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

Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams

The neural basis of object recognition and semantic knowledge has been extensively studied but the high dimensionality of object space makes it challenging to develop overarching theories on how the brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to 1,854 object concepts and 22,248 images in the THINGS stimulus set, a manually curated and high-quality image database that was specifically designed for studying human vision. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing 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

How musical rhythms entrain the human brain : clarifying the neural mechanisms of sensory-motor entrainment to rhythms

When listening to music, people across cultures tend to spontaneously perceive and move the body along a periodic pulse-like meter. Increasing evidence suggests that this ability is supported by neural mechanisms that selectively amplify periodicities corresponding to the perceived metric pulses. However, the nature of these neural mechanisms, i.e., the endogenous or exogenous factors that may selectively enhance meter periodicities in brain responses to rhythm, remains largely unknown. This question was investigated in a series of studies in which the electroencephalogram (EEG) of healthy participants was recorded while they listened to musical rhythm. From this EEG, selective contrast at meter periodicities in the elicited neural activity was captured using frequency-tagging, a method allowing direct comparison of this contrast between the sensory input, EEG response, biologically-plausible models of auditory subcortical processing, and behavioral output. The results show that the selective amplification of meter periodicities is shaped by a continuously updated combination of factors including sound spectral content, long-term training and recent context, irrespective of attentional focus and beyond auditory subcortical nonlinear processing. Together, these observations demonstrate that perception of rhythm involves a number of processes that transform the sensory input via fixed low-level nonlinearities, but also through flexible mappings shaped by prior experience at different timescales. These higher-level neural mechanisms could represent a neurobiological basis for the remarkable flexibility and stability of meter perception relative to the acoustic input, which is commonly observed within and across individuals. Fundamentally, the current results add to the evidence that evolution has endowed the human brain with an extraordinary capacity to organize, transform, and interact with rhythmic signals, to achieve adaptive behavior in a complex dynamic environment

Untangling featural and conceptual object representations

Data is hosted on figshare: https://doi.org/10.6084/m9.figshare.9310430.v1 Stimuli for the experiment were obtained from https://osf.io/69pbd

Untangling featural and conceptual object representations

How are visual inputs transformed into conceptual representations by the human visual system? The contents of human perception, such as objects presented on a visual display, can reliably be decoded from voxel activation patterns in fMRI, and in evoked sensor activations in MEG and EEG. A prevailing question is the extent to which brain activation associated with object categories is due to statistical regularities of visual features within object categories. Here, we assessed the contribution of mid-level features to conceptual category decoding using EEG and a novel fast periodic decoding paradigm. Our study used a stimulus set consisting of intact objects from the animate (e.g., fish) and inanimate categories (e.g., chair) and scrambled versions of the same objects that were unrecognizable and preserved their visual features (Long et al., 2018). By presenting the images at different periodic rates, we biased processing to different levels of the visual hierarchy. We found that scrambled objects and their intact counterparts elicited similar patterns of activation, which could be used to decode the conceptual category (animate or inanimate), even for the unrecognizable scrambled objects. Animacy decoding for the scrambled objects, however, was only possible at the slowest periodic presentation rate. Animacy decoding for intact objects was faster, more robust, and could be achieved at faster presentation rates. Our results confirm that the mid-level visual features preserved in the scrambled objects contribute to animacy decoding, but also demonstrate that the dynamics vary markedly for intact versus scrambled objects. Our findings suggest a complex interplay between visual feature coding and categorical representations that is mediated by the visual system’s capacity to use image features to resolve a recognisable object