Martin N. Hebart

Decoding the Contents of Visual Short-Term Memory from Human Visual and Parietal Cortex

How content is stored in the human brain during visual short-term memory (VSTM) is still an open question. Different theories postulate storage of remembered stimuli in prefrontal, parietal, or visual areas. Aiming at a distinction between these theories, we investigated the content-specificity of BOLD signals from various brain regions during a VSTM task using multivariate pattern classification. To participate in memory maintenance, candidate regions would need to have information about the different contents held in memory. We identified two brain regions where local patterns of fMRI signals represented the remembered content. Apart from the previously established storage in visual areas, we also discovered an area in the posterior parietal cortex where activity patterns allowed us to decode the specific stimuli held in memory. Our results demonstrate that storage in VSTM extends beyond visual areas, but no frontal regions were found. Thus, while frontal and parietal areas typically coactivate during VSTM, maintenance of content in the frontoparietal network might be limited to parietal cortex.

Breaking Continuous Flash Suppression: A New Measure of Unconscious Processing during Interocular Suppression?

Until recently, it has been thought that under interocular suppression high-level visual processing is strongly inhibited if not abolished. With the development of continuous flash suppression (CFS), a variant of binocular rivalry, this notion has now been challenged by a number of reports showing that even high-level aspects of visual stimuli, such as familiarity, affect the time stimuli need to overcome CFS and emerge into awareness. In this “breaking continuous flash suppression” (b-CFS) paradigm, differential unconscious processing during suppression is inferred when (a) speeded detection responses to initially invisible stimuli differ, and (b) no comparable differences are found in non-rivalrous control conditions supposed to measure non-specific threshold differences between stimuli. The aim of the present study was to critically evaluate these assumptions. In six experiments we compared the detection of upright and inverted faces. We found that not only under CFS, but also in control conditions upright faces were detected faster and more accurately than inverted faces, although the effect was larger during CFS. However, reaction time (RT) distributions indicated critical differences between the CFS and the control condition. When RT distributions were matched, similar effect sizes were obtained in both conditions. Moreover, subjective ratings revealed that CFS and control conditions are not perceptually comparable. These findings cast doubt on the usefulness of non-rivalrous control conditions to rule out non-specific threshold differences as a cause of shorter detection latencies during CFS. Thus, at least in its present form, the b-CFS paradigm cannot provide unequivocal evidence for unconscious processing under interocular suppression. Nevertheless, our findings also demonstrate that the b-CFS paradigm can be fruitfully applied as a highly sensitive device to probe differences between stimuli in their potency to gain access to awareness.

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns.

Rapid Fear Detection Relies on High Spatial Frequencies

Signals of threat—such as fearful faces—are processed with priority and have privileged access to awareness. This fear advantage is commonly believed to engage a specialized subcortical pathway to the amygdala that bypasses visual cortex and processes predominantly low-spatial-frequency information but is largely insensitive to high spatial frequencies. We tested visual detection of low- and high-pass-filtered fearful and neutral faces under continuous flash suppression and sandwich masking, and we found consistently that the fear advantage was specific to high spatial frequencies. This demonstrates that rapid fear detection relies not on low- but on high-spatial-frequency information—indicative of an involvement of cortical visual areas. These findings challenge the traditional notion that a subcortical pathway to the amygdala is essential for the initial processing of fear signals and support the emerging view that the cerebral cortex is crucial for the processing of ecologically relevant signals.

What Visual Information Is Processed in the Human Dorsal Stream

The idea of a division between a dorsal and a ventral visual stream is one of the most basic principles of visual processing in the brain ([Milner and Goodale, 1995][1]). The ventral stream originates in primary visual cortex and extends along the ventral surface into the temporal cortex; the dorsal

Mesolimbic confidence signals guide perceptual learning in the absence of external feedback

It is well established that learning can occur without external feedback, yet normative reinforcement learning theories have difficulties explaining such instances of learning. Here, we propose that human observers are capable of generating their own feedback signals by monitoring internal decision variables. We investigated this hypothesis in a visual perceptual learning task using fMRI and confidence reports as a measure for this monitoring process. Employing a novel computational model in which learning is guided by confidence-based reinforcement signals, we found that mesolimbic brain areas encoded both anticipation and prediction error of confidence—in remarkable similarity to previous findings for external reward-based feedback. We demonstrate that the model accounts for choice and confidence reports and show that the mesolimbic confidence prediction error modulation derived through the model predicts individual learning success. These results provide a mechanistic neurobiological explanation for learning without external feedback by augmenting reinforcement models with confidence-based feedback. DOI: http://dx.doi.org/10.7554/eLife.13388.001

Analyzing neuroimaging data with subclasses: A shrinkage approach

Among the numerous methods used to analyze neuroimaging data, Linear Discriminant Analysis (LDA) is commonly applied for binary classification problems. LDAs popularity derives from its simplicity and its competitive classification performance, which has been reported for various types of neuroimaging data. Yet the standard LDA approach proves less than optimal for binary classification problems when additional label information (i.e. subclass labels) is present. Subclass labels allow to model structure in the data, which can be used to facilitate the classification task. In this paper, we illustrate how neuroimaging data exhibit subclass labels that may contain valuable information. We also show that the standard LDA classifier is unable to exploit subclass labels. We introduce a novel method that allows subclass labels to be incorporated efficiently into the classifier. The novel method, which we call Relevance Subclass LDA (RSLDA), computes an individual classification hyperplane for each subclass. It is based on regularized estimators of the subclass mean and uses other subclasses as regularization targets. We demonstrate the applicability and performance of our method on data drawn from two different neuroimaging modalities: (I) EEG data from brain-computer interfacing with event-related potentials, and (II) fMRI data in response to different levels of visual motion. We show that RSLDA outperforms the standard LDA approach for both types of datasets. These findings illustrate the benefits of exploiting subclass structure in neuroimaging data. Finally, we show that our classifier also outputs regularization profiles, enabling researchers to interpret the subclass structure in a meaningful way. RSLDA therefore yields increased classification accuracy as well as a better interpretation of neuroimaging data. Since both results are highly favorable, we suggest to apply RSLDA for various classification problems within neuroimaging and beyond.