Muttenthaler, L. & Hebart, M.N.2021 , Frontiers in Neuroinformatics , Volume: 15 , pages: 45
Over the past decade, deep neural network (DNN) models have received a lot of attention due to their near-human object classification performance and their excellent prediction of signals recorded from biological visual systems. To better understand the function of these networks and relate them to hypotheses about brain activity and behavior, researchers need to extract the activations to images across different DNN layers. The abundance of different DNN variants, however, can often be unwieldy, and the task of extracting DNN activations from different layers may be non-trivial and error-prone for someone without a strong computational background. Thus, researchers in the fields of cognitive science and computational neuroscience would benefit from a library or package that supports a user in the extraction task. THINGSvision is a new Python module that aims at closing this gap by providing a simple and unified tool for extracting layer activations for a wide range of pretrained and randomly-initialized neural network architectures, even for users with little to no programming experience. We demonstrate the general utility of THINGsvision by relating extracted DNN activations to a number of functional MRI and behavioral datasets using representational similarity analysis, which can be performed as an integral part of the toolbox. Together, THINGSvision enables researchers across diverse fields to extract features in a streamlined manner for their custom image dataset, thereby improving the ease of relating DNNs, brain activity, and behavior, and improving the reproducibility of findings in these research fields.
Liu, P., Chrysidou, A, Doehler, J, Hebart, M.N., Wolbers, T, & Kuehn, E.2021 , eLife , Volume: 10 , pages: e60090
Topographic maps are a fundamental feature of cortex architecture in the mammalian brain. One common theory is that the de-differentiation of topographic maps links to impairments in everyday behavior due to less precise functional map readouts. Here, we tested this theory by characterizing de-differentiated topographic maps in primary somatosensory cortex (SI) of younger and older adults by means of ultra-high resolution functional magnetic resonance imaging together with perceptual finger individuation and hand motor performance. Older adults’ SI maps showed similar amplitude and size to younger adults’ maps, but presented with less representational similarity between distant fingers. Larger population receptive field sizes in older adults’ maps did not correlate with behavior, whereas reduced cortical distances between D2 and D3 related to worse finger individuation but better motor performance. Our data uncover the drawbacks of a simple de-differentiation model of topographic map function, and motivate the introduction of feature-based models of cortical reorganization.
Singer, J., Seeliger, K., Kietzmann, T.C., & Hebart, M.N.2021 , PsyArXiv
Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photos, drawings and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images such as drawings seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network.
Singer, J., Seeliger, K., & Hebart, M.N.2020 , NeurIPS Workshop SVRHM
Drawings are universal in human culture and serve as tools to efficiently convey meaning with little visual information. Humans are adept at recognizing even highly abstracted drawings of objects, and their visual system has been shown to respond similarly to different object depictions. Yet, the processing of object drawings in deep convolutional neural networks (CNNs) has yielded conflicting results. While CNNs have been shown to perform poorly on drawings, there is evidence that representations in CNNs are similar for object photographs and drawings. Here, we resolve these disparate findings by probing the generalization ability of a CNN trained on natural object images for a set of photos, drawings and sketches of the same objects, with each depiction representing a different level of abstraction. We demonstrate that despite poor classification performance on drawings and sketches, the network exhibits a similar representational structure across levels of abstraction in intermediate layers which, however, disappears in later layers. Further, we show that a texture bias found in CNNs contributes both to the poor classification performance for drawings and the dissimilar representational structure, specifically in the later layers of the network. By finetuning only those layers on a database of object drawings, we show that features in early and intermediate layers learned on natural object photographs are indeed sufficient for downstream recognition of drawings. Our findings reconcile previous investigations on the generalization ability of CNNs for drawings and reveal both opportunities and limitations of CNNs as models for the representation and recognition of drawings and sketches.
Hebart, M.N., Zheng, C.Y., Pereira, F., & Baker, C.I.2020 , Nature Human Behaviour , pages: 1173-1185
Objects can be characterized according to a vast number of possible criteria (such as animacy, shape, colour and 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 judgements for real-world images of 1,854 objects. The model captured most explainable variance in similarity judgements and produced 49 highly reproducible and meaningful object dimensions that reflect various conceptual and perceptual properties of those objects. These dimensions predicted external categorization behaviour and reflected typicality judgements of those categories. Furthermore, 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 judgements can be captured by a fairly low-dimensional, interpretable embedding that generalizes to external behaviour.
Hebart, M.N. & Schuck, N.W.2020 , Neuropsychologia
Computational Cognitive Neuroscience is a discipline at the intersection of psychology, neuroscience and artificial intelligence. At its core is the development and comparison of computational models that allow the prediction of behavior, cognition and brain activity, with the long-term goal of providing a neurophysiologically plausible characterization of the underlying brain structure or function (Ashby and Helie, 2011; Kriegeskorte and Douglas, 2018; Love, 2015; O’Reilly and Munakata, 2000). Fueled by recent developments with machine learning techniques that solve cognitive tasks such as object recognition, decision making, or language processing (Krizhevsky et al., 2012; Mikolov et al., 2013; Mnih et al., 2015), computational cognitive neuroscientists have started to link these artificial intelligence approaches to neural processes (Huth et al., 2016; Stachenfeld et al., 2017; Yamins et al., 2014). This, in turn, has led to applications of computational modeling in neuroscience that have become increasingly sophisticated. Today, the field is moving fast, and hardly a year goes by without discoveries that seem like a true expansion of our horizon. These exciting developments motivated us to bring to life this Special Issue on Computational Cognitive Neuroscience.
Bönstrup, M., Iturrate, I., Hebart, M.N., Censor, N., & Cohen, L.G.2020 , Science of Learning , pages: 1--10
Performance improvements during early human motor skill learning are suggested to be driven by short periods of rest during practice, at the scale of seconds. To reveal the unknown mechanisms behind these “micro-offline” gains, we leveraged the sampling power offered by online crowdsourcing (cumulative N over all experiments = 951). First, we replicated the original in-lab findings, demonstrating generalizability to subjects learning the task in their daily living environment (N = 389). Second, we show that offline improvements during rest are equivalent when significantly shortening practice period duration, thus confirming that they are not a result of recovery from performance fatigue (N = 118). Third, retroactive interference immediately after each practice period reduced the learning rate relative to interference after passage of time (N = 373), indicating stabilization of the motor memory at a microscale of several seconds. Finally, we show that random termination of practice periods did not impact offline gains, ruling out a contribution of predictive motor slowing (N = 71). Altogether, these results demonstrate that micro-offline gains indicate rapid, within-seconds consolidation accounting for early skill learning.
Hebart, M.N., Dickter, A.H., Kidder, A., Kwok, W.Y., Corriveau, A., Van Wicklin, C., & Baker, C.I.2019 , PloS one , pages: e0223792
In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.
Görgen, K., Hebart, M.N., Allefeld, C., & Haynes, J.-D.2018 , Neuroimage , pages: 19--30
Standard neuroimaging data analysis based on traditional principles of experimental design, modelling, and statistical inference is increasingly complemented by novel analysis methods, driven e.g. by machine learning methods. While these novel approaches provide new insights into neuroimaging data, they often have unexpected properties, generating a growing literature on possible pitfalls. We propose to meet this challenge by adopting a habit of systematic testing of experimental design, analysis procedures, and statistical inference. Specifically, we suggest to apply the analysis method used for experimental data also to aspects of the experimental design, simulated confounds, simulated null data, and control data. We stress the importance of keeping the analysis method the same in main and test analyses, because only this way possible confounds and unexpected properties can be reliably detected and avoided. We describe and discuss this Same Analysis Approach in detail, and demonstrate it in two worked examples using multivariate decoding. With these examples, we reveal two sources of error: A mismatch between counterbalancing (crossover designs) and cross-validation which leads to systematic below-chance accuracies, and linear decoding of a nonlinear effect, a difference in variance.
Hebart, M.N. & Baker, C.I.2018 , Neuroimage , pages: 4--18
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.
Hebart, M.N., Bankson, B.B., Harel, A., Baker, C.I.*, & Cichy, R.M.*2018 , Elife , pages: e32816
Despite the importance of an observer’s goals in determining how a visual object is categorized, surprisingly little is known about how humans process the task context in which objects occur and how it may interact with the processing of objects. Using magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) and multivariate techniques, we studied the spatial and temporal dynamics of task and object processing. Our results reveal a sequence of separate but overlapping task-related processes spread across frontoparietal and occipitotemporal cortex. Task exhibited late effects on object processing by selectively enhancing task-relevant object features, with limited impact on the overall pattern of object representations. Combining MEG and fMRI data, we reveal a parallel rise in task-related signals throughout the cerebral cortex, with an increasing dominance of task over object representations from early to higher visual areas. Collectively, our results reveal the complex dynamics underlying task and object representations throughout human cortex.
Bankson, B.B.*, Hebart, M.N.*, Groen, I.I.A., & Baker, C.I.2018 , NeuroImage , pages: 172--182
Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categorical or conceptual representations. Here, we aimed to estimate a lower temporal bound for the emergence of conceptual representations by defining two criteria that characterize such representations: 1) conceptual object representations should generalize across different exemplars of the same object, and 2) these representations should reflect high-level behavioral judgments. To test these criteria, we compared magnetoencephalography (MEG) recordings between two groups of participants (n = 16 per group) exposed to different exemplar images of the same object concepts. Further, we disentangled low-level from high-level MEG responses by estimating the unique and shared contribution of models of behavioral judgments, semantics, and different layers of deep neural networks of visual object processing. We find that 1) both generalization across exemplars as well as generalization of object-related signals across time increase after 150 ms, peaking around 230 ms; 2) representations specific to behavioral judgments emerged rapidly, peaking around 160 ms. Collectively, these results suggest a lower bound for the emergence of conceptual object representations around 150 ms following stimulus onset.
Hebart, M.N., Schriever, Y., Donner, T.H.*, & Haynes, J.-D.*2016 , Cerebral Cortex , pages: 118--130
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.
Höhne, J., Bartz, D., Hebart, M.N., Müller, K.-R., & Blankertz, B.2016 , NeuroImage , pages: 740--751
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.
Guggenmos, M., Wilbertz, G., Hebart, M.N.*, & Sterzer, P.*2016 , eLife , pages: e13388
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.
Korjus, K., Hebart, M.N., & Vicente, R.2016 , PloS one , pages: e0161788
Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with cross-validation. This is followed by application of the classifier with optimized parameters to a separate test set for estimating the classifier’s generalization performance. With limited data, this separation of test data creates a difficult trade-off between having more statistical power in estimating generalization performance versus choosing better parameters and fitting a better model. We propose a novel approach that we term “Cross-validation and cross-testing” improving this trade-off by re-using test data without biasing classifier performance. The novel approach is validated using simulated data and electrophysiological recordings in humans and rodents. The results demonstrate that the approach has a higher probability of discovering significant results than the standard approach of cross-validation and testing, while maintaining the nominal alpha level. In contrast to nested cross-validation, which is maximally efficient in re-using data, the proposed approach additionally maintains the interpretability of individual parameters. Taken together, we suggest an addition to currently used machine learning approaches which may be particularly useful in cases where model weights do not require interpretation, but parameters do.
Guo, R., Böhmer, W., Hebart, M., Chien, S., Sommer, T., Obermayer, K., & Gläscher, J.2016 , Journal of Neuroscience , pages: 12650--12660
Goal-directed and instrumental learning are both important controllers of human behavior. Learning about which stimulus event occurs in the environment and the reward associated with them allows humans to seek out the most valuable stimulus and move through the environment in a goal-directed manner. Stimulus–response associations are characteristic of instrumental learning, whereas response–outcome associations are the hallmark of goal-directed learning. Here we provide behavioral, computational, and neuroimaging results from a novel task in which stimulus–response and response–outcome associations are learned simultaneously but dominate behavior at different stages of the experiment. We found that prediction error representations in the ventral striatum depend on which type of learning dominates. Furthermore, the amygdala tracks the time-dependent weighting of stimulus–response versus response–outcome learning. Our findings suggest that the goal-directed and instrumental controllers dynamically engage the ventral striatum in representing prediction errors whenever one of them is dominating choice behavior.
Hebart, M.N. & Gläscher, J.2015 , Psychopharmacology , pages: 437--451
Human motivation and decision-making is influenced by the interaction of Pavlovian and instrumental systems. The neurotransmitters dopamine and serotonin have been suggested to play a major role in motivation and decision-making, but how they affect this interaction in humans is largely unknown. We investigated the effect of these neurotransmitters in a general Pavlovian-to-instrumental transfer (PIT) task which measured the nonspecific effect of appetitive and aversive Pavlovian cues on instrumental responses. For that purpose, we used selective dietary depletion of the amino acid precursors of serotonin and dopamine: tryptophan (n = 34) and tyrosine/phenylalanine (n = 35), respectively, and compared the performance of these groups to a control group (n = 34) receiving a nondepleted (balanced) amino acid drink. We found that PIT differed between groups: Relative to the control group that exhibited only appetitive PIT, we found reduced appetitive PIT in the tyrosine/phenylalanine-depleted group and enhanced aversive PIT in the tryptophan-depleted group. These results demonstrate a differential involvement of serotonin and dopamine in motivated behavior. They suggest that reductions in serotonin enhance the motivational influence of aversive stimuli on instrumental behavior and do not affect the influence of appetitive stimuli, while reductions in dopamine diminish the influence of appetitive stimuli. No conclusions could be drawn about how dopamine affects the influence of aversive stimuli. The interplay of both neurotransmitter systems allows for flexible and adaptive responses depending on the behavioral context.
Human motivation and decision-making is influenced by the interaction of Pavlovian and instrumental systems. The neurotransmitters dopamine and serotonin have been suggested to play a major role in motivation and decision-making, but how they affect this interaction in humans is largely unknown.
We investigated the effect of these neurotransmitters in a general Pavlovian-to-instrumental transfer (PIT) task which measured the nonspecific effect of appetitive and aversive Pavlovian cues on instrumental responses.
For that purpose, we used selective dietary depletion of the amino acid precursors of serotonin and dopamine: tryptophan (n = 34) and tyrosine/phenylalanine (n = 35), respectively, and compared the performance of these groups to a control group (n = 34) receiving a nondepleted (balanced) amino acid drink.
We found that PIT differed between groups: Relative to the control group that exhibited only appetitive PIT, we found reduced appetitive PIT in the tyrosine/phenylalanine-depleted group and enhanced aversive PIT in the tryptophan-depleted group.
These results demonstrate a differential involvement of serotonin and dopamine in motivated behavior. They suggest that reductions in serotonin enhance the motivational influence of aversive stimuli on instrumental behavior and do not affect the influence of appetitive stimuli, while reductions in dopamine diminish the influence of appetitive stimuli. No conclusions could be drawn about how dopamine affects the influence of aversive stimuli. The interplay of both neurotransmitter systems allows for flexible and adaptive responses depending on the behavioral context.
Christophel, T.B., Cichy, R.M., Hebart, M.N., & Haynes, J.-D.2015 , Neuroimage , pages: 198--206
Active and flexible manipulations of memory contents “in the mind's eye” are believed to occur in a dedicated neural workspace, frequently referred to as visual working memory. Such a neural workspace should have two important properties: The ability to store sensory information across delay periods and the ability to flexibly transform sensory information. Here we used a combination of functional MRI and multivariate decoding to indentify such neural representations. Subjects were required to memorize a complex artificial pattern for an extended delay, then rotate the mental image as instructed by a cue and memorize this transformed pattern. We found that patterns of brain activity already in early visual areas and posterior parietal cortex encode not only the initially remembered image, but also the transformed contents after mental rotation. Our results thus suggest that the flexible and general neural workspace supporting visual working memory can be realized within posterior brain regions.
Peth, J., Sommer, T., Hebart, M.N., Vossel, G., Büchel, C., & Gamer, M.2015 , NeuroImage , pages: 164--174
Recent research revealed that the presentation of crime related details during the Concealed Information Test (CIT) reliably activates a network of bilateral inferior frontal, right medial frontal and right temporal–parietal brain regions. However, the ecological validity of these findings as well as the influence of the encoding context are still unclear. To tackle these questions, three different groups of subjects participated in the current study. Two groups of guilty subjects encoded critical details either only by planning (guilty intention group) or by really enacting (guilty action group) a complex, realistic mock crime. In addition, a group of informed innocent subjects encoded half of the relevant details in a neutral context. Univariate analyses showed robust activation differences between known relevant compared to neutral details in the previously identified ventral frontal–parietal network with no differences between experimental groups. Moreover, validity estimates for average changes in neural activity were similar between groups when focusing on the known details and did not differ substantially from the validity of electrodermal recordings. Additional multivariate analyses provided evidence for differential patterns of activity in the ventral fronto-parietal network between the guilty action and the informed innocent group and yielded higher validity coefficients for the detection of crime related knowledge when relying on whole brain data. Together, these findings demonstrate that an fMRI-based CIT enables the accurate detection of concealed crime related memories, largely independent of encoding context. On the one hand, this indicates that even persons who planned a (mock) crime could be validly identified as having specific crime related knowledge. On the other hand, innocents with such knowledge have a high risk of failing the test, at least when considering univariate changes of neural activation.
Stein, T., Seymour, K., Hebart, M.N., & Sterzer, P.2014 , Psychological Science , pages: 566--574
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.
Ritter, C., Hebart, M.N., Wolbers, T., & Bingel, U.2014 , Journal of Neuroscience , pages: 4634--4639
Behavioral studies have demonstrated that descending pain modulation can be spatially specific, as is evident in placebo analgesia, which can be limited to the location at which pain relief is expected. This suggests that higher-order cortical structures of the descending pain modulatory system carry spatial information about the site of stimulation. Here, we used functional magnetic resonance imaging and multivariate pattern analysis in 15 healthy human volunteers to test whether spatial information of painful stimuli is represented in areas of the descending pain modulatory system. We show that the site of nociceptive stimulation (arm or leg) can be successfully decoded from local patterns of brain activity during the anticipation and receipt of painful stimulation in the rostral anterior cingulate cortex, the dorsolateral prefrontal cortices, and the contralateral parietal operculum. These results demonstrate that information regarding the site of nociceptive stimulation is represented in these brain regions. Attempts to predict arm and leg stimulation from the periaqueductal gray, control regions (e.g., white matter) or the control time interval in the intertrial phase did not allow for classifications above chance level. This finding represents an important conceptual advance in the understanding of endogenous pain control mechanisms by bridging the gap between previous behavioral and neuroimaging studies, suggesting a spatial specificity of endogenous pain control.
Hebart, M.N., Görgen, K., & Haynes, J.-D.2014 , Frontiers in Neuroinformatics
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.
Hebart, M.N., Donner, T.H.*, & Haynes, J.-D.*2012 , Neuroimage , pages: 1393--1403
Perceptual decision-making entails the transformation of graded sensory signals into categorical judgments. Often, there is a direct mapping between these judgments and specific motor responses. However, when stimulus–response mappings are fixed, neural activity underlying decision-making cannot be separated from neural activity reflecting motor planning. Several human neuroimaging studies have reported changes in brain activity associated with perceptual decisions. Nevertheless, to date it has remained unknown where and how specific choices are encoded in the human brain when motor planning is decoupled from the decision process. We addressed this question by having subjects judge the direction of motion of dynamic random dot patterns at various levels of motion strength while measuring their brain activity with fMRI. We used multivariate decoding analyses to search the whole brain for patterns of brain activity encoding subjects' choices. To decouple the decision process from motor planning, subjects were informed about the required motor response only after stimulus presentation. Patterns of fMRI signals in early visual and inferior parietal cortex predicted subjects' perceptual choices irrespective of motor planning. This was true across several levels of motion strength and even in the absence of any coherent stimulus motion. We also found that the cortical distribution of choice-selective brain signals depended on stimulus strength: While visual cortex carried most choice-selective information for strong motion, information in parietal cortex decreased with increasing motion coherence. These results demonstrate that human visual and inferior parietal cortex carry information about the visual decision in a more abstract format than can be explained by simple motor intentions. Both brain regions may be differentially involved in perceptual decision-making in the face of strong and weak sensory evidence.
Christophel, T.B., Hebart, M.N., & Haynes, J.-D.2012 , Journal of Neuroscience , pages: 12983--12989
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.
Hesselmann, G., Hebart, M., & Malach, R.2011 , Journal of Neuroscience , pages: 12936--12944
The study of conscious visual perception invariably necessitates some means of report. Report can be either subjective, i.e., an introspective evaluation of conscious experience, or objective, i.e., a forced-choice discrimination regarding different stimulus states. However, the link between report type and fMRI-BOLD signals has remained unknown. Here we used continuous flash suppression to render target images invisible, and observed a long-lasting dissociation between subjective report of visibility and human subjects' forced-choice localization of targets (“blindsight”). Our results show a robust dissociation between brain regions and type of report. We find subjective visibility effects in high-order visual areas even under equal objective performance. No significant BOLD difference was found between correct and incorrect trials in these areas when subjective report was constant. On the other hand, objective performance was linked to the accuracy of multivariate pattern classification mainly in early visual areas. Together, our data support the notion that subjective and objective reports tap cortical signals of different location and amplitude within the visual cortex.
Stein, T., Hebart, M.N., & Sterzer, P.2011 , Frontiers in Human Neuroscience , pages: 167
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.