52 research outputs found
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The hollow of being: What can we learn from Merleau-Ponty's ontology for a science of consciousness?
Representative for contemporary attempts to establish a science of consciousness we examine Chalmers' statement and resolution of the "hard problem of consciousness". Agreeing with him that in order to account for subjectivity it is necessary to expand the ontology of the natural sciences, we argue that it is not sufficient to just add conscious experience to the list of fundamental features of the world. Instead, we turn to phenomenology as the philosophy of conscious experience and give an outline of Merleau-Ponty's critique of the objectivist ontology underlying science which excludes subjectivity from the world. We reconstruct his proposal for a revised ontology in The Visible and the Invisible aiming at an extended understanding of Being including subjectivity, which takes on the form of a constellation of new ontological terms centered around the concept of the "flesh of the world". Trying to spell out the consequences of Merleau-Ponty's ontological considerations for scientific practice and especially the science of consciousness, we notice that his philosophy of subjectivity-in-the-world on its part is unable to connect to the insights of the natural sciences. The phenomenological critique of the "hard problem" reveals a deeper disparity which, at present, limits its practical implications
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Inverse transformed encoding models - A solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding
Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the measured signal using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates are highly variable and serially correlated due to the temporally extended shape of the hemodynamic response function (HRF). Here, we describe inverse transformed encoding modelling (ITEM), a principled approach of accounting for those serial correlations and decoding from the resulting estimates, at low computational cost and with no loss in statistical power. We use simulated data to show that ITEM outperforms the current standard approach in terms of decoding accuracy and analyze empirical data to demonstrate that ITEM is capable of visual reconstruction from fMRI signals
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Instantaneous oscillatory direction and phase for multivariate timeseries
This text describes a generalization of the analytic signal (Gabor, 1946) approach for the definition of instantaneous amplitude and phase to the case of multivariate signals. It was originally written as an appendix for another paper, where the determination of the locally dominant oscillatory direction (the instantaneous amplitude) described here is used as a preprocessing step for another kind of data analysis. The text is reproduced in a 'standalone' form because the procedure might prove useful in other contexts too, especially for the purpose of phase synchronization analysis (Rosenblum et al., 1996) between two (or more) multivariate sets of time series (Pascual-Marqui, 2007)
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MACS - a new SPM toolbox for model assessment, comparison and selection
Background: In cognitive neuroscience, functional magnetic resonance imaging (fMRI) data are widely analyzed using general linear models (GLMs). However, model quality of GLMs for fMRI is rarely assessed, in part due to the lack of formal measures for
statistical model inference.
New Method: We introduce a new SPM toolbox for model assessment, comparison and selection (MACS) of GLMs applied to fMRI data. MACS includes classical, information-theoretic and Bayesian methods of model assessment previously applied to GLMs for fMRI as well as recent methodological developments of model selection and model averaging in fMRI data analysis.
Results: The toolbox - which is freely available from GitHub - directly builds on the Statistical Parametric Mapping (SPM) software package and is easy-to-use, general-purpose, modular, readable and extendable. We validate the toolbox by reproducing model selection and model averaging results from earlier publications. Comparison with Existing Methods: A previous toolbox for model diagnosis in fMRI
has been discontinued and other approaches to model comparison between GLMs have not been translated into reusable computational resources in the past.
Conclusions: Increased attention on model quality will lead to lower false-positive rates in cognitive neuroscience and increased application of the MACS toolbox will increase the reproducibility of GLM analyses and is likely to increase the replicability of fMRI
studies
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Exceedance Probabilities for the Dirichlet Distribution
We derive an efficient method to calculate exceedance probabilities (EP) for the Dirichlet distribution when the number of event types is larger than two. Also, we present an intuitive application of Dirichlet EPs and compare our method to a sampling approach which is the current practice in neuroimaging model selection
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Kullback-Leibler Divergence for the Normal-Gamma Distribution
We derive the Kullback-Leibler divergence for the normal-gamma distribution and show that it is identical to the Bayesian complexity penalty for the univariate general linear model with conjugate priors. Based on this finding, we provide two applications of the KL divergence, one in simulated and one in empirical data
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How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection
Voxel-wise general linear models (GLMs) are a standard approach for analyzing functional magnetic resonance imaging (fMRI) data. An advantage of GLMs is that they are flexible and can be adapted to the requirements of many different data sets. However, the specification of first-level GLMs leaves the researcher with many degrees of freedom which is problematic given recent efforts to ensure robust and reproducible fMRI data analysis. Formal model comparisons that allow a systematic assessment of GLMs are only rarely performed. On the one hand, too simple models may underfit data and leave real effects undiscovered. On the other hand, too complex models might overfit data and also reduce statistical power. Here we present a systematic approach termed cross-validated Bayesian model selection (cvBMS) that allows to decide which GLM best describes a given fMRI data set. Importantly, our approach allows for non-nested model comparison, i.e. comparing more than two models that do not just differ by adding one or more regressors. It also allows for spatially heterogeneous modelling, i.e. using different models for different parts of the brain. We validate our method using simulated data and demonstrate potential applications to empirical data. The increased use of model comparison and model selection should increase the reliability of GLM results and reproducibility of fMRI studies
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Flicker-light induced visual phenomena: Frequency dependence and specificity of whole percepts and percept features
Flickering light induces visual hallucinations in human observers. Despite a long history of the phenomenon, little is known about the dependence of flicker-induced subjective impressions on the flicker frequency. We investigate this question using Ganzfeld stimulation and an experimental paradigm combining a continuous frequency scan (1–50 Hz) with a focus on re-occurring, whole percepts. On the single-subject level, we find a high degree of frequency stability of percepts. To generalize across subjects, we apply two rating systems, (1) a set of complex percept classes derived from subjects’ reports and (2) an enumeration of elementary percept features, and determine distributions of occurrences over flicker frequency. We observe a stronger frequency specificity for complex percept classes than elementary percept features. Comparing the similarity relations among percept categories to those among frequency profiles, we observe that though percepts are preferentially induced by particular frequencies, the frequency does not unambiguously determine the experienced percept
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View-Independent Working Memory Representations of Artificial Shapes in Prefrontal and Posterior Regions of the Human Brain
Traditional views of visual working memory postulate that memorized contents are stored in dorsolateral prefrontal cortex using an adaptive and flexible code. In contrast, recent studies proposed that contents are maintained by posterior brain areas using codes akin to perceptual representations. An important question is whether this reflects a difference in the level of abstraction between posterior and prefrontal representations. Here we investigated whether neural representations of visual working memory contents are view-independent, as indicated by rotation-invariance. Using fMRI and multivariate pattern analyses, we show that when subjects memorize complex shapes, both posterior and frontal brain regions maintain the memorized contents using a rotation-invariant code. Importantly, we found the representations in frontal cortex to be localized to the frontal eye fields rather than dorsolateral prefrontal cortices. Thus, our results give evidence for the view-independent storage of complex shapes in distributed representations across posterior and frontal brain regions
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Cortical specialization for attended versus unattended working memory
Items held in working memory can be either attended or not, depending on their current behavioral relevance. It has been suggested that unattended contents might be solely retained in an activity-silent form. Instead, we demonstrate here that encoding unattended contents involves a division of labor. While visual cortex only maintains attended items, intraparietal areas and the frontal eye fields represent both attended and unattended items
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