Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain–computer interfaces, 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 generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

Increasing evidence suggests that attention can concurrently select multiple locations; yet it is not clear whether this ability relies on continuous allocation of attention to the different targets (a “parallel” strategy) or whether attention switches rapidly between the targets (a periodic “sampling” strategy). Here, we propose a method to distinguish between these two alternatives. The human psychometric function for detection of a single target as a function of its duration can be used to predict the corresponding function for two or more attended targets. Importantly, the predicted curves differ, depending on whether a parallel or sampling strategy is assumed. For a challenging detection task, we found that human performance was best reflected by a sampling model, indicating that multiple items of interest were processed in series at a rate of approximately seven items per second. Surprisingly, the data suggested that attention operated in this periodic regime, even when it was focused on a single target. That is, attention might rely on an intrinsically periodic process.

We effortlessly and seemingly instantaneously recognize thousands of objects, although we rarely—if ever—see the same image of an object twice. The retinal image of an object can vary by context, size, viewpoint, illumination, and location. The present study examined how the visual system abstracts object category across variations in retinal location. In three experiments, participants viewed images of objects presented to different retinal locations while brain activity was recorded using magnetoencephalography (MEG). A pattern classifier was trained to recover the stimulus position (Experiments 1, 2, and 3) and category (Experiment 3) from the recordings. Using this decoding approach, we show that an object's location in the visual field can be recovered in high temporal resolution (5 ms) and with sufficient fidelity to capture topographic organization in visual areas. Experiment 3 showed that an object's category could be recovered from the recordings as early as 135 ms after the onset of the stimulus and that category decoding generalized across retinal location (i.e., position invariance). Our experiments thus show that the visual system rapidly constructs a category representation for objects that is invariant to position.