Multivariate pattern analysis, or MVPA, has become one of the most popular analytic methods in cognitive neuroscience. Since its inception, MVPA has been heralded as offering much more than regular univariate analyses, for—we are told—it not only can tell us which brain regions are engaged while processing particular stimuli, but also which patterns of neural activity represent the categories the stimuli are selected from. We disagree, and in the current paper we offer four conceptual challenges to the use of MVPA (...) to make claims about neural representation. Our view is that the use of MVPA to make claims about neural representation is problematic. (shrink)

This work introduces two new curves that are multivariate generalizations of the “classical” Lorenz curve. All data of d-variate distributions can be visualized by drawing these curves in the plane, whereas Koshevoy’s and Mosler’s generalization by a lift zonoid in ℝd+1 can only be drawn for d = 2. The generalizations of the Lorenz curve induce partial orderings of d-variate distributions. Furthermore, two inequality or heterogeneity measures that are consistent with the induced rankings are proposed. They can be considered as (...) new generalizations of the univariate Gini coefficient. For deciding which of the two measures is more appropriate for measuring a sort of convergence concerning different countries of an union or of regions of a country, we establish systems of axioms. Although these systems are reflecting natural properties, several of the axioms are new. Moreover, by means of these axioms well- known inequality measures are tested, too. (shrink)

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, (...) lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries. (shrink)

Social scientists regularly make use of multivariate models to describe complex social phenomena. It is argued that this approach is useful for modelling the variety of cognitive and social factors contributing to scientific change, and superior to the integrated models of scientific change currently available. It is also argued that care needs to be taken in drawing normative conclusions: cognitive factors are not instrinsically more "rational" than social factors, nor is it likely that social factors, by some "invisible hand of (...) reason," generally work to produce scientific success. A multivariate model of the biasing factors within a scientific community at particular times is developed. This model, which is an example of work in social epistemology, yields normative conclusions. (shrink)

Alessandro Ferrara’s The Democratic Horizon: Hyperpluralism and the Renewal of Political Liberalism poses an important challenge to recent defenders of ‘realism’ in political theory and shows that a renewal of Rawlsian ideal theory is possible. Ferrara focuses on the contemporary condition of ‘hyperpluralism’, in which every comprehensive worldview and religion has to admit the equal validity of at least one other conception, and claims that only a ‘pluralist justification of pluralism’ can lead to a genuine revival of the democratic horizon. (...) Naming such a project of democracy the ‘multivariate’ polity, he uses Rawls’ method of conjecture to show how such a justification strategy is possible. I argue that the multivariate polity may attain pluralism but not stability and may fail in securing democratic respect among equal citizens for each other’s point of view. (shrink)

This article provides a retrospective, current and prospective overview on developments in brain research and neuroscience. Both theoretical and empirical studies are considered, with emphasis in the concept of multivariability and metastability in the brain. In this new view on the human brain, the potential multivariability of the neuronal networks appears to be far from continuous in time, but confined by the dynamics of short-term local and global metastable brain states. The article closes by suggesting some of the (...) implications of this view in future multidisciplinary brain research. (shrink)

We reflect upon quantification in biology in two ways. First, from a sensory scientific perspective, we address theories and methods for studying sensation, perception, and cognition. Sensory science concerns action of the human senses, which are not passive receivers but operate in an active and fundamental way for human beings in various social and environmental contexts. In the past one could only handle one-to-one relationships within a univariate framework. Today we have tools to capture complexity closer to real world situations. (...) Thus, the second perspective is rooted in multivariate thinking and modeling tools. Relying mainly on soft modeling-exploratory methods, such as partial least squares regression, we claim that quantification in sensory biology in the future should be met with a dialectic view that simultaneously exploits the two perspectives. (shrink)

We reflect upon quantification in biology in two ways. First, from a sensory scientific perspective, we address theories and methods for studying sensation, perception, and cognition. Sensory science concerns action of the human senses, which are not passive receivers but operate in an active and fundamental way for human beings in various social and environmental contexts. In the past one could only handle one-to-one relationships within a univariate framework. Today we have tools to capture complexity closer to real world situations. (...) Thus, the second perspective is rooted in multivariate thinking and modeling tools. Relying mainly on soft modeling-exploratory methods, such as partial least squares regression, we claim that quantification in sensory biology in the future should be met with a dialectic view that simultaneously exploits the two perspectives. (shrink)

Based on the review and research on the teleworking modality and the variables that impact it at the organizational level, such as quality of life, communication, organizational culture or productivity, a case study is proposed under the innovation and teleworking model of a company. Japanese automotive industry for the application of Multivariate Analysis techniques such as Principal Component Analysis and Linear Regression, in order to condense the information provided by multiple variables into principal components and validate the relationships and impact (...) that exist between them, thus determining the interdependence and correlation of the same with the telework variable, allowing to simplify the complexity of sample spaces with many dimensions. It was possible to identify the main components with the variables' own labels and the dependent and predictive variables of the case were statistically validated, through the use of the IBM SPSS. (shrink)

Simple explanations are very often inadequate and can encourage faulty inferences. We examined college students’ explanations regarding illegal immigration to determine the prevalence of single-factor explanations. The form of students’ explanations was predicted by their responses on a simple three-item forced-choice multivariable causal reasoning task in which they selected the strongest evidence against a causal claim. In a further qualitative investigation of explanations by a sample of community adults, we identified positive features among those who scored high on this multivariable (...) causal reasoning task. We consider limitations of single-factor reasoning and means of encouraging more comprehensive explanations to support claims. (shrink)

While univariate functional magnetic resonance imaging data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during (...) the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models. (shrink)

Mazur & Booth (1998) (M&B) suggested that high testosterone (T) relates to status, dominance, and (anti-) social behaviour. However, low T also relates to status and to formal dominance. The General Trait Covariance (GTC) model predicts both relations under the assumption that high and low T modulates the genotype in ways that enforce the development of almost polar covariant patterns of body, brain, intellectual, and personality traits, irrespective of race. The precise modelling of these dose-dependent molecular body-intelligence-personality-behaviour relations requires that (...) causes, mechanisms, and effects enjoy equal operational standing. (shrink)

Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an advantageous framework for identifying emotion-specific biomarkers and for testing predictions of theoretical models of emotion. Based on initial studies using multivariate pattern classification, we suggest that (...) central and autonomic nervous system activity can be reliably decoded into distinct emotional states. Finally, we consider future directions in applying pattern classification to understand the nature of emotion in the nervous system. (shrink)

In the global world, momentous migratory tides have produced hyper-pluralism on the domestic scale, bringing citizens with radically different conceptions of life, justice and the good to coexist side by side. Conjectural arguments about the acceptance of pluralism, the next best to public reason when shared premises are too thin, may not succeed in convincing all constituencies. What resources, then, can liberal democracy mobilize? The multivariate democratic polity is the original answer to this question, based on an interpretation of Rawls (...) which revisits Political Liberalism in the light of The Law of Peoples . The unscrutinized assumption is highlighted, often read into Rawls’s Political Liberalism , that a polity moves homogeneously and all of a piece from religious conflict to modus vivendi, constitutional consensus and finally to overlapping consensus. Drawing on The Law of Peoples , a different picture can be obtained. (shrink)

In recent years, multivariate pattern analysis has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging, electroencephalography, and other neuroimaging methodologies. In a similar time frame, “deep learning” has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on (...) linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox intended to facilitate dMVPA for neuroscientists everywhere. (shrink)

Despite the fact that human daily emotions are co-occurring by nature, most neuroscience studies have primarily adopted a univariate approach to identify the neural representation of emotion (emotion experience within a single emotion category) without adequate consideration of the co-occurrence of different emotions (emotion experience across different emotion categories simultaneously). To investigate the neural representations of multivariate emotion experience, this study employed the inter-situation representational similarity analysis (RSA) method. Researchers used an EEG dataset of 78 participants who watched 28 video (...) clips and rated their experience on eight emotion categories. The EEG-based electrophysiological representation was extracted as the power spectral density (PSD) feature per channel in the five frequency bands. The inter-situation RSA method revealed significant correlations between the multivariate emotion experience ratings and PSD features in the Alpha and Beta bands, primarily over the frontal and parietal-occipital brain regions. The study found the identified EEG representations to be reliable with sufficient situations and participants. Moreover, through a series of ablation analyses, the inter-situation RSA further demonstrated the stability and specificity of the EEG representations for multivariate emotion experience. These findings highlight the importance of adopting a multivariate perspective for a comprehensive understanding of the neural representation of human emotion experience. (shrink)

Sungmoon Kim’s pragmatic Confucian democracy tries to provide a mediating position between the instrumental model and the intrinsic model of democracy. However, this model of Confucian democracy is problematic because it fails to justify the unique role Confucianism plays in accommodating democracy when it is one among many comprehensive doctrines in East Asia. To be truly pragmatic about democracy is to hold a pluralistic attitude toward how people will come to terms with it. This article aims to push the pragmatic (...) tendency further and propose an alternative model of democracy that has a multivariate structure, a neutral state, and an active public role for Confucianism. This multivariate model represents a more promising future for democracy in East Asia. (shrink)

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor (...) that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models. (shrink)

Since its introduction, multivariate pattern analysis, or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is (...) a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. 1Introduction 2A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1What is multivariate pattern analysis? 2.2The informational benefits of multivariate pattern analysis 3Why the Decoder’s Dictum Is False 3.1We don’t know what information is decoded 3.2The theoretical basis for the dictum 3.3Undermining the theoretical basis 4Objections and Replies 4.1Does anyone really believe the dictum? 4.2Good decoding is not enough 4.3Predicting behaviour is not enough 5Moving beyond the Dictum 6Conclusion. (shrink)

Since its introduction, multivariate pattern analysis, or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is (...) a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. (shrink)

_Abstract_: The significance of neuroscientific findings for the analysis of central problems in cognitive science has long been a matter of debate. Recent developments in cognitive neuroscience have reignited this discussion, especially with regard to the study of cognitive representations and cognitive processes. The present paper focuses on multivariate analyses, a class of neuroscientific methods that promises to shed new light on the neural bases of cognitive representations. Multivariate approaches are both powerful and increasingly used. Yet, we argue that their (...) successful application in neuroscience requires significant theoretical and methodological clarification. After providing a preliminary assessment of the pros and cons of multivariate methods, we claim that their successful application crucially depends on how we conceptualize the relationships between representations, cognitive processes, and neural data, in other words, on the cognitive ontology we use to describe the human mind. Our discussion also highlights some general strengths and weaknesses of neuroscientific contributions to the program of classical cognitive science. _Keywords_: Cognitive Process; fMRI; Multivariate Analysis; Marr’s Three Levels of Analysis; Cognitive Ontology _Rappresentazioni e processi: quale ruolo per i metodi multivariati nelle neuroscienze cognitive?_ _Riassunto_: La rilevanza dei risultati neuroscientifici per quanto riguarda i problemi centrali delle scienze cognitive è motivo di discussione. Recenti sviluppi nelle neuroscienze cognitive hanno rianimato tale dibattito, in particolare rispetto allo studio delle rappresentazioni e dei processi cognitivi. Il presente articolo si focalizza sull’analisi multivariata, un insieme di metodi neuroscientifici che si promettono di studiare le basi neurali delle rappresentazioni cognitive. Nonostante le potenzialità e l’uso pervasivo degli approcci multivariati, in questo lavoro sosteniamo che prima di poter valutare il loro effettivo contributo nello studio delle rappresentazioni cognitive sia necessaria una chiarificazione teorica e metodologica. Dopo una discussione preliminare dei vantaggi e degli svantaggi dei metodi multivariati, evidenziamo come una loro efficace applicazione dipenda in maniera sostanziale da come viene intesa la relazione tra rappresentazioni, processi cognitivi e dati neurali o, in altre parole, dall’ontologia cognitiva che impieghiamo per descrivere la mente umana. Il presente lavoro affronta inoltre i generali punti di forza e gli elementi critici rispetto al contributo della ricerca neuroscientifica nel programma delle scienze cognitive classiche. _Parole chiave_: Cognitive Process; fMRI; Multivariate Analysis; Marr’s Three Levels of Analysis; Cognitive Ontology. (shrink)

The program of the evolution of the base of knowledge in machines' mechanisms on the example of technical systems of arbitrary purpose and structure with the aim of formalization and structurization of knowledge for creation of new techniques of automatized projecting in suggested. The program is declared as the process of transference of the base of knowledge from its initial state into final one, at the permissible restrictions in quality and resources in real time. The program's concept is based on (...) the introduction of N-measured structural space. Discrete coordinates of the multi-measured vector of this space are: flathierarchical sets of languages, objects, problems, estimations, resources, restrictions, types of working areas, etc. The vectors' co-ordinates define the set of the program projects. The possibility of choice of the optimal program strategies' realization is discussed. The concept can be applied to objects of arbitrary purpose. (shrink)

Within the scope of multifactor approach, the ways and peculiarities of nonlinear paradigm formation and its establishment, as well as, multidimensional nature and essence of things in a single research field of the interdisciplinary science that is directly based on the perception of multidimensional integrity of the real world were significantly reconsidered. The idea of multifactorness is deployed as the scientific and logical basis for the methodology of scientific and philosophical research. In the course of investigation, it was revealed that (...) the contemporary level of understanding the notional diversity of "multidimensionality" concentrates within the conceptual structures of nonlinear thinking style considering the principle of oriental world-perception and world-understanding. (shrink)

The aim of this paper is to demonstrate the applicability of multivariate mapping techniques to the exploration of the rhetorical semiotic brand image structure of ad films. By drawing on correspondence analysis and multidimensional scaling, two techniques that are amply used in corpus linguistics and in marketing research, but also on the data reduction technique of factor analysis, it will be displayed how a set of nuclear semes and classemes or an intended semic structure that underlies ad filmic discursive structures (...) may be projected alongside rhetorical figures by its internal stakeholders (i.e., a brand management team, an account planning team or a marketing research team) with view to attaining differential brand associations. The illustration of the exploratory analytical methods takes place by recourse to a corpus of 62 ad filmic texts from 13 sub-brands of the 3 major brands in the UK cereals market and 321 ad filmic segments that resulted from the segmentation procedure. This paper seeks to contribute to advancements in the brand image and advertising rhetoric research streams by addressing distinctive modes of advertising rhetorical configuration at the level of the ad filmic text (as against print ads where the bulk of research on advertising rhetoric has concentrated), and moreover on a segment-by-segment level, rather than treating the ad film as a standalone unit of analysis, by adopting a multimodal outlook to advertising configurations that takes into account not only the verbal or the visual mode, but also interactions among modes, by adopting a product category-specific approach to advertising/brand textuality that draws on rhetorical semiotics and by employing exploratory statistical methods against the background of content analytic output. (shrink)