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)

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)

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)

Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

BackgroundKinarm Standard Tests is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior. We have previously used principal component analysis to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab. Here, we performed PCA using data from the Kinarm Exoskeleton Lab, and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether further (...) dimensionality reduction was possible by using PCA across behavioral tasks.MethodsHealthy participants were assessed using the Kinarm EXO and EP. Four behavioral tasks were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching ] and three motor tasks [Visually Guided Reaching, Object Hit, and Object Hit and Avoid ]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type was decided on a per-task basis. To assess agreement, we compared principal components across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments.ResultsBy applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58–75% while accounting for 76–87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance.ConclusionPCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations. (shrink)

BackgroundDepression is a prevalent condition that has a significant impact on psychosocial functioning and quality of life. The onset and persistence of depression have been linked to a variety of biological and psychosocial variables. Many of these variables are associated with specific lifestyle characteristics, such as physical activity, diet, and sleep patterns. Some psychosocial determinants have an impact on people’ health-related behavior change. These include personal factors such as sense of coherence, patient activation, health literacy, self-efficacy, and procrastination. This study (...) aims to analyze the association between the severity of depression, lifestyle patterns, and personal factors related to health behavior. It also aims to analyze whether personal factors moderate the relationship between lifestyles and depression.MethodsThis study is a secondary data analysis of baseline data collected at the start of a randomized controlled trial. A sample of 226 patients with subclinical, mild, or moderate depression from primary healthcare centers in two sites in Spain was used, and descriptive, bivariate, multivariate, and moderation analyses were performed. Depression was the primary outcome, measured by Beck II Self-Applied Depression Inventory. Lifestyle variables such as physical exercise, adherence to Mediterranean diet and sleep quality, social support, and personal factors such as self-efficacy, patient activation in their own health, sense of coherence, health literacy, and procrastination were considered secondary outcomes.ResultsLow sense of coherence, poor sleep quality, low patient activation, and sedentarism are predictors of having more depressive symptoms. Moderation analyses were not significant.DiscussionLifestyle and personal factors are related to depressive symptomatology. Our findings reveal that sense of coherence, patient’s activation level, sedentarism, and sleep quality are associated with depression. Further research is needed regarding adherence to Mediterranean diet, minutes walking per week and the interrelationship between lifestyles, personal factors, and depression. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. (...) I argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

In sprint track events, the starting reaction time is an important professional capacity of the athletes, and it is closely related to their performance. This study examines the reaction time and the results of the male and female sprinters participating in the World Athletics Championships from 2011 to 2019 in the 100 m, 200 m, 100 m, and 110 m hurdles. The researchers used least squares estimation, multivariate analysis of variance, and other methods and theories to construct a (...) double logarithmic model and a multivariate analysis of a variance model. The researchers used Econometrics Views and SPSS software programs to analyze the correlation between the performance and the starting reaction time, as well as the patterns in the changes of the reaction time of athletes of both genders in different types of and rounds in the competitions. Research results show that there is a direct correlation between the reaction time and the performance, and the degrees of correlation vary depending on the gender of the athlete, year of competition, type of competition, and round of competition. There is a correlation between the foul types and the type of competition, but there is no correlation between foul types and the gender of the athlete. The research results are science-based and are of practical value and thus can be used as a reference by coaches in sprint running to offer more professional guidance to the athletes. (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)

Routine action sequences critically rely on neural mechanisms maintaining contextual and temporal information to disambiguate similar tasks (e.g. making coffee or tea). In this study we show the involvement of areas in temporal and lateral prefrontal cortices in maintaining temporal and contextual information for the execution of hierarchically‐organized action sequences.

Nowadays, football goalkeepers play an important role in the team's organization, namely, considering the offensive and defensive processes. The purpose of our investigation focuses on the notational and T-pattern analysis of the offensive and defensive actions of elite young football GKs. The participating GKs presented 8 years of experience in the specific position, were internationally selected for the national team of Portugal, and competed in the national U-17 championship of Portugal. Thirty football matches were observed. The observational sample (...) consisted of defensive actions and offensive actions. Two observational instruments were used to codify the actions: the observation system of defensive technical-tactical actions of GKs and the observation system of offensive technical-tactical actions of GKs. Both instruments underwent a validation process, and inter- and intra-observer reliability was tested. The codification of the actions was performed with the LINCE program, and later the data were exported to Microsoft Excel and THEME 5.0. The notational data were analyzed in SPSS, and T-pattern detection analysis was performed in THEME 5.0. The predominant actions of young observed GKs were fundamentally goal defense and participation in the team's offensive process construction through actions performed with the foot and hand. The analysis of T-patterns allowed to identify T-pattern actions in the defensive actions of goal defense and exit of the goal, as well as related to the defensive set pieces. In the offensive process, the analysis of T-patterns reinforced the participation of the GK in the team's first phase of construction and in the execution of goal kicks and actions that start as a result of the actions of the opponent. The GK's defensive actions are mostly focused on the objective of goal defense and offensive actions with the hands and feet are important for their participation in positional attack construction. Our study contributes to a better knowledge of the GK's actions in the competition and is relevant to be considered by the specific position coaches in the training process organization. (shrink)

Objective: To estimate the level of complementary feeding pattern among children aged between 6 to 23 months and to identify the determinants in individual, household and community level in Bangladesh. Methods: From secondary data of Bangladesh Demographic Health Survey 2011 was used in this study. A total of 2,373 children aged between 6 to 23 months were selected. To estimate the level of CFP “dimension index” was used and the score of the index was used as dependent variables. Statistical (...) analyses and tests were guided by the nature of the variables. Multivariable logistic regression analyses were performed to identify the significant determinants of CFP. Results: The overall level of CFP among children aged between 6 to 23 months was low. More than 95% of the children experienced either no or inadequate CFP. The mean levels of CFP as well as percentages of no or inadequate CFP were significantly lower among children of the youngest age group, uneducated parents, unemployed/laborer fathers, socio-economically poor families, food insecure families and rural areas. No weekly exposure to mass media also revealed significant associations with CFP. However, only few variables remained significant for adequate CFP in the multivariable logistic regression analysis. Adequate CFP was significantly lower among the children aged between 6 to 23 months, children of illiterate fathers and socio-economically middle-class families as compared to their reference categories. Conclusion: Inappropriate and inadequate CFP may cause serious health hazards among children of 6 to 23 months in Bangladesh. It is ethical to take effective interventions and strategies by the government and other concerned stakeholders to improve the overall situation of CFP in Bangladesh. (shrink)

Objective: To estimate the level of complementary feeding pattern (CFP) among children aged between 6 to 23 months and to identify the determinants in individual, household and community level in Bangladesh. Methods: From secondary data of Bangladesh Demographic Health Survey (BDHS) 2011 was used in this study. A total of 2,373 children aged between 6 to 23 months were selected. To estimate the level of CFP dimension index and the “score of the index” was used as dependent variables. Statistical (...) analyses and tests were guided by the nature of the variables. Multivariable logistic regression analyses were performed to identify the significant determinants of CFP. Results: The overall level of CFP among children aged between 6 to 23 months was low. More than 95% of the children experienced inadequate (92.7%) CFP level. The mean levels of CFP as well as percentages of no or inadequate (94.1%) CFP were significantly lower among children of the youngest age group (06 months), uneducated parents, unemployed/laborer fathers, socio-economically poor families, food insecure families and rural areas. However, only few variables remained significant for adequate CFP in the multivariable logistic regression analysis. Adequate CFP was significantly lower among the children aged between 6 to 23 months (OR: 0.22, 95% CI: 0.10-0.47), children of illiterate fathers (OR: 0.32, 95% CI: 0.11-0.95) and socio-economically middle-class families (OR: 0.28, 95% CI: 0.09-0.86) as compared to their reference categories. Conclusion: Inappropriate and inadequate CFP may cause serious health hazards among children of 6 to 23 months in Bangladesh. It is ethical to take effective interventions and strategies by the government and other concerned stakeholders to improve the overall situation of CFP in Bangladesh. (shrink)

Recent developments in pattern analysis research have made this methodology suitable for the study of the processes that are set in motion in psychological interventions. Outcome research, based on the comparison between clinical results from treatment and control groups, has leveraged our empirical knowledge about the efficacy of psychological interventions. However, these methods of research are not precise enough for the analysis of these processes. On the contrary, pattern analysis could be a powerful tool to (...) study moment-to-moment interactions typical of psychological interventions. This is methodology is relevant because clinical psychology is experiencing a paradigm shift from a protocol for syndrome perspective to a principle-based and person-centered intervention. This evidence-based, theory-grounded, and process-oriented paradigm of clinical intervention needs new research methods to thrive. The analysis of the therapeutic relationship built into the verbal interaction between the clinician and the client is one of the cornerstones of this new era of research. So, the purpose of this article is three-fold: to discuss the role of the verbal interaction pattern analysis in the clinical context to the development of the principle-based clinical psychology, to analyze the patterns of verbal interaction in a clinical case, and to compare the results using two different methods. To reach these purposes, using the observational methodology, we have coded the verbal interaction of 16 clinical sessions with a person diagnosed with a borderline personality disorder. We have analyzed the data using sequential analysis and pattern recognition algorithms. We have been able to detect typical patterns during different phases of psychological intervention. Finally, the conceptual, methodological, and empirical implications of this study will be discussed within the realms of pattern analysis research and principle-based clinical psychology. (shrink)

Multi-voxel pattern analysis (MVPA) is a popular analytical technique in neuroscience that involves identifying patterns in fMRI BOLD signal data that are predictive of task conditions. But the technique is also frequently used to make inferences about the regions of the brain that are most important to the tasks in question, and our analysis shows that this is a mistake. MVPA does not provide a reliable guide to what information is being used by the brain during cognitive (...) tasks, nor where that information is. This is due in part to inherent run to run variability in the decision space generated by the classifier, but there are also several other issues, discussed below, that make inference from the characteristics of the learned models to relevant brain activity deeply problematic. These issues have significant implications both for many papers already published, and for how the field uses this technique in the future. (shrink)

Humans are equipped with the remarkable ability to comprehend an infinite number of utterances. Relations between grammatical categories restrict the way words combine into phrases and sentences. How the brain recognizes different word combinations remains largely unknown, although this is a necessary condition for combinatorial unboundedness in language. Here, we used functional magnetic resonance imaging and multivariate pattern analysis to explore whether distinct neural populations of a known language network hub—Broca’s area—are specialized for recognizing distinct simple word (...) combinations. The phrases consisted of a noun (flag) occurring either with a content word, an adjective (green flag), or with a function word, a determiner (that flag). The key result is that the distribution of neural populations classifying word combination in Broca’s area seems sensitive to neuroanatomical subdivisions within this area, irrespective of task. The information patterns for adjective + noun were localized in its anterior part (BA45) whereas those for determiner + noun were localized in its posterior part (BA44). Our findings provide preliminary answers to the fundamental question of how lexical and grammatical category information interact during simple word combination, with the observation that Broca’s area is sensitive to the recognition of categorical relationships during combinatory processing, based on different demands placed on syntactic and semantic information. This supports the hypothesis that the combinatorial power of language consists of some neural computation capturing phrasal differences when processing linguistic input. (shrink)

The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate (...) class='Hi'>pattern analysis and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis. I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain. (shrink)

ArgumentIn the United States, scientific knowledge is brought before the courts by way of testimony – the testimony of scientific experts. We argue that this expertise is best understoodfirstas related to the quality of the underlying scienceand thenin terms of who delivers it. Bloodstain pattern analysis (BPA), a contemporary forensic science, serves as the vaulting point for our exploration of objectivity as a metric for the quality of a science in judicial contexts. We argue that BPA fails to (...) meet the minimal standard set by Helen Longino’s social-procedural account of objectivity (1990, 2002). In light of some pressing issues for social-procedural accounts, we offer an infrastructural account of objectivity. This account offers what amounts to a friendly amendment to Longino’s account and adds to the ways in which we might analyze social-procedural objectivity. Finally, we address an issue that is pressing in the legal context: given that scientific knowledge is delivered by individuals, not communities, at least in U.S. courts, we (may) need a way to evaluate individual scientific and epistemic agents. We suggest a means for making this evaluation that is derived from our infrastructural account of objectivity. (shrink)

Research in evolutionary biology and philosophy of biology and cognition strongly suggests that human organisms modify their environment through active processes of niche construction. Recently, proponents of the free-energy principle and variational active inference have argued that their approach can deepen our understanding of the reciprocal causal relationship between organisms and their niche on various scales. This paper examines the feasibility and scope of variational formalisations and conceptualisations of the organism-niche nexus with a particular focus on the extended active inference (...) account. I will draw a conceptual distinction between selective niche construction, developmental niche construction, and organism-niche coordination dynamics and argue that these notions capture different causal patterns, each of which with a distinct scope. Against this background, I will analyse and discuss the extended active inference account and its strategy to integrate variational active inference with work on extended cognition. The proponents of extended active inference assume that their account can provide an explanation of selective niche construction, developmental niche construction, and organism-niche coordination dynamics. However, my key claim will be that this account has the potential to elucidate the workings of organism-niche coordination dynamics, but does not adequately capture the causal patterns of selective niche construction and developmental niche construction. (shrink)

Child trauma plays an important role in the etiology of Bordeline Personality Disorder. Of all traumas, sexual trauma is the most common, severe and most associated with receiving a BPD diagnosis when adult. Etiologic models posit sexual abuse as a prognostic factor in BPD. Here we apply machine learning using Multiple Kernel Regression to the Magnetic Resonance Structural Images of 20 BPD and 13 healthy control to see whether their brain predicts five sources of traumas: sex abuse, emotion neglect, emotional (...) abuse, physical neglect, physical abuse. We also applied the same analysis to predict symptom severity in five domains: affective, cognitive, impulsivity, interpersonal for BPD patients only. Results indicate that CTQ sexual trauma is predicted by a set of areas including the amygdala, the Heschl area, the Caudate, the Putamen, and portions of the Cerebellum in BPD patients only. Importantly, interpersonal problems only in BPD patients were predicted by a set of areas including temporal lobe and cerebellar regions. Notably, sexual trauma and interpersonal problems were not predicted by structural features in matched healthy controls. This finding may help elucidate the brain circuit affected by traumatic experiences and connected with interpersonal problems BPD suffer from. (shrink)

The primary argument against Rachlin's act-versus-pattern analysis of self-control is that it is wrong to think of a temptation as a solitary act while the alternative is conceived of as an element of a pattern. Either both are solitary acts or both are members of patterns, however different the patterns may be in their complexity and abstractness.

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)

Emotions change over time. A comprehensive understanding of emotions will require that their temporal nature be observed and analysed. By observing emotion over time, one can disentangle and simultaneously analyse temporal variability within individuals and between-individual variability using a two-step process approach. First, within-person temporal patterns (e.g., covariation, lead–lag relation, periodicity, etc.) are assessed for each subject. Second, between-person analyses are conducted on the within-person patterns. These two steps can be done simultaneously with hierarchical linear models (HLM) or in two (...) actual steps with the process approach. HLM is limited to the intra-individual analysis of linear patterns (e.g., slope, intercept), whereas the process approach can be used to examine non-linear aspects of intra-individual change, such as multivariate patterns, within-subject skew, or phase relations between oscillating processes. In this paper we provide a description of the process approach and present several examples of, as well as suggestions for, intra-individual analysis applied to emotion and individual differences. (shrink)

Purpose This study aims to statistically verify the links of income and education with information and communication technology diffusion across 191 countries of the world taking into account a total of 9 indicators best representing the socio-economic variables. Design/methodology/approach Multivariate regression analysis was used as a prime method to rigorously test the relationships of income and education with ICT diffusion across 191 countries. Statistical Package for the Social Sciences was used to analyze and predict patterns in the data. (...) Findings The results support the hypotheses that income and education are positively related to ICT diffusion. The findings statistically confirm that poverty is a leading cause of digital divide worldwide. Research limitations/implications Academic, policy and practice groups should work in collaboration to fight against digital divide. Present results also imply that digital divide shall never end but rather it can be controlled to an extent with multiple collaborative efforts. Originality/value Prior research assignments on the digital divide concentrate on exploring the links between few socio-economic and ICT variables in select few regions theoretically. The present work addresses this literature gap by developing and testing two hypotheses to statistically investigate the relationships between a broad set of socio-economic and ICT indicators. (shrink)

A hypnotic induction produces different patterns of spontaneous experiences across individuals. The magnitude and characteristics of these responses covary moderately with hypnotic suggestibility, but also differ within levels of hypnotic suggestibility. This study sought to identify discrete phenomenological profiles in response to a hypnotic induction and assess whether experiential variability among highly suggestible individuals matches the phenomenological profiles predicted by dissociative typological models of high hypnotic suggestibility. Phenomenological state scores indexed in reference to a resting epoch during hypnosis were submitted (...) to a latent profile analysis. The profiles in the derived four-class solution differed in multiple experiential dimensions and hypnotic suggestibility. Highly suggestible individuals were distributed across two classes that exhibited response patterns suggesting an inward attention subtype and a dissociative subtype. These results provide support for dissociative typological models of high hypnotic suggestibility and indicate that highly suggestible individuals do not display a uniform response to a hypnotic induction. (shrink)

A shortage or backlog of inventory can easily occur due to the backward forecasting method typically used, which will affect the normal flow of funds in pharmacies. This paper proposes a replenishment decision model with back propagation neural network multivariate regression analysis methods. With the regular pattern between sales and individual variables, supplemented with the safety stock empirical formula, an accurate replenishment quantity can be obtained. In the case analysis, this paper takes the sales situation of (...) a pharmacy as an example and tests the accuracy and stability of the model. The results show that the model has good prediction accuracy which can be introduced into the intelligent pharmacy system and used in the replenishment of the intelligent pharmacy to prevent overstocking or a shortage of stock, thus improving the financial situation, reducing the manpower burden of typical retail pharmacy, and helping residents buy medicines. (shrink)