Bubbles: a technique to reveal the use of information in recognition tasks.

By F. Gosselin, Philippe G. Schyns. Vision Research, 2001

741 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/18b271097d0d5ff8b57675bf67450104d4ce31f4

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

A mechanism for impaired fear recognition after amygdala damage (2005, background): “SM and normal control subjects were each shown approximately 3,000 trials of sparsely revealed faces varying in gender and emotional expression (fear or happiness”
The composite face illusion: A whole window into our understanding of holistic face perception (2013, positive): “…does not imply that the parts have the same weight in face perception: Some parts are certainly more diagnostic than others when perceiving faces, for instance the region of the eyes (e.g., Davies, Ellis, & Shepherd, 1977; Gosselin & Schyns, 2001; Haig, 1985, 1986; Sheperd, Davies, & Ellis, 1981).”
Transmitting and Decoding Facial Expressions (2005, methodology): “We applied Bubbles (Gosselin & Schyns, 2001) to estimate how the brain uses spatial-scale information to decode and classify the six basic facial expressions (plus neutral), and also to quantify how the information in these expressions overlaps.”
Recognizing Disguised Faces: Human and Machine Evaluation (2014, background): “In human cognition research, Gosselin and Schyns [45] have proposed a technique to identify relevant facial regions for recognition which shows that certain facial parts are more important than others for recognition.”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, methodology): “tion, similar phenomena were also observed in facial recognition [28].”
Fine-Grained Crowdsourcing for Fine-Grained Recognition (2013, background): “The game is named after a well known psychology technique for studying features that humans use for face recognition [14].”
Eye can read your mind: decoding gaze fixations to reveal categorical search targets. (2013, methodology): “…and revealing information embedded in human behavior (e.g., Baddeley & Tatler, 2006; Caspi, Beutter, & Eckstein, 2004; Eckstein & Ahumada, 2002; Gosselin & Schyns, 2001; Tavassoli, van der Linde, Bovik, & Cormack, (2009), with a difference being that our approach uses computer vision methods…”
Social attributions from faces: determinants, consequences, accuracy, and functional significance. (2015, methodology): “Bubbles: a technique to reveal the use of information in recognition tasks.”
Recurrent Processing during Object Recognition (2013, methodology): “To algorithmically and parametrically manipulate occlusion in an automated fashion, we use a method similar to the “Bubbles” approach (Gosselin and Schyns, 2001) in which selected portions of an image are spatially masked via filtering operations.”
Development of the other-race effect during infancy: Evidence toward universality? (2009, methodology): “N IH -PA Author M anuscript N IH -PA Author M anuscript N IH -PA Author M anuscript to be required to perform face recognition (e.g., Gosselin & Schyns, 2001).”
What are the Visual Features Underlying Human Versus Machine Vision? (2017, background): “We refer to these revealed image patches hereafter as “bubbles” [8, 5].”
Vision in autism spectrum disorders. (2009, methodology): “Spezio, Adolphs, Hurley, and Piven (2007) looked explicitly at eye gaze to fearful and happy faces, but used the ‘‘Bubbles” method (Gosselin & Schyns, 2001) to reveal different amounts of information from trial to trial, and thereby test the information being used to perform the task whilst also…”
Reverse Correlating Social Face Perception (2012, methodology): “Arguably, this might be achieved using yet another RC technique, bubbles (Gosselin & Schyns, 2001), in which good exemplars of the target categories are partially presented (masked by Gaussian ‘‘bubbles’’).”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, methodology): “The task was face detection, with stimuli as in Figure 4A, but here the images were sampled with Bubbles: randomly positioned Gaussian apertures which selectively reveal different parts of the image on different trials (Gosselin and Schyns, 2001).”
Foundations of Vision (2018, methodology): “This approach can be used to map what features are most informative for determining the gender or emotional expression of a face (Adolphs et al., 2005; Gosselin & Schyns, 2001).”