Colours sometimes count: awareness and bidirectionality in grapheme-colour synaesthesia

Three experiments were conducted with 10 grapheme-colour synaesthetes and 10 matched controls to investigate (a) whether awareness of the inducer grapheme is necessary for synaesthetic colour induction and (b) whether grapheme-colour synaesthesia may be bidirectional in the sense that not only do graphemes induce colours, but that colours influence the processing of graphemes. Using attentional blink and Stroop paradigms with digit targets, we found that some synaesthetes did report “seeing” synaesthetic colours even when they were not able to report the inducing digit. Moreover, congruency effects (effects of matching the colour of digit presentation with the synaesthetic colour associated with that digit) suggested that grapheme-colour synaesthesia can be bidirectional, at least for some synaesthetes.     

Isolating automatic photism generation from strategic photism use in grapheme-colour synaesthesia

Grapheme-colour synaesthesia is a phenomenon in which ordinary black numbers and letters (graphemes) trigger the experience of highly specific colours (photisms). The Synaesthetic Stroop task has been used to demonstrate that graphemes trigger photisms automatically. In the standard Stroop task, congruent trial probability (CTP) has been manipulated to isolate effects of automaticity from higher-order strategic effects, with larger Stroop effects at high CTP attributed to participants strategically attending to the stimulus word to facilitate responding, and smaller Stroop effects at low CTP reflecting automatic word processing. Here we apply this logic for the first time to the Synaesthetic Stroop task. At high CTP we showed larger Stroop effects due to synaesthetes using their synaesthetic colours strategically. At low CTP Stroop effects were reduced but were still significant. We directly isolate automatic processing of graphemes from strategic effects and conclusively show that, in synaesthesia, viewing black graphemes automatically triggers colour experiences.     

Varieties of grapheme-colour synaesthesia: a new theory of phenomenological and behavioural differences

Recent research has suggested that not all grapheme-colour synaesthetes are alike. One suggestion is that they can be divided, phenomenologically, in terms of whether the colours are experienced in external or internal space (projector–associator distinction). Another suggestion is that they can be divided according to whether it is the perceptual or conceptual attributes of a stimulus that is critical (higher–lower distinction). This study compares the behavioural performance of 7 projector and 7 associator synaesthetes. We demonstrate that this distinction does not map on to behavioural traits expected from the higher–lower distinction. We replicate previous research showing that projectors are faster at naming their synaesthetic colours than veridical colours, and that associators show the reverse profile. Synaesthetes who project colours into external space but not on to the surface of the grapheme behave like associators on this task. In a second task, graphemes presented briefly in the periphery are more likely to elicit reports of colour in projectors than associators, but the colours only tend to be accurate when the grapheme itself is also accurately identified. We propose an alternative model of individual differences in grapheme-colour synaesthesia that emphasises the role of different spatial reference frames in synaesthetic perception. In doing so, we attempt to bring the synaesthesia literature closer to current models of non-synaesthetic perception, attention and binding.     

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.     

Intermodal sensory image generation: An fMRI analysis

Although both imagery and perception may be related to more than one sensory input, and information coming from different sensory channels is often integrated in a unique mental representation, most recent neuroimaging literature has focused on visual imaging. Contrasting results have been obtained concerning the sharing of the same mechanisms by visual perception and visual imagery, in part due to assessment techniques and to interindividual variability in brain activation. In recent years, an increasing number of researchers have adopted novel neuroimaging techniques in order to investigate intermodal connections in mental imagery and have reported a high degree of interaction between mental imagery and other cognitive functions. In the present study the specific nature of mental imagery was investigated by means of fMRI on a more extensive set of perceptual experiences (shapes, sounds, touches, odours, flavours, self‐perceived movements, and internal sensations). Results show that the left middle‐inferior temporal area is recruited by mental imagery for all modalities investigated and not only for the visual one, while parietal and prefrontal areas exhibit a more heterogeneous pattern of activation across modalities. The prominent left lateralisation observed for almost all the conditions suggests that verbal cues affect the processes underlying the generation of images.     

Fitting computational models to fMRI data

Many computational models in psychology predict how neural activation in specific brain regions should change during certain cognitive tasks. The emergence of fMRI as a research tool provides an ideal vehicle totest these predictions. Before such tests are possible, however, significant methodological problems must be solved. These problems include transforming the neural activations predicted by the model into predicted BOLD responses, identifying the voxels within each region of interest against which to test the model, and comparing the observed and predicted BOLD responses in each of these regions. In the present article, methods are described for solving each of these problems.     

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.     

An exploratory study of syncretic experience: eidetics, synaesthesia and absorption.

Synaesthesia and eidetic imagery are both syncretic experiences entailing a dedifferentiation of perceptual qualities. In this paper the correlation between synaesthesia and eidetic imagery is explored. Ten subjects selected for possible eidetic and/or synaesthetic ability were tested in a battery of tasks that tap structural and typographic eidetic imagery, and colour-hearing and colour-mood synaesthesia. It was found that both structural and typographic eidetic imagery were correlated with measures of synaesthesia, indicating a relationship between the two phenomena.     

An fMRI investigation of posttraumatic flashbacks

Flashbacks are a defining feature of posttraumatic stress disorder (PTSD), but there have been few studies of their neural basis. We tested predictions from a dual representation model of PTSD that, compared with ordinary episodic memories of the same traumatic event, flashbacks would be associated with activity in dorsal visual stream and related areas rather than in the medial temporal lobe. Participants with PTSD, with depression but not PTSD, and healthy controls were scanned during a recognition task with personally relevant stimuli. The contrast of flashbacks versus ordinary episodic trauma memories in PTSD was associated with increased activation in sensory and motor areas including the insula, precentral gyrus, supplementary motor area, and mid-occipital cortex. The same contrast was associated with decreased activation in the midbrain, parahippocampal gyrus, and precuneus/posterior cingulate cortex. The results were discussed in terms of theories of PTSD and dual-process models of recognition.     

An fMRI study of imagined self-rotation

In the present study, functional magnetic resonance imaging was used to examine the neural mechanisms involved in the imagined spatial transformation of one’s body. The task required subjects to update the position of one of four external objects from memory after they had performed an imagined self-rotation to a new position. Activation in the rotation condition was compared with that in a control condition in which subjects located the positions of objects without imagining a change in selfposition. The results indicated similar networks of activation to other egocentric transformation tasks involving decisions about body parts. The most significant area of activation was in the left posterior parietal cortex. Other regions of activation common among several of the subjects were secondary visual, premotor, and frontal lobe regions. These results are discussed relative to motor and visual imagery processes as well as to the distinctions between the present task and other imagined egocentric transformation tasks.     

Principal component analysis of three-mode data by means of alternating least squares algorithms

A new method to estimate the parameters of Tucker's three-mode principal component model is discussed, and the convergence properties of the alternating least squares algorithm to solve the estimation problem are considered. A special case of the general Tucker model, in which the principal component analysis is only performed over two of the three modes is briefly outlined as well. The Miller & Nicely data on the confusion of English consonants are used to illustrate the programs TUCKALS3 and TUCKALS2 which incorporate the algorithms for the two models described.     

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.     

An MCMI-II item-level component analysis: personality and clinical factors

The Millon Clinical Multiaxial Inventory, Version 2 (MCMI-II) was released to replace the MCMI-I. Research into the factor structure of the items of the MCMI-I showed components consistent with the underlying construction theory. No such work has been done with the new MCMI-II. For this study, we analyzed the personality disorder and clinical syndrome items across two subject samples. For 579 Veterans Administration patients and 492 normal college students, six personality factors were identified. The samples shared Hostility, Histrionic/Schizoid, Dependent, Compulsive, and a Sadistic variant. For the clinical syndrome items, eight factors were isolated for veterans and seven for normals. Depression, Alcohol Abuse, Drug Abuse, Crying, and Mania were shared factors. Most of the factors were found to be highly consistent with MCMI-II scale keyings.     

An example of cooperating compact data analysis programs

Some user-oriented compact data analysis programs are described. One program is useful for transforming and reformatting data, and the others perform analysis of variance and multiple regression. Along with other programs not described here, these form an adequate statistical package without sacrificing ease of use or computational power.