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.

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.

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function.

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.

Objects can be characterized according to a vast number of possible criteria (e.g. animacy, shape, color, function), but some dimensions are more useful than others for making sense of the objects around us. To identify these “core dimensions” of object representations, we developed a data-driven computational model of similarity judgments for real-world images of 1,854 objects. The model captured most explainable variance in similarity judgments and produced 49 highly reproducible and meaningful object dimensions that reflect various conceptual and perceptual properties of those objects. These dimensions predicted external categorization behavior and reflected typicality judgments of those categories. Further, humans can accurately rate objects along these dimensions, highlighting their interpretability and opening up a way to generate similarity estimates from object dimensions alone. Collectively, these results demonstrate that human similarity judgments can be captured by a fairly low-dimensional, interpretable embedding that generalizes to external behavior.

Perceptual confidence refers to the degree to which we believe in the accuracy of our percepts. Signal detection theory suggests that perceptual confidence is computed from an internal "decision variable," which reflects the amount of available information in favor of one or another perceptual interpretation of the sensory input. The neural processes underlying these computations have, however, remained elusive. Here, we used fMRI and multivariate decoding techniques to identify regions of the human brain that encode this decision variable and confidence during a visual motion discrimination task. We used observers' binary perceptual choices and confidence ratings to reconstruct the internal decision variable that governed the subjects' behavior. A number of areas in prefrontal and posterior parietal association cortex encoded this decision variable, and activity in the ventral striatum reflected the degree of perceptual confidence. Using a multivariate connectivity analysis, we demonstrate that patterns of brain activity in the right ventrolateral prefrontal cortex reflecting the decision variable were linked to brain signals in the ventral striatum reflecting confidence. Our results suggest that the representation of perceptual confidence in the ventral striatum is derived from a transformation of the continuous decision variable encoded in the cerebral cortex.

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.

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