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Outputs (3)

Detecting a viewer's familiarity with a face: Evidence from event-related brain potentials and classifier analyses (2021)
Journal Article
Wiese, H., Anderson, D., Beierholm, U., Tuettenberg, S. C., Young, A. W., & Burton, A. M. (2022). Detecting a viewer's familiarity with a face: Evidence from event-related brain potentials and classifier analyses. Psychophysiology, 59(1), Article e13950. https://doi.org/10.1111/psyp.13950

Human observers recognise the faces of people they know efficiently and without apparent effort. Consequently, recognising a familiar face is often assumed to be an automatic process beyond voluntary control. However, there are circumstances in which... Read More about Detecting a viewer's familiarity with a face: Evidence from event-related brain potentials and classifier analyses.

Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data (2021)
Journal Article
Aston, S., Negen, J., Nardini, M., & Beierholm, U. (2022). Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data. Behavior Research Methods, 54(1), 508-521. https://doi.org/10.3758/s13428-021-01633-2

Observers in perceptual tasks are often reported to combine multiple sensory cues in a weighted average that improves precision – in some studies, approaching statistically-optimal (Bayesian) weighting, but in others departing from optimality, or not... Read More about Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss (2021)
Conference Proceeding
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. . https://doi.org/10.1109/icpr48806.2021.9413197

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our... Read More about Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss.