Biology subjects

Grootswagers, T.

Publications and source records attributed to Grootswagers, T..

6 recordsLinked to original sources

bioRxiv2018

The representational dynamics of visual objects in rapid serial visual processing streams

In our daily lives, we are bombarded with a stream of rapidly changing visual input. Humans have the remarkable capacity to detect and identify objects in fast-changing scenes. Yet, when studying brain representations, stimuli are generally presented in isolation. Here, we studied the dynamics of human vision using a combination of fast stimulus presentation rates, electroencephalography and multivariate decoding analyses. Using a presentation rate of 5 images per second, we obtained the representational structure of a large number of stimuli, and showed the emerging abstract categorical organisation of this structure. Furthermore, we could separate the temporal dynamics of perceptual processing from higher-level target selection effects. In a second experiment, we used the same paradigm at 20Hz to show that shorter image presentation limits the categorical abstraction of object representations. Our results show that applying multivariate pattern analysis to every image in rapid serial visual processing streams has unprecedented potential for studying the temporal dynamics of the structure of representations in the human visual system.

neuroscience↗

bioRxiv2018

Seeing versus Knowing: The Temporal Dynamics of Real and Implied Colour Processing in the Human Brain

Colour is a defining feature of many objects, playing a crucial role in our ability to rapidly recognise things in the world around us and make categorical distinctions. For example, colour is a useful cue when distinguishing lemons from limes or blackberries from raspberries. That means our representation of many objects includes key colour-related information. The question addressed here is whether the neural representation activated by knowing that something is red is the same as that activated when we actually see something red, particularly in regard to timing. We addressed this question using neural timeseries (magnetoencephalography, MEG) data to contrast real colour perception and implied object colour activation. We applied multivariate pattern analysis (MVPA) to analyse the brain activation patterns evoked by colour accessed via real colour perception and implied colour activation. Applying MVPA to MEG data allows us here to focus on the temporal dynamics of these processes. Male and female human participants (N=18) viewed isoluminant red and green shapes and grey-scale, luminance-matched pictures of fruits and vegetables that are red (e.g., tomato) or green (e.g., kiwifruit) in nature. We show that the brain activation pattern evoked by real colour perception is similar to implied colour activation, but that this pattern is instantiated at a later time. These results suggest that a common colour representation can be triggered by activating object representations from memory and perceiving colours.

neuroscience↗

bioRxiv2018

Decoding digits and dice with Magnetoencephalography: Evidence for a shared representation of magnitude

Numerical format describes the way magnitude is conveyed, for example as a digit ( 3) or Roman Numeral ( III). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography (MEG) offers an opportunity to record brain activity with high temporal resolution. Multivariate Pattern Analysis (MVPA) applied to MEG data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can crossdecode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests similar representation of these numerical symbols. Additionally, results from a time-generalisation analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

neuroscience↗

bioRxiv2018

Finding decodable information that is read out in behaviour

Multivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that information decoded as such by the experimenter is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a spatially-unbiased multivariate decoding analysis. We then related brain activation patterns to behaviour using a machine-learning based extension of signal detection theory. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, located mainly in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim.

neuroscience↗

bioRxiv2017

Neural signatures of dynamic emotion constructs in the human brain

How is emotion represented in the brain: is it categorical or along dimensions? In the present study, we applied multivariate pattern analysis (MVPA) to magnetoencephalography (MEG) to study the brains temporally unfolding representations of different emotion constructs. First, participants rated 525 images on the dimensions of valence and arousal and by intensity of discrete emotion categories (happiness, sadness, fear, disgust, and sadness). Thirteen new participants then viewed subsets of these images within an MEG scanner. We used Representational Similarity Analysis (RSA) to compare behavioral ratings to the unfolding neural representation of the stimuli in the brain. Ratings of valence and arousal explained significant proportions of the MEG data, even after corrections for low-level image properties. Additionally, behavioral ratings of the discrete emotions fear, disgust, and happiness significantly predicted early neural representations, whereas rating models of anger and sadness did not. Different emotion constructs also showed unique temporal signatures. Fear and disgust - both highly arousing and negative - were rapidly discriminated by the brain, but disgust was represented for an extended period of time relative to fear. Overall, our findings suggest that 1) dimensions of valence and arousal are quickly represented by the brain, as are some discrete emotions, and 2) different emotion constructs exhibit unique temporal dynamics. We discuss implications of these findings for theoretical understanding of emotion and for the interplay of discrete and dimensional aspects of emotional experience.

neuroscience↗

bioRxiv2017

Asymmetric compression of representational space for object animacy categorization under degraded viewing conditions

Animacy is a robust organizing principle amongst object category representations in the human brain. Using multivariate pattern analysis methods (MVPA), it has been shown that distance to the decision boundary of a classifier trained to discriminate neural activation patterns for animate and inanimate objects correlates with observer reaction times for the same animacy categorization task (Carlson, Ritchie, Kriegeskorte, Durvasula, & Ma, 2014; Ritchie, Tovar, & Carlson, 2015). Using MEG decoding, we tested if the same relationship holds when a stimulus manipulation (degradation) increases task difficulty, which we predicted would systematically decrease the distance of activation patterns from the decision boundary, and increase reaction times. In addition, we tested whether distance to the classifier boundary correlates with drift rates in the Linear Ballistic Accumulator (Brown & Heathcote, 2008). We found that distance to the classifier boundary correlated with reaction time, accuracy, and drift rates in an animacy categorization task. Split by animacy, the correlations between brain and behavior were sustained for longer over the time course for animate than for inanimate stimuli. Interestingly, when examining the distance to the classifier boundary during the peak correlation between brain and behavior, we found that only degraded versions of animate, but not inanimate, objects had systematically shifted towards the classifier decision boundary as predicted. Our results support an asymmetry in the representation of animate and inanimate object categories in the human brain.

neuroscience↗