A habituation account of change detection in same/different judgments

We investigated the basis of change detection in a short-term priming task. In two experiments, participants were asked to indicate whether or not a target word was the same as a previously presented cue. Data from an experiment measuring magnetoencephalography failed to find different patterns for “same” and “different” responses, consistent with the claim that both arise from a common neural source, with response magnitude defining the difference between immediate novelty versus familiarity. In a behavioral experiment, we tested and confirmed the predictions of a habituation account of these judgments by comparing conditions in which the target, the cue, or neither was primed by its presentation in the previous trial. As predicted, cue-primed trials had faster response times, and target-primed trials had slower response times relative to the neither-primed baseline. These results were obtained irrespective of response repetition and stimulus–response contingencies. The behavioral and brain activity data support the view that detection of change drives performance in these tasks and that the underlying mechanism is neuronal habituation

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

Top-down effects on early visual processing in humans: a predictive coding framework

An increasing number of human electroencephalography (EEG) studies examining the earliest component of the visual evoked potential, the so-called C1, have cast doubts on the previously prevalent notion that this component is impermeable to top-down effects. This article reviews the original studies that (i) described the C1, (ii) linked it to primary visual cortex (V1) activity, and (iii) suggested that its electrophysiological characteristics are exclusively determined by low-level stimulus attributes, particularly the spatial position of the stimulus within the visual field. We then describe conflicting evidence from animal studies and human neuroimaging experiments and provide an overview of recent EEG and magnetoencephalography (MEG) work showing that initial V1 activity in humans may be strongly modulated by higher-level cognitive factors. Finally, we formulate a theoretical framework for understanding top-down effects on early visual processing in terms of predictive coding

Testing cognitive theories with multivariate pattern analysis of neuroimaging data

The development of non-invasive neuroimaging techniques to measure brain activity while human participants engage in cognitive tasks has driven thousands of investigations over recent decades. This has been paralleled by advances in experimental design and analysis, including the family of approaches known as multivariate pattern analysis (MVPA). For many researchers, the increased sensitivity provided by applying MVPA to functional MRI, EEG or MEG data made it possible to address theories that describe cognition at the functional level. Here, we review a selection of studies that used MVPA to test cognitive theories from a range of domains, including perception, attention, memory, navigation, emotion, social cognition, and motor control. This broad view reveals properties of MVPA that make it suitable for understanding the ‘how’ of human cognition, such as the ability to test predictions expressed at the item or event level. It also reveals limitations and points to future directions

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Explicit processing of verbal and spatial features during letter-location binding modulates oscillatory activity of a fronto-parietal network.

The present study investigated the binding of verbal and spatial features in immediate memory. In a recent study, we demonstrated incidental and asymmetrical letter-location binding effects when participants attended to letter features (but not when they attended to location features) that were associated with greater oscillatory activity over prefrontal and posterior regions during the retention period. We were interested to investigate whether the patterns of brain activity associated with the incidental binding of letters and locations observed when only the verbal feature is attended differ from those reflecting the binding resulting from the controlled/explicit processing of both verbal and spatial features. To achieve this, neural activity was recorded using magnetoencephalography (MEG) while participants performed two working memory tasks. Both tasks were identical in terms of their perceptual characteristics and only differed with respect to the task instructions. One of the tasks required participants to process both letters and locations. In the other, participants were instructed to memorize only the letters, regardless of their location. Time–frequency representation of MEG data based on the wavelet transform of the signals was calculated on a single trial basis during the maintenance period of both tasks. Critically, despite equivalent behavioural binding effects in both tasks, single and dual feature encoding relied on different neuroanatomical and neural oscillatory correlates. We propose that enhanced activation of an anterior–posterior dorsal network observed in the task requiring the processing of both features reflects the necessity for allocating greater resources to intentionally process verbal and spatial features in this task

Rules from Words: A Dynamic Neural Basis for a Lawful Linguistic Process

Listeners show a reliable bias towards interpreting speech sounds in a way that conforms to linguistic restrictions (phonotactic constraints) on the permissible patterning of speech sounds in a language. This perceptual bias may enforce and strengthen the systematicity that is the hallmark of phonological representation. Using Granger causality analysis of magnetic resonance imaging (MRI)- constrained magnetoencephalography (MEG) and electroencephalography (EEG) data, we tested the differential predictions of rule-based, frequency–based, and top-down lexical influence-driven explanations of processes that produce phonotactic biases in phoneme categorization. Consistent with the top-down lexical influence account, brain regions associated with the representation of words had a stronger influence on acoustic-phonetic regions in trials that led to the identification of phonotactically legal (versus illegal) word-initial consonant clusters. Regions associated with the application of linguistic rules had no such effect. Similarly, high frequency phoneme clusters failed to produce stronger feedforward influences by acoustic-phonetic regions on areas associated with higher linguistic representation. These results suggest that top-down lexical influences contribute to the systematicity of phonological representation

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