Flexible Learning of Natural Statistics in the Human Brain

Schwarzkopf, D. Samuel; Zhang, Jiaxiang; Kourtzi, Zoe

doi:10.1152/jn.00028.2009

Cited by 21 publications

(17 citation statements)

References 85 publications

(104 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, such tuning is demonstrated in relatively restricted stimulus sets, and we are far from understanding the representational space of more complex images. In this review we will describe the stimulus selectivity of IT neurons and its modulation by learning in terms of tuning curves for dimensions and features, but this selectivity could also be characterized in terms of more abstract principles, such as the statistical structure of images [27,28]. …”

Section: Visual Object Coding In It Cortexmentioning

confidence: 99%

“…The problem with applying this approach to the learning of more complex objects is that, as described above, visual objects occupy a multi-dimensional space of which the dimensions are not very well known. One potential way out of this problem would be to abandon the notion of an explicit representation of features or dimensions, and describe the tuning curves of neurons and experience-related changes in terms of relatively abstract notions of the statistical properties of complex visual images [27,28]. …”

Section: Effects Of Learning On Object Representations: Empirical Evimentioning

confidence: 99%

See 1 more Smart Citation

The neural basis of visual object learning

Beeck

Baker

2010

Trends in Cognitive Sciences

100

View full text Add to dashboard Cite

Object vision in human and nonhuman primates is often cited as a primary example of adult plasticity in neural information processing. It has been hypothesized that visual experience leads to single neurons in the monkey brain with strong selectivity for complex objects, and to regions in the human brain with a preference for particular categories of highly familiar objects. This view suggests that adult visual experience causes dramatic local changes in the response properties of high-level visual cortex. Here, we review the current neurophysiological and neuroimaging evidence and find that the available data support a different conclusion: adult visual experience introduces moderate, relatively distributed effects that modulate a pre-existing, rich and flexible set of neural object representations. The pervasive role of experience in visual processingSensory information processing in adult mammalian brains is highly malleable [1] with neural processing at all levels adapting to both the short-and long-term properties of the incoming information. In vision, prominent examples include short-term adaptation to input statistics in the retina [2], primary visual cortex (V1) [3] and subsequent cortical stages [4], and long-term reorganization due to changed visual input [5].Nevertheless, cortical neural plasticity has often been viewed as more likely in visual regions selective for complex objects than in the input stage of processing, V1 [6]. It is unlikely that cortical representations could be constructed, a priori, to represent all possible objects that might be encountered throughout life. Indeed, human beings can recognize an almost infinite number of objects, most of us sharing the ability to individuate thousands of faces despite their similarity (two eyes, nose, and mouth in a standard configuration). Further, we often develop expertise in the recognition of particular types of objects such as cars, birds or wild mushrooms [7].In apparent support of extensive learning processes, the past two decades have seen numerous electrophysiological and brain imaging studies reporting effects of learning on high-level visual representations. By learning we mean the effects of any form of experience, whether that be through passive exposure or some sort of explicit training, such as learning to categorize [8] Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptTrends Cogn Sci. Author manuscript; available in PMC 2011 January 1. [9] between objects. Most of these studies have focused on the lateral occipital, occipitotemporal and inferior ...

show abstract

Section: Visual Object Coding In It Cortexmentioning

confidence: 99%

Section: Effects Of Learning On Object Representations: Empirical Evimentioning

confidence: 99%

The neural basis of visual object learning

Beeck

Baker

2010

Trends in Cognitive Sciences

100

View full text Add to dashboard Cite

show abstract

“…They found that degree of facilitation observed depends on the distance between the target and flankers (proximity), as well as the respective orientations and positions of the flankers relative to the target, which translates into collinearity (Polat & Sagi, ). The distance across which facilitation occurs is modifiable (to some extent) as a function of perceptual experience (i.e., perceptual learning, Polat & Sagi, ; Schwarzkopf, Zhang, & Kourtzi, ), suggesting that the underlying mechanism is subject to change as a function of experience (see Loffler, for a review).…”

Section: Introductionmentioning

confidence: 99%

Age‐related changes in visual contour integration: Implications for physiology from psychophysics

Hipp

Dickerson

Moser

et al. 2014

Developmental Psychobiology

View full text Add to dashboard Cite

Visual contour detection is enhanced by grouping principles, such as proximity and collinearity, which appear to rely on horizontal connectivity in visual cortex. Previous experiments suggest that children require greater proximity to detect contours and that, unlike adults, collinearity does not compensate for their proximity limitation. Over two experiments we test whether closure, a global property known to facilitate contour detection, compensates for this limitation. Adults and children (3-9 years old) performed a 2AFC task; one panel contained an illusory contour (closed or open) in visual noise, and one only noise. The experiments were identical except proximity was doubled in Exp. 2, enabling shorter-range spatial integration. Results suggest children are limited by proximity, and that closure did not reliably improve their performance as it did for adults. We conclude that perceptual maturity lags behind anatomy within this system, and suggest that slow statistical learning of long-range orientation correlations controls this disparity.

show abstract

“…However, recent computational approaches propose that experience with the statistics of natural environments in adulthood plays a critical role in enhancing our ability to interpret complex scenes (7,8). Our previous work showed that observers learn to integrate image discontinuities (i.e., orthogonal elements) for contour detection, suggesting that short-term training may alter the utility of image regularities (9,10). Despite accumulating computational and behavioral evidence for the role of experience in the interpretation of complex scenes, the brain plasticity mechanisms that mediate learning of statistical regularities in natural images remain largely unknown.…”

mentioning

confidence: 99%

Learning-dependent plasticity with and without training in the human brain

Zhang

Kourtzi²

2010

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.T he ability to detect and identify meaningful targets in cluttered scenes is a fundamental skill for survival and social interactions. In fact, it is thought that the visual system is optimized through evolution and development for the detection of frequently occurring regularities that typically define shape contours in natural scenes (e.g., elements collinear to the contour path) (1-3). Previous studies have shown that human observers are indeed better at detecting collinear contours than contours defined by regularities (e.g., elements orthogonal to the contour path) that typically define discontinuities (e.g., texture boundaries) rather than coherent shape contours (4-6). However, recent computational approaches propose that experience with the statistics of natural environments in adulthood plays a critical role in enhancing our ability to interpret complex scenes (7,8). Our previous work showed that observers learn to integrate image discontinuities (i.e., orthogonal elements) for contour detection, suggesting that short-term training may alter the utility of image regularities (9, 10). Despite accumulating computational and behavioral evidence for the role of experience in the interpretation of complex scenes, the brain plasticity mechanisms that mediate learning of statistical regularities in natural images remain largely unknown.Here, we combine behavioral and functional MRI (fMRI) measurements to investigate...

show abstract

Flexible Learning of Natural Statistics in the Human Brain

Cited by 21 publications

References 85 publications

The neural basis of visual object learning

The neural basis of visual object learning

Age‐related changes in visual contour integration: Implications for physiology from psychophysics

Learning-dependent plasticity with and without training in the human brain

Contact Info

Product

Resources

About