Biology subjects

Smout, C. A.

Publications and source records attributed to Smout, C. A..

5 recordsLinked to original sources

bioRxiv2020

Open Science Saves Lives: Lessons from the COVID-19 Pandemic

In the last decade Open Science principles have been successfully advocated for and are being slowly adopted in different research communities. In response to the COVID-19 pandemic many publishers and researchers have sped up their adoption of Open Science practices, sometimes embracing them fully and sometimes partially or in a sub-optimal manner. In this article, we express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output. We provide evidence of the misuses of these principles at different stages of the scientific process. We call for a wider adoption of Open Science practices in the hope that this work will encourage a broader endorsement of Open Science principles and serve as a reminder that science should always be a rigorous process, reliable and transparent, especially in the context of a pandemic where research findings are being translated into practice even more rapidly. We provide all data and scripts at https://osf.io/renxy/.

scientific communication and education↗

bioRxiv2019

Global Effects of Feature-based Attention Depend on Surprise

Recent studies have shown that prediction and attention can interact under various circumstances, suggesting that the two processes are based on interdependent neural mechanisms. In the visual modality, attention can be deployed to the location of a task-relevant stimulus ( spatial attention) or to a specific feature of the stimulus, such as colour or shape, irrespective of its location ( feature-based attention). Here we asked whether predictive processes are influenced by feature-based attention outside the current spatial focus of attention. Across two experiments, we recorded neural activity with electroencephalography (EEG) as human observers performed a feature-based attention task at fixation and ignored a stream of peripheral stimuli with predictable or surprising features. Central targets were defined by a single feature (colour or orientation) and differed in salience across the two experiments. Task-irrelevant peripheral patterns usually comprised one particular conjunction of features (standards), but occasionally deviated in one or both features (deviants). Consistent with previous studies, we found reliable effects of feature-based attention and prediction on neural responses to task-irrelevant patterns in both experiments. Crucially, we observed an interaction between prediction and feature-based attention in both experiments: the neural effect of feature-based attention was larger for surprising patterns than it was for predicted patterns. These findings suggest that global effects of feature-based attention depend on surprise, and are consistent with the idea that attention optimises the precision of predictions by modulating the gain of prediction errors. Significance StatementTwo principal mechanisms facilitate the efficient processing of sensory information: prediction uses prior information to guide the interpretation of sensory events, whereas attention biases the processing of these events according to their behavioural relevance. A recent theory proposes to reconcile attention and prediction under a unifying framework, casting attention as a precision optimisation mechanism that enhances the gain of prediction errors. Crucially, this theory suggests that attention and prediction interact to modulate neural responses, but this hypothesis remains to be tested with respect to feature-based attention mechanisms outside the spatial focus of attention. Here we show that global effects of feature-based attention are enhanced when stimuli possess surprising features, suggesting that feature-based attention and prediction are interdependent neural mechanisms.

neuroscience↗

bioRxiv2019

Attention Promotes the Neural Encoding of Prediction Errors

The human brain is thought to optimise the encoding of incoming sensory information through two principal mechanisms: prediction uses stored information to guide the interpretation of forthcoming sensory events, and attention prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Since prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models proposes that attention increases the activity of prediction (or representation) neurons, and would therefore suggest that attention and prediction synergistically modulate selectivity for feature information in the brain. Here we applied multivariate forward encoding techniques to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task, to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, and support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.

neuroscience↗

bioRxiv2017

Prediction Error and Repetition Suppression Have Distinct Effects on Neural Representations of Visual Information

Predictive coding theories argue that recent experience establishes expectations in the brain that generate prediction errors when violated. Prediction errors provide a possible explanation for repetition suppression, where evoked neural activity is attenuated across repeated presentations of the same stimulus. The predictive coding account argues repetition suppression arises because repeated stimuli are expected, whereas non-repeated stimuli are unexpected and thus elicit larger neural responses. Here we employed electroencephalography in humans to test the predictive coding account of repetition suppression by presenting sequences of visual gratings with orientations that were expected either to repeat or change in separate blocks of trials. We applied multivariate forward modelling to determine how orientation selectivity was affected by repetition and prediction. Unexpected stimuli were associated with significantly enhanced orientation selectivity, whereas selectivity was unaffected for repeated stimuli. Our results suggest that repetition suppression and expectation have separable effects on neural representations of visual feature information.

neuroscience↗

bioRxiv2017

Spatial Attention Enhances the Neural Representation of Invisible Signals Embedded in Noise

Recent evidence suggests that voluntary spatial attention can affect neural processing of visual stimuli that do not enter conscious awareness (i.e. invisible stimuli), supporting the notion that attention and awareness are dissociable processes (Watanabe et al., 2011; Wyart, Dehaene, & Tallon-Baudry, 2012). To date, however, no study has demonstrated that these effects reflect enhancement of the neural representation of invisible stimuli per se, as opposed to other neural processes not specifically tied to the stimulus in question. In addition, it remains unclear whether spatial attention can modulate neural representations of invisible stimuli in direct competition with highly salient and visible stimuli. Here we developed a novel electroencephalography (EEG) frequency-tagging paradigm to obtain a continuous readout of human brain activity associated with visible and invisible signals embedded in dynamic noise. Participants (N = 23) detected occasional contrast changes in one of two flickering image streams on either side of fixation. Each image stream contained a visible or invisible signal embedded in every second noise image, the visibility of which was titrated and checked using a two-interval forced-choice detection task. Steady-state visual-evoked potentials (SSVEPs) were computed from EEG data at the signal and noise frequencies of interest. Cluster-based permutation analyses revealed significant neural responses to both visible and invisible signals across posterior scalp electrodes. Control analyses revealed that these responses did not reflect a subharmonic response to noise stimuli. In line with previous findings, spatial attention increased the neural representation of visible signals. Crucially, spatial attention also increased the neural representation of invisible signals. As such, the present results replicate and extend previous studies by demonstrating that attention can modulate the neural representation of invisible signals that are in direct competition with highly salient masking stimuli.

neuroscience↗