The reverse hierarchy theory of visual perceptual learning.

By Merav Ahissar, Shaul Hochstein. Trends in Cognitive Sciences, 2004

1,007 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0c0d6e2c7e87702ca3a5dd2ba5d5ccfec72f15d4

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In Cognitive Neuroscience

Citations in the corpus

A Hierarchy of Temporal Receptive Windows in Human Cortex (2008, background): “…and contex- tual constrains can affect the response properties of spatial receptive fields (Moran and Desimone, 1985; Sheinberg and Logothetis, 2001; Ahissar and Hochstein, 2004; Furmanski et al., 2004), the notion of a cortical hierarchy of spatial receptive fields is still useful for…”
The dynamics of perceptual learning: an incremental reweighting model. (2005, methodology): “For example, the reweighting principle is generically compatible with the reverse hierarchy theory (Ahissar & Hochstein, 1997, 2004), which shares the view that the early sensory representations are not to be altered unless absolutely necessary.”
Video Games and Spatial Cognition (2010, background): “Green and Bavelier (2008) have suggested that research by Ahissar, Hochstein, and colleagues (Ahissar & Hochstein, 2004; Ahissar, Nahum, Nelken, & Hochstein, 2009; Hershler & Hochstein, 2009) is relevant to understanding the mechanisms of learning while playing action video games.”
Exercising Your Brain: A Review of Human Brain Plasticity and Training-Induced Learning (2008, positive): “The reverse hierarchy theory, which was initially proposed by Ahissar and Hochstein (2004) to account for learning in the perceptual domain, has such general appeal.”
Cognitive Penetrability of Perception in the Age of Prediction: Predictive Systems are Penetrable Systems (2015, background): “The changes that happen with such practice are at higher-levels (Ahissar and Hochstein 2004; Hochstein and Ahissar 2002), yet they can influence what we see.”
Visual long-term memory has a massive storage capacity for object details (2008, background): “Thus, to maintain featural details, long-term memory representations of objects might be stored throughout the entire hierarchy of the visual processing stream, including early visual areas, possibly retrieved on demand by means of a feedback process (41, 42).”
Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations. (2015, background): “Such modulation of bottom-up processing by an interpretation on more abstract levels higher up in the processing hierarchy has been repeatedly conceptualized, e.g., in the context of reverse-hierarchy theory by (Ahissar and Hochstein, 2004) several years ago.”
A hidden ambiguity of the term “feedback” in its use as an explanatory mechanism for psychophysical visual phenomena (2014, background): “…“reverse hierarchy” to specify the mechanisms that underlie various visual phenomena (e.g., Di Lollo et al., 2000; Lamme and Roelfsema, 2000; Pascual-Leone and Walsh, 2001; Supèr et al., 2001; Ro et al., 2003; Ahissar and Hochstein, 2004; Bar et al., 2006; Fahrenfort et al., 2007; Koivisto, 2012).”
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence (2016, background): “…IT and IPS, be propagated backwards from high-level visual regions through the visual hierarchies through abundantly present feedback connections in the visual stream at all levels 44 during visual learning 45 , and provide the basis of learning at all stages of the processing in visual brain 46 .”
Plasticity in the adult human auditory brainstem following short-term linguistic training (2008, methodology): “…line of reasoning is also consistent with the Reverse Hierarchy Theory (RHT) of visual learning suggesting that learning consists of an attention-driven, task-dependent ‘‘backward’’ search for increased SNR, especially in highly skilled performers and perceptual experts (Ahissar & Hochstein, 2004).”
Deep Generative Vision as Approximate Bayesian Computation (2014, background): “Interestingly, such type of a model could produce novel insights to the reverse hierarchy theory in visual scene perception [1].”
Perceptual Learning, Cognition, and Expertise (2013, background): “…under which PL occurs and the variables that affect it is a much more tractable one than developing models of high-level PL. Understanding the principles of PL is an active area of research (e.g. Ahissar & Hochstein, 2004; Mettler & Kellman, 2010; Seitz & Watanabe, 2005; Zhang et al., 2010).”