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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., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2022. Published by Elsevier Inc.)

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., (Copyright © 2019. Published by Elsevier Inc.)

Rapid image presentations combined with time-resolved multivariate analysis methods of EEG or MEG (rapid-MVPA) offer unique potential in assessing the temporal limitations of the human visual system. Recent work has shown that multiple visual objects presented sequentially can be simultaneously decoded from M/EEG recordings. Interestingly, object representations reached higher stages of processing for slower image presentation rates compared to fast rates. This fast rate attenuation is probably caused by forward and backward masking from the other images in the stream. Two factors that are likely to influence masking during rapid streams are stimulus duration and stimulus onset asynchrony (SOA). Here, we disentangle these effects by studying the emerging neural representation of visual objects using rapid-MVPA while independently manipulating stimulus duration and SOA. Our results show that longer SOAs enhance the decodability of neural representations, regardless of stimulus presentation duration, suggesting that subsequent images act as effective backward masks. In contrast, image duration does not appear to have a graded influence on object representations. Interestingly, however, decodability was improved when there was a gap between subsequent images, indicating that an abrupt onset or offset of an image enhances its representation. Our study yields insight into the dynamics of object processing in rapid streams, paving the way for future work using this promising approach., (Copyright © 2019. Published by Elsevier Inc.)

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., (Copyright © 2018. Published by Elsevier Inc.)

Humans recognize faces with ease, despite the complexity of the task and of the visual system which underlies it. Different spatial regions, including both the core and extended face processing networks, and distinct temporal stages of processing have been implicated in face recognition, but there is ongoing controversy regarding the extent to which the mechanisms for recognizing a familiar face differ from those for an unfamiliar face. Here, we used electroencephalogram (EEG) and flicker SSVEP, a high signal-to-noise approach, and searchlight decoding methods to elucidate the mechanisms mediating the processing of familiar and unfamiliar faces in the time domain. Familiar and unfamiliar faces were presented periodically at 15 Hz, 6 Hz and 3.75 Hz either upright or inverted in separate blocks, with the rationale that faster frequencies require shorter processing times per image and tap into fundamentally different levels of visual processing. The 15 Hz trials, likely to reflect early visual processing, exhibited enhanced neural responses for familiar over unfamiliar face trials, but only when the faces were upright. In contrast, decoding methods revealed similar classification accuracies for upright and inverted faces for both familiar and unfamiliar faces. For the 6 Hz frequency, familiar faces had lower amplitude responses than unfamiliar faces, and decoding familiarity was more accurate for upright compared with inverted faces. Finally, the 3.75 Hz frequency revealed no main effects of familiarity, but decoding showed significant correlations with behavioral ratings of face familiarity, suggesting that activity evoked by this slow presentation frequency reflected higher-level, cognitive aspects of familiarity processing. This three-way dissociation between frequencies reveals that fundamentally different stages of the visual hierarchy are modulated by face familiarity. The combination of experimental and analytical approaches used here represent a novel method for elucidating spatio-temporal characteristics within the visual system., (Copyright © 2018 Elsevier Inc. All rights reserved.)