzy regression models from fuzzy least squares tradition.We rewrite these models within the Generalized Maximum Entropy Approach of estimation.We compare LS and GME approaches in the multicollinearity problem.Monte Carlo studies show increasing multicollinearity GME outperforms LS in efficiency.Empirical evidence shows some applicative advantages of GME. Fuzzy statistics provides useful techniques for handling real situations which are affected by vagueness and imprecision. Several fuzzy statistical techniques (e.g., fuzzy regression, fuzzy principal component analysis, fuzzy clustering) have been developed over the years. Among these, fuzzy regression can be considered an important tool for modeling the relation between a dependent variable and a set of independent variables in order to evaluate how the independent variables explain the empirical data which are modeled through the regression system. In general, the standard fuzzy least squares method has been used in these situations. However, several applicative contexts, such as for example, analysis with small samples and short and fat matrices, violation of distributional assumptions, matrices affected by multicollinearity (ill-posed problems), may show more complex situations which cannot successfully be solved by the fuzzy least squares. In all these cases, different estimation methods should instead be preferred. In this paper we address the problem of estimating fuzzy regression models characterized by ill-posed features. We introduce a novel fuzzy regression framework based on the Generalized Maximum Entropy (GME) estimation method. Finally, in order to better highlight some characteristics of the proposed method, we perform two Monte Carlo experiments and we analyze a real case study.

Obstructive sleep apnea syndrome and olfactory perception: An OERP study

Obstructive Sleep Apnea Syndrome (OSA) is characterized by snoring associated with repeated apnea and/or obstructive hypopnea. The nasal airways of OSA patients, measured via acoustic rhinometry, could be significantly narrower than healthy subjects and this reduced nasal structure can impair olfactory function. The relationship between nasal structure and olfactory function, assessed via behavioral test results, indicates that there is a high prevalence of nasal airflow problems. Based on these assumptions, the purpose of this study was to carry out an assessment of olfactory perception in OSA patients through the Chemosensory Event-Related Potentials (CSERP), investigating the N1 component and the Late Positive Component (LPC). Twelve OSA patients, non-smokers, were recruited in the Pulmonary Rehabilitation Unit, scored with the Epworth Sleepiness Scales, after Polygraphic Recording, Apnea Hypopnea Index and Body Mass Index evaluation. The control group consisted of twelve healthy controls, non-smokers, recruited as volunteers. Subjects, during an EEG recording, performed an oddball olfactory recognition task based on two scents: rose and eucalyptus. Main results highlighted differences in N1 and LPC between OSA and controls. OSA patients presented faster N1 latencies and greater amplitude. The same trend was found in LPC, where OSA showed decreased latency and increased amplitude during rose stimulation, in the right inferior frontal cortex. and faster latencies in left centroparietal cortex OERP results can suggest an impairment in endogenous components. This result could be the consequence of the exogenous perceptual difficulty highlighted in N1 component. The increased arousal could also be related to the respiratory activity involved during the olfactory task.

Olfactory and Haptic Crossmodal Perception in a Visual Recognition Task

Olfactory perception is affected by cross-modal interactions between different senses. However, although the effect of cross-modal interactions for smell have been well investigated, little attention has been paid to the facilitation expressed by haptic interactions with a manipulation of the odorous object’s shape. The aim of this research is to investigate whether there is a cortical modulation in a visual recognition task if the stimulus is processed through an odorous cross-modal pathway or by haptic manipulation, and how these interactions may have an influence on early visual-recognition patterns. Ten healthy non-smoking subjects (25 years ± 5 years) were trained to have a haptic manipulation of 3-D models and olfactory stimulation. Subsequently, a visual recognition task was performed during an electroencephalography recording to investigate the P3 Event Related Potentials components. The subjects had to respond on the keyboard according to their subjective predominant recognition (olfactory or haptic). The effects of haptic and olfactory condition were assessed via linear mixed-effects models (LMMs) of the lme4 package. This model allows for the variance related to random factors to be controlled without any data aggregation. The main results highlighted that P3 increased in the olfactory cross-modal condition, with a significant two-way interaction between odor and left-sided lateralization. Furthermore, our results could be interpreted according to ventral and dorsal pathways as favorite ways to olfactory crossmodal perception.