An independent component analysis of fMRI data of grapheme-colour synaesthesia

The independent component analysis (ICA) method was applied to fMRI data from two synaesthetes and their matched controls while they performed the coloured-word Stroop task and the single-letter (synaesthetic) Stroop task. ICA identified an 'attention' network, a 'perceptual' network as well as a 'conflict monitoring' network. Increased activity was observed in right V4 during the single-letter Stroop task for synaesthetes only. The finding confirms that the same neural substrate that is known to support the experience of physical colours also supports the experience of synaesthetic colours.     

Toward a complete analysis of GSR data

A critique of amplitude analyses in GSR conditioning is discussed. It was pointed out that, when amplitudes are properly analyzed with all Ss contributing equally to each repeated measures point, the concern of latency interval, definition of response probability, and distribution of GSR amplitudes are concerns which apply to analyses of magnitude, probability, and latency, as well as of amplitude. It was concluded that nothing is lost in separating magnitude into its amplitude and probability components, particularly when magnitude, as well, is included in the statistical analyses, and that, to the contrary, we stand to learn something by such a partitioning which could not be learned without it.     

Multiple visualisation in data analysis: a ViSta application for principal component analysis

Multiple visualisation (MV) is a statistic graphical method barely applied in data analysis practice, even though it provides interesting features for this purpose. This paper: (1) describes the application of the MV graphical method; (2) presents a number of rules related to the design of an MV; (3) introduces a general outline for developing MVs and shows how MV may be implemented in the ViSta statistical system; (4) illustrates this strategy by means of an example of MV oriented to principal component analysis; and, finally, (5) discusses some limitations of using and developing MVs.     

Beyond mind-reading: multi-voxel pattern analysis of fMRI data

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.     

Implicit probabilistic sequence learning is independent of explicit awareness

Studies into interactions between explicit and implicit motor sequence learning have yielded mixed results. Some of these discrepancies have been attributed to difficulties in isolating implicit learning. In the present study, the effect of explicit knowledge on implicit learning was investigated using a modified version of the Alternating Serial Response Time (ASRT) task, a probabilistic sequence learning paradigm that yields continuous and relatively pure measures of implicit learning. Results revealed that implicit learning occurred to the same extent, whether or not subjects had explicit knowledge. Some evidence, however, indicated that explicit knowledge could interfere with the expression of implicit learning early in training. In addition, there were dissociations between learning measures, in that reaction time and accuracy were differentially affected by explicit knowledge. These findings indicate that implicit sequence learning occurs independently of explicit knowledge, and help to explain previous discrepant findings.     

Clusterwise simultaneous component analysis for analyzing structural differences in multivariate multiblock data

Many studies yield multivariate multiblock data, that is, multiple data blocks that all involve the same set of variables (e.g., the scores of different groups of subjects on the same set of variables). The question then rises whether the same processes underlie the different data blocks. To explore the structure of such multivariate multiblock data, component analysis can be very useful. Specifically, 2 approaches are often applied: principal component analysis (PCA) on each data block separately and different variants of simultaneous component analysis (SCA) on all data blocks simultaneously. The PCA approach yields a different loading matrix for each data block and is thus not useful for discovering structural similarities. The SCA approach may fail to yield insight into structural differences, since the obtained loading matrix is identical for all data blocks. We introduce a new generic modeling strategy, called clusterwise SCA, that comprises the separate PCA approach and SCA as special cases. The key idea behind clusterwise SCA is that the data blocks form a few clusters, where data blocks that belong to the same cluster are modeled with SCA and thus have the same structure, and different clusters have different underlying structures. In this article, we use the SCA variant that imposes equal average cross-products constraints (ECP). An algorithm for fitting clusterwise SCA-ECP solutions is proposed and evaluated in a simulation study. Finally, the usefulness of clusterwise SCA is illustrated by empirical examples from eating disorder research and social psychology.     

Kernel principal component analysis combining rotation forest method for linearly inseparable data

Rotation forest (RoF) is an ensemble classifier combining linear analysis theories and decision tree algorithms. In recent existing works, RoF was widely applied to various fields with outstanding performance compared to traditional machine learning techniques, given that a reasonable number of base classifiers is provided. However, the conventional RoF algorithm suffers from classifying linearly inseparable datasets. In this study, a hybrid algorithm integrating kernel principal component analysis (KPCA) and the conventional RoF algorithm is proposed to overcome the classification difficulty for linearly inseparable datasets. The radial basis function (RBF) is selected as the kernel for the KPCA method to establish the nonlinear mapping for linearly inseparable data. Moreover, we evaluate various kernel parameters for better performance. Experimental results show that our algorithm improves the performance of RoF with linearly inseparable datasets, and therefore provides higher classification accuracy rates compared with other ensemble machine learning methods.     

The neural correlates of reward-related trial-and-error learning: an fMRI study with a probabilistic learning task

This fMRI study investigated the neural correlates of reward-related trial-and-error learning in association with changing degrees of stimulus-outcome predictabilities. We found that decreasing predictability was associated with increasing activation in a frontoparietal network. Only maximum predictability was associated with signal decreases across the learning process. The receipt of monetary reward revealed activation in the striatum and associated frontoparietal regions. Present data indicate that during reward-related learning, high uncertainty forces areas relevant for cognitive control to remain activated. In contrast, learning on the basis of predictable stimulus-outcome associations enables the brain to reduce resources in association with the processes of prediction.     

Toward a taxonomy of attention shifting: Individual differences in fMRI during multiple shift types

Although task switching is often considered one of the fundamental abilities underlying executive functioning and general intelligence, there is little evidence that switching is a unitary construct and little evidence regarding the relationship between brain activity and switching performance. We examined individual differences in multiple types of attention shifting in order to determine whether behavioral performance and fMRI activity are correlated across different types of shifting. The participants (n=39) switched between objects and attributes both when stimuli were perceptually available (external) and when stimuli were stored in memory (internal). We found that there were more switchrelated activations in many regions associated with executive control—including the dorsolateral and medial prefrontal and parietal cortices—when behavioral switch costs were higher (poor performance). Conversely, activation in the ventromedial prefrontal cortex (VMPFC) and the rostral anterior cingulate was consistently correlated with good performance, suggesting a general role for these areas in efficient attention shifting. We discuss these findings in terms of a model of cognitive-emotional interaction in attention shifting, in which reward-related signals in the VMPFC guide efficient selection of tasks in the lateral prefrontal and parietal cortices.     

Reliability formulas for independent decision data when reliability data are matched

A distinction is made between reliability data and decision data. Each of these sets of data may be matched or independent, depending on whether the same instruments (tests, judges, etc.) are applied to every individual in the group or the instruments to be applied to each individual are selected independently for him. Reliability formulas are developed (for both single observations and for composites ofk observations) for the case where reliability data are matched but decision data are independent. Formulas previously reported in the literature are inappropriate for this case.This paper was prepared under USPHS Grant M-1839, in which the author is associated with Lee J. Cronbach and Goldine C. Gleser, and is an outgrowth of a more comprehensive conceptualization of reliability being developed in that study. The author is indebted to Dr. Frederic M. Lord for numerous suggestions.An unpublished paper prepared in 1958 entitled A generalization of Kuder-Richardson reliability formula 21.     

Toward a behavioral analysis of joint attention

Joint attention (JA) initiation is defined in cognitive-developmental psychology as a child's actions that verify or produce simultaneous attending by that child and an adult to some object or event in the environment so that both may experience the object or event together. This paper presents a contingency analysis of gaze shift in JA initiation. The analysis describes reinforcer-establishing and evocative effects of antecedent objects or events, discriminative and conditioned reinforcing functions of stimuli generated by adult behavior, and socially mediated reinforcers that may maintain JA behavior. A functional analysis of JA may describe multiple operant classes. The paper concludes with a discussion of JA deficits in children with autism spectrum disorders and suggestions for research and treatment.     

Toward a formal analysis of deceptive signaling

Deception has long been an important topic in philosophy (see Augustine in Treatises on various subjects, New York, Fathers of the Church, 1952; Kant in Practical philosophy, Cambridge University Press, Cambridge, 1996; Chisholm and Feehan in J Philos 74: 143–159, 1977; Mahon in Int J Appl Philos 21: 181–194, 2007; Carson in Lying and deception, Oxford University Press, New York, 2010). However, the traditional analysis of the concept, which requires that a deceiver intentionally cause her victim to have a false belief, rules out the possibility of much deception in the animal kingdom. Cognitively unsophisticated species, such as fireflies and butterflies, have simply evolved to mislead potential predators and/or prey. To capture such cases of “functional deception,” several researchers (e.g., Sober, From a biological point of view, Cambridge University Press, Cambridge, 1994; Hauser in: Whiten, Byrne (eds) Machiavellian intelligence II, Cambridge University Press, Cambridge, pp 112–143, 1997; Searcy and Nowicki, The evolution of animal communication, Princeton University Press, Princeton, 2005; Skyrms, Signals, Oxford University Press, Oxford 2010) have endorsed the broader view that deception only requires that a deceiver benefit from sending a misleading signal. Moreover, in order to facilitate game-theoretic study of deception in the context of Lewisian sender-receiver games, Brian Skyrms has proposed an influential formal analysis of this view. Such formal analyses have the potential to enhance our philosophical understanding of deception in humans as well as animals. However, as we argue in this paper, Skyrms’s analysis, as well as two recently proposed alternative analyses (viz., Godfrey-Smith in Review of signals: evolution, learning, and information by Brian Skyrms, Mind, 120: 1288–1297, 2001; McWhirter in Brit J Philos Sci 67: 757–780, 2016), are seriously flawed and can lead us to draw unwarranted conclusions about deception.