The neural basis of visual object learning

Laboratory of Biological Psychology, University of Leuven (K.U.Leuven), Tiensestraat 102, 3000 Leuven, Belgium. <>
Trends in Cognitive Sciences (Impact Factor: 21.97). 11/2009; 14(1):22-30. DOI: 10.1016/j.tics.2009.11.002
Source: PubMed


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.

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    • "The same attractor state may be activated by many different stimuli, including purely internal activations. For simple visual percepts, such as shapes of objects, similarity between brain activations A(M) in the inferotemporal cortical area have been directly compared, using fMRI neuroimaging, to the similarity of the shapes of these objects [11]. Significant similarity has been also found in the fMRI patterns of whole brain activity when people think about specific objects [12], showing how meaning of concepts is encoded in distributed activity of the brain. "
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    ABSTRACT: Conspiracy theories, or in general seriously distorted beliefs, are widespread. How and why are they formed in the brain is still more a matter of speculation rather than science. In this paper one plausible mechanisms is investigated: rapid freezing of high neuroplasticity (RFHN). Emotional arousal increases neuroplasticity and leads to creation of new pathways spreading neural activation. Using the language of neurodynamics a meme is defined as quasi-stable associative memory attractor state. Depending on the temporal characteristics of the incoming information and the plasticity of the network, memory may self-organize creating memes with large attractor basins, linking many unrelated input patterns. Memes with fake rich associations distort relations between memory states. Simulations of various neural network models trained with competitive Hebbian learning (CHL) on stationary and non-stationary data lead to the same conclusion: short learning with high plasticity followed by rapid decrease of plasticity leads to memes with large attraction basins, distorting input pattern representations in associative memory. Such system-level models may be used to understand creation of distorted beliefs and formation of conspiracy memes, understood as strong attractor states of the neurodynamics.
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    • "Human: Schiltz et al., 1999; Schwartz et al., 2002; Furmanski et al., 2004; Yotsumoto et al., 2008; for a recent review see Lu et al., 2011). Further, long-term training with artificial objects in both human (e.g., Op de Beeck et al., 2006; Yue et al., 2006; Wong et al., 2009b; Zhang et al., 2010) and non-human primates (e.g., Kobatake et al., 1998; Op de Beeck et al., 2001; Baker et al., 2002; Woloszyn and Sheinberg, 2012) have revealed specific changes in the response of high-level visual cortex such as increases or decreases in response magnitude and increased selectivity for trained objects and task-relevant stimulus dimensions (for review, see Op de Beeck and Baker, 2010b). For example, Op de Beeck et al. (2006) trained human subjects for approximately 10 h to discriminate between exemplars in one of three novel object classes (“smoothies”, “spikies”, and “cubies”). "
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    ABSTRACT: Real-world expertise provides a valuable opportunity to understand how experience shapes human behavior and neural function. In the visual domain, the study of expert object recognition, such as in car enthusiasts or bird watchers, has produced a large, growing, and often-controversial literature. Here, we synthesize this literature, focusing primarily on results from functional brain imaging, and propose an interactive framework that incorporates the impact of high-level factors, such as attention and conceptual knowledge, in supporting expertise. This framework contrasts with the perceptual view of object expertise that has concentrated largely on stimulus-driven processing in visual cortex. One prominent version of this perceptual account has almost exclusively focused on the relation of expertise to face processing and, in terms of the neural substrates, has centered on face-selective cortical regions such as the Fusiform Face Area (FFA). We discuss the limitations of this face-centric approach as well as the more general perceptual view, and highlight that expert related activity is: (i) found throughout visual cortex, not just FFA, with a strong relationship between neural response and behavioral expertise even in the earliest stages of visual processing, (ii) found outside visual cortex in areas such as parietal and prefrontal cortices, and (iii) modulated by the attentional engagement of the observer suggesting that it is neither automatic nor driven solely by stimulus properties. These findings strongly support a framework in which object expertise emerges from extensive interactions within and between the visual system and other cognitive systems, resulting in widespread, distributed patterns of expertise-related activity across the entire cortex.
    Frontiers in Human Neuroscience 12/2013; 7(10):885. DOI:10.3389/fnhum.2013.00885 · 3.63 Impact Factor
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    • "After training, the mean magnitude of neural activation in a region of interest can be either increased [1], [2], [3], [4], [5], [6], [7], decreased [2], [3], [6], [8], [9], [10], [11] or even unchanged [12]. We argue that one possible interpretation of these inconsistent findings is that learning-induced changes are not homogenous in that the activation of some neurons is increased and the activation of others is decreased (for a review, see [13]). Accordingly, fMRI studies based on the mean magnitudes of neural activation averaged across voxels may show inconsistent findings. "
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    ABSTRACT: Learning to be skillful is an endowed talent of humans, but neural mechanisms underlying behavioral improvement remain largely unknown. Some studies have reported that the mean magnitude of neural activation is increased after learning, whereas others have instead shown decreased activation. In this study, we used functional magnetic resonance imaging (fMRI) to investigate learning-induced changes in the neural activation in the human brain with a classic motor training task. Specifically, instead of comparing the mean magnitudes of activation before and after training, we analyzed the learning-induced changes in multi-voxel spatial patterns of neural activation. We observed that the stability of the activation patterns, or the similarity of the activation patterns between the even and odd runs of the fMRI scans, was significantly increased in the primary motor cortex (M1) after training. By contrast, the mean magnitude of neural activation remained unchanged. Therefore, our study suggests that learning shapes the brain by increasing the stability of the activation patterns, therefore providing a new perspective in understanding the neural mechanisms underlying learning.
    PLoS ONE 01/2013; 8(1):e53555. DOI:10.1371/journal.pone.0053555 · 3.23 Impact Factor
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