Visual prediction and perceptual expertise

Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 Thirteenth Street, Charlestown, MA 02129, USA.
International journal of psychophysiology: official journal of the International Organization of Psychophysiology (Impact Factor: 2.88). 11/2011; 83(2):156-63. DOI: 10.1016/j.ijpsycho.2011.11.002
Source: PubMed


Making accurate predictions about what may happen in the environment requires analogies between perceptual input and associations in memory. These elements of predictions are based on cortical representations, but little is known about how these processes can be enhanced by experience and training. On the other hand, studies on perceptual expertise have revealed that the acquisition of expertise leads to strengthened associative processing among features or objects, suggesting that predictions and expertise may be tightly connected. Here we review the behavioral and neural findings regarding the mechanisms involving prediction and expert processing, and highlight important possible overlaps between them. Future investigation should examine the relations among perception, memory and prediction skills as a function of expertise. The knowledge gained by this line of research will have implications for visual cognition research, and will advance our understanding of how the human brain can improve its ability to predict by learning from experience.

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Available from: Olivia Cheung, Nov 01, 2014
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    • "ics of the volleyball shown in Figure 1. Theories in perception and cognition attribute this capability, among many explanations, to previous experience [9] and existence of an underlying physical abstraction [14]. "
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    ABSTRACT: In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics.
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    • "Moshe Bar's " visual prediction theory " is consistent with perception–action coupling in the visual domain and points to specific cortical areas likely central to differentiating experts from novices. Specifically , Bar (Bar, 2009a,b; Cheung and Bar, 2012; Kveraga et al., 2011) notes the role of the orbitofrontal cortex (OFC) in multimodal associations and links this capability with heightened prediction capability in visual experts. He furthermore provides evidence that the OFC is part of a larger visual expertise network that includes the fusiform face area (FFA). "
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    ABSTRACT: Given a decision that requires less than half a second for evaluating the characteristics of the incoming pitch and generating a motor response, hitting a baseball potentially requires unique perception-action coupling to achieve high performance. We designed a rapid perceptual decision making experiment modeled as a Go/No-Go task, yet tailored to reflect a real scenario confronted by a baseball hitter. For groups of experts (Division I baseball players) and novices (non-players) we recorded electroencephalography (EEG) while they performed the task. We analyzed evoked EEG single-trial variability, contingent negative variation (CNV), and pre-stimulus alpha power with respect to the expert vs. novice groups. We found strong evidence for differences in inhibitory processes between the two groups, specifically differential activity in supplementary motor areas (SMA), indicative of enhanced inhibitory control in the expert (baseball player) group. We also found selective activity in the fusiform gyrus (FG) and orbital gyrus in the expert group, suggesting an enhanced perception-action coupling in baseball players that differentiates them from matched controls. In sum, our results show that EEG correlates of decision formation can be used to identify neural markers of high-performance athletes. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 08/2015; 123. DOI:10.1016/j.neuroimage.2015.08.028 · 6.36 Impact Factor
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    • "In this context, it is important to note that facilitated object and pattern recognition have also been demonstrated in situations where expertise results in strengthened associative processing among features or objects (Cheung & Bar, 2012), suggesting a close relation between prediction and expertise. Specifically, experts typically have more elaborative knowledge structures that enable predictions about stimulus input and automatically direct attention towards the most important stimulus and object features, thus enabling more efficient pattern recognition (Bilalić, Langner, Erb, & Grodd, 2010; Bilalić, Turella, Campitelli, Erb, & Grodd, 2012; Cheung & Bar, 2012). While the predictive process in the aforementioned cases is initialized only after stimulus presentation, in other situations expectations may be formulated prior to the appearance of the stimulus itself when triggered by task instructions (Carlsson, Petrovic, Skare, Petersson, & Ingvar, 2000; Simmons, Matthews, Stein, & Paulus, 2004) or by the stimuli preceding the critical event (Schubotz & von Cramon, 2001). "

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