Does self-prioritization affect perceptual processes?

This study is based on a proposal pre-submitted to Visual Cognition, where the study design and analysis plan was registered before data collection. We thank Margaret Jackson for pointers on the memory literature and Sandie Cleland for providing a list of non-words. Further, we thank Aleksandar Visokomogilski for his advice on HDDM, as well as Tanya Bhayani and Malwina Filipczuk for their help with data collection.Peer reviewedPostprintPostprin

Response selection modulates crowding : a cautionary tale for invoking top-down explanations

Peer reviewedPublisher PD

Subitizing object parts reveals a second stage of individuation

Open Practices Statement The experimental programs, data and code for data analysis for all studies are made available publicly available on OSF at https://osf.io/wv9hq/. None of the experiments were preregistered.Peer reviewedPublisher PD

Clustering leads to underestimation of numerosity, but crowding is not the cause

Acknowledgments We would like to thank Ian Thornton for his helpful comments on an earlier draft, and Marlene Poncet for useful discussions regarding the experimental design.Peer reviewedPostprin

For whom the bell tolls : periodic reactivation of sensory cortex in the gamma band as a substrate of visual working memory maintenance

Peer reviewedPublisher PD

Visual field asymmetries in numerosity processing

Acknowledgements We thank Marlene Poncet for helpful comments on the manuscript.Peer reviewedPublisher PD

Masking, crowding and grouping:Connecting low and mid-level vision

Acknowledgments JM (principal investigator) and RC (co-investigator) were supported by grant BB/R009287/1 from UKRI BBSRC. We thank Prof. Arash Sahraie for lending us the equipment used to monitor eye movements, as well as the trial lens set. We also thank Nicholas Jeerakun for his helpful comments and the inspiration that led to the visualisation of the visual fields in figure 18.Peer reviewedPublisher PD

Critical resolution : A superior measure of crowding

This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/J01446X/1]. We would like to thank our anonymous reviewers for their thoughtful feedback, which helped us fine-tune our proposal about critical resolution.Peer reviewedPublisher PD

The eye that binds : Feature integration is not disrupted by saccadic eye movements

Open Access via the Springer Compact Agreement FundRef James S. McDonnell Foundation The data for both experiments, as well as a file containing the stimuli of experiment 1 are available at https://osf.io/k49mf/, where experiment 1 was also preregistered. Acknowledgements: The authors thank Johanna Barclay, Rachel Buhler, Qjan Li, Jesus Rendon, Caitlyn Smith, Alejandro Suarez and Vasilena Voynikova, who collected the data of experiment 2 as part of a group project.Peer reviewedPublisher PD

Crowding in humans is unlike that in convolutional neural networks

Object recognition is a primary function of the human visual system. It has recently been claimed that the highly successful ability to recognise objects in a set of emergent computer vision systems---Deep Convolutional Neural Networks (DCNNs)---can form a useful guide to recognition in humans. To test this assertion, we systematically evaluated visual crowding, a dramatic breakdown of recognition in clutter, in DCNNs and compared their performance to extant research in humans. We examined crowding in three architectures of DCNNs with the same methodology as that used among humans. We manipulated multiple stimulus factors including inter-letter spacing, letter colour, size, and flanker location to assess the extent and shape of crowding in DCNNs. We found that crowding followed a predictable pattern across architectures that was different from that in humans. Some characteristic hallmarks of human crowding, such as invariance to size, the effect of target-flanker similarity, and confusions between target and flanker identities, were completely missing, minimised or even reversed. These data show that DCNNs, while proficient in object recognition, likely achieve this competence through a set of mechanisms that are distinct from those in humans. They are not necessarily equivalent models of human or primate object recognition and caution must be exercised when inferring mechanisms derived from their operation