Effects of temporal association on recognition memory

By G. Wallis, Heinrich H. Bülthoff. 2001

255 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

Citations in the corpus

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation (2016, positive): “There is considerable evidence from physiology and psychophysics that the brain employs a temporal-association strategy of this sort [35, 56, 12, 30, 57, 31].”
Unsupervised Natural Visual Experience Rapidly Reshapes Size Invariant Object Representation in Inferior Temporal Cortex (2010, unknown): “Several studies have also shown that manipulating the spatiotemporal contiguity statistics of visual experience can alter the tolerance of human object perception (Cox et al., 2005; Wallis et al., 2009; Wallis and Bülthoff, 2001).”
Perceptual History Influences Neural Responses to Face and Body Postures (2003, background): “Psychophysical studies (Wallis & Bülthoff, 2001; Stone, 1998) have also stressed the importance of the temporal order of image presentation for recognition.”
Transformation of shape information in the ventral pathway. (2007, background): “For these larger rotations, generalization might depend on learning temporal associations between multiple views of rotating objects [51].”
Learning and disrupting invariance in visual recognition (2011, methodology): “In accord with the psychophysics literature [2] [3], our model also predicts that the difficulty of the discrimination task is the primary determiner of the amount of exposure necessary to observe a behavioral effect rather than the strength of the neural effect on individual cells.”
Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About (2006, background): “Fig. 13. Time course of sequences shown to observers in Wallis and Bulthoff [ 83 ].”
Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? (2013, background): “There is psychophysical and neurophysiological evidence that the brain employs such learning rules (e.g. [53, 54] and references therein).”
Continuous transformation learning of translation invariant representations (2010, background): “…Perry et al. (2006), human learning was better if adjacent views of an object occurred in temporal succession, and this as well as other evidence (Wallis and Bulthoff 2001; Wallis 1998, 2002; Stone 1998) suggests that temporal continuity is a useful factor in helping humans to learn view…”
Unsupervised Learning of Visual Structure using Predictive Generative Networks (2015, background): “One would hope that the discriminator would be able to discern if the identity changed for the proposed rotated view, but interestingly, even humans struggle with this task (Wallis & Bulthoff, 2001).”
Unsupervised Learning of Invariant Representations in Hierarchical Architectures (2013, unknown): “[58, 60] and references therein).”