139 research outputs found
An introduction to time-resolved decoding analysis for M/EEG
The human brain is constantly processing and integrating information in order
to make decisions and interact with the world, for tasks from recognizing a
familiar face to playing a game of tennis. These complex cognitive processes
require communication between large populations of neurons. The non-invasive
neuroimaging methods of electroencephalography (EEG) and magnetoencephalography
(MEG) provide population measures of neural activity with millisecond precision
that allow us to study the temporal dynamics of cognitive processes. However,
multi-sensor M/EEG data is inherently high dimensional, making it difficult to
parse important signal from noise. Multivariate pattern analysis (MVPA) or
"decoding" methods offer vast potential for understanding high-dimensional
M/EEG neural data. MVPA can be used to distinguish between different conditions
and map the time courses of various neural processes, from basic sensory
processing to high-level cognitive processes. In this chapter, we discuss the
practical aspects of performing decoding analyses on M/EEG data as well as the
limitations of the method, and then we discuss some applications for
understanding representational dynamics in the human brain
Overfitting the literature to one set of stimuli and data
The fast-growing field of Computational Cognitive Neuroscience is on track to
meet its first crisis. A large number of papers in this nascent field are
developing and testing novel analysis methods using the same stimuli and
neuroimaging datasets. Publication bias and confirmatory exploration will
result in overfitting to the limited available data. The field urgently needs
to collect more good quality open neuroimaging data using a variety of
experimental stimuli, to test the generalisability of current published
results, and allow for more robust results in future work
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data
Multivariate pattern analysis (MVPA) or brain decoding methods have become
standard practice in analysing fMRI data. Although decoding methods have been
extensively applied in Brain Computing Interfaces (BCI), these methods have
only recently been applied to time-series neuroimaging data such as MEG and EEG
to address experimental questions in Cognitive Neuroscience. In a
tutorial-style review, we describe a broad set of options to inform future
time-series decoding studies from a Cognitive Neuroscience perspective. Using
example MEG data, we illustrate the effects that different options in the
decoding analysis pipeline can have on experimental results where the aim is to
'decode' different perceptual stimuli or cognitive states over time from
dynamic brain activation patterns. We show that decisions made at both
preprocessing (e.g., dimensionality reduction, subsampling, trial averaging)
and decoding (e.g., classifier selection, cross-validation design) stages of
the analysis can significantly affect the results. In addition to standard
decoding, we describe extensions to MVPA for time-varying neuroimaging data
including representational similarity analysis, temporal generalisation, and
the interpretation of classifier weight maps. Finally, we outline important
caveats in the design and interpretation of time-series decoding experiments.Comment: 64 pages, 15 figure
Unique contributions of perceptual and conceptual humanness to object representations in the human brain
The human brain is able to quickly and accurately identify objects in a dynamic visual world. Objects evoke different patterns of neural activity in the visual system, which reflect object category memberships. However, the underlying dimensions of object representations in the brain remain unclear. Recent research suggests that objects similarity to humans is one of the main dimensions used by the brain to organise objects, but the nature of the human-similarity features driving this organisation are still unknown. Here, we investigate the relative contributions of perceptual and conceptual features of humanness to the representational organisation of objects in the human visual system. We collected behavioural judgements of human-similarity of various objects, which were compared with time-resolved neuroimaging responses to the same objects. The behavioural judgement tasks targeted either perceptual or conceptual humanness features to determine their respective contribution to perceived human-similarity. Behavioural and neuroimaging data revealed significant and unique contributions of both perceptual and conceptual features of humanness, each explaining unique variance in neuroimaging data. Furthermore, our results showed distinct spatio-temporal dynamics in the processing of conceptual and perceptual humanness features, with later and more lateralised brain responses to conceptual features. This study highlights the critical importance of social requirements in information processing and organisation in the human brain
An empirically driven guide on using Bayes factors for M/EEG decoding
Bayes factors can be used to provide quantifiable evidence for contrasting hypotheses and have thus become increasingly popular in cognitive science. However, Bayes factors are rarely used to statistically assess the results of neuroimaging experiments. Here, we provide an empirically driven guide on implementing Bayes factors for time-series neural decoding results. Using real and simulated magnetoencephalography (MEG) data, we examine how parameters such as the shape of the prior and data size affect Bayes factors. Additionally, we discuss the benefits Bayes factors bring to analysing multivariate pattern analysis data and show how using Bayes factors can be used instead or in addition to traditional frequentist approaches
Decoding images in the mind's eye : the temporal dynamics of visual imagery
Mental imagery is the ability to generate images in the mind in the absence of sensory input. Both perceptual visual processing and internally generated imagery engage large, overlapping networks of brain regions. However, it is unclear whether they are characterized by similar temporal dynamics. Recent magnetoencephalography work has shown that object category information was decodable from brain activity during mental imagery, but the timing was delayed relative to perception. The current study builds on these findings, using electroencephalography to investigate the dynamics of mental imagery. Sixteen participants viewed two images of the Sydney Harbour Bridge and two images of Santa Claus. On each trial, they viewed a sequence of the four images and were asked to imagine one of them, which was cued retroactively by its temporal location in the sequence. Time-resolved multivariate pattern analysis was used to decode the viewed and imagined stimuli. Although category and exemplar information was decodable for viewed stimuli, there were no informative patterns of activity during mental imagery. The current findings suggest stimulus complexity, task design and individual differences may influence the ability to successfully decode imagined images. We discuss the implications of these results in the context of prior findings of mental imagery
Temporal dissociation of neural activity underlying synesthetic and perceptual colors
Grapheme-color synesthetes experience color when seeing achromatic symbols. We examined whether similar neural mechanisms underlie color perception and synesthetic colors using magnetoencephalography. Classification models trained on neural activity from viewing colored stimuli could distinguish synesthetic color evoked by achromatic symbols after a delay of ∼100 ms. Our results provide an objective neural signature for synesthetic experience and temporal evidence consistent with higher-level processing in synesthesia
Recommended from our members
Capacity for movement is an organisational principle in object representations
The ability to perceive moving objects is crucial for threat identification and survival. Recent neuroimaging evidence has shown that goal-directed movement is an important element of object processing in the brain. However, prior work has primarily used moving stimuli that are also animate, making it difficult to disentangle the effect of movement from aliveness or animacy in representational categorisation. In the current study, we investigated the relationship between how the brain processes movement and aliveness by including stimuli that are alive but still (e.g., plants), and stimuli that are not alive but move (e.g., waves). We examined electroencephalographic (EEG) data recorded while participants viewed static images of moving or non-moving objects that were either natural or artificial. Participants classified the images according to aliveness, or according to capacity for movement. Movement explained significant variance in the neural data over and above that of aliveness, showing that capacity for movement is an important dimension in the representation of visual objects in humans
A humanness dimension to visual object coding in the brain
Neuroimaging studies investigating human object recognition have primarily focused on a relatively small number of object categories, in particular, faces, bodies, scenes, and vehicles. More recent studies have taken a broader focus, investigating hypothesized dichotomies, for example, animate versus inanimate, and continuous feature dimensions, such as biologically similarity. These studies typically have used stimuli that are identified as animate or inanimate, neglecting objects that may not fit into this dichotomy. We generated a novel stimulus set including standard objects and objects that blur the animate-inanimate dichotomy, for example, robots and toy animals. We used MEG time-series decoding to study the brain's emerging representation of these objects. Our analysis examined contemporary models of object coding such as dichotomous animacy, as well as several new higher order models that take into account an object's capacity for agency (i.e. its ability to move voluntarily) and capacity to experience the world. We show that early (0–200 ms) responses are predicted by the stimulus shape, assessed using a retinotopic model and shape similarity computed from human judgments. Thereafter, higher order models of agency/experience provided a better explanation of the brain's representation of the stimuli. Strikingly, a model of human similarity provided the best account for the brain's representation after an initial perceptual processing phase. Our findings provide evidence for a new dimension of object coding in the human brain – one that has a “human-centric” focus
Overlapping neural representations for the position of visible and imagined objects
Humans can covertly track the position of an object, even if the object is
temporarily occluded. What are the neural mechanisms underlying our capacity to
track moving objects when there is no physical stimulus for the brain to track?
One possibility is that the brain 'fills-in' information about imagined objects
using internally generated representations similar to those generated by
feed-forward perceptual mechanisms. Alternatively, the brain might deploy a
higher order mechanism, for example using an object tracking model that
integrates visual signals and motion dynamics. In the present study, we used
EEG and time-resolved multivariate pattern analyses to investigate the spatial
processing of visible and imagined objects. Participants tracked an object that
moved in discrete steps around fixation, occupying six consecutive locations.
They were asked to imagine that the object continued on the same trajectory
after it disappeared and move their attention to the corresponding positions.
Time-resolved decoding of EEG data revealed that the location of the visible
stimuli could be decoded shortly after image onset, consistent with early
retinotopic visual processes. For processing of unseen/imagined positions, the
patterns of neural activity resembled stimulus-driven mid-level visual
processes, but were detected earlier than perceptual mechanisms, implicating an
anticipatory and more variable tracking mechanism. Encoding models revealed
that spatial representations were much weaker for imagined than visible
stimuli. Monitoring the position of imagined objects thus utilises similar
perceptual and attentional processes as monitoring objects that are actually
present, but with different temporal dynamics. These results indicate that
internally generated representations rely on top-down processes, and their
timing is influenced by the predictability of the stimulus.Comment: All data and analysis code for this study are available at
https://osf.io/8v47t
- …