Modeling learned categorical perception in human vision

Casey, Matthew; Sowden, Paul T.

doi:10.1016/j.neunet.2012.05.001

Cited by 7 publications

(7 citation statements)

References 55 publications

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“…Our results relate to a debate, both, in the categorization literature (Roberson and Davidoff, ; Goldstone et al., ; Notman et al., ; Casey and Sowden, ) and in pain research (Wiech et al., ; Woo et al., ), on how fundamentally prior information influences perception. Are the effects on pain perception we find in our data mainly a reinterpretation following sensory processing or is it a phenomenon affecting perception earlier, at the level of nociception?…”

Section: Discussionsupporting

confidence: 58%

Painful decisions: How classifying sensations can change the experience of pain

Meulen

Anton

Petersen

2017

European Journal of Pain

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Section: Discussionsupporting

confidence: 58%

Painful decisions: How classifying sensations can change the experience of pain

Meulen

Anton

Petersen

2017

European Journal of Pain

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“…Our results provide first evidence that a higher‐level cognitive process which warps perception to fit discrete categories, exists in animals without a visual cortex. Our findings therefore challenge the notion that CP is a phenomenon which requires higher‐order processing in a hierarchical system, such as the visual cortex (Casey & Sowden, 2012) and opens the possibility for either a much simpler explanation for CP (Yu et al, 2017), or the existence of a complex processing system in the fish brain. Future research is required to explore those possibilities.…”

Section: Discussioncontrasting

confidence: 73%

Categorical face perception in fish: How a fish brain warps reality to dissociate “same” from “different”

Parker

Wallis

Obergrussberger

et al. 2020

J of Comparative Neurology

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Categorical perception (CP) is the phenomenon by which a smoothly varying stimulus property undergoes a nonlinear transformation during processing in the brain. Consequently, the stimuli are perceived as belonging to distinct categories separated by a sharp boundary. Originally thought to be largely innate, the discovery of CP in tasks such as novel image discrimination has piqued the interest of cognitive scientists because it provides compelling evidence that learning can shape a category's perceptual boundaries. CP has been particularly closely studied in human face perception. In nonprimates, there is evidence for CP for sound and color discrimination, but not for image or face discrimination. Here, we investigate the potential for learned CP in a lower vertebrate, the damselfish Pomacentrus amboinensis. Specifically, we tested whether the ability of these fish to discriminate complex facial patterns tracked categorical rather than metric differences in the stimuli. We first trained the fish to discriminate sets of two facial patterns. Next, we morphed between these patterns and determined the just noticeable difference (JND) between a morph and original image. Finally, we tested for CP by analyzing the discrimination ability of the fish for pairs of JND stimuli along the spectrum of morphs between two original images. Discrimination performance was significant for the image pair straddling the boundary between categories, and chance for equivalent stimulus pairs on either side, thus producing the classic "category boundary" effect. Our results reveal how perception can be influenced in a top-down manner even in the absence of a visual cortex.

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“…Perceptual learning occurs for different kinds of tasks at different levels of visual analysis (figure 1). It improves detection or discrimination for single features such as orientation (Dosher & Lu 1998, Dosher & Lu 1999, Schoups et al 1995, Vogels & Orban 1985), spatial frequency (Bennett & Westheimer 1991, Fiorentini & Berardi 1981), phase (Dosher et al 2010, Fiorentini & Berardi 1980), contrast (Adini et al 2004, Dorais & Sagi 1997, Sowden et al 2002), color (Casey & Sowden 2012, Özgen & Davies 2002, Thurston & Dobkins 2007), acuity (Bennett & Westheimer 1991, Westheimer 2001), and hyper-acuity (Crist et al 1997, McKee & Westheimer 1978, Poggio et al 1992). It improves pattern discrimination in tasks involving compound stimuli (Fiorentini & Berardi 1980, Fiorentini & Berardi 1981), textures (Ahissar & Hochstein 1993, Karni & Sagi 1991), depth (Fendick & Westheimer 1983, Ramachandran & Braddick 1973), and motion (Ball & Sekuler 1982, Lu et al 2006, Lu et al 2005, Matthews & Welch 1997, Watanabe et al 2002).…”

Section: Observing Perceptual Learningmentioning

confidence: 99%

Visual Perceptual Learning and Models

Dosher

Lü

2017

Annu. Rev. Vis. Sci.

187

210

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Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

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Modeling learned categorical perception in human vision

Cited by 7 publications

References 55 publications

Painful decisions: How classifying sensations can change the experience of pain

Painful decisions: How classifying sensations can change the experience of pain

Categorical face perception in fish: How a fish brain warps reality to dissociate “same” from “different”

Visual Perceptual Learning and Models

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