Trends in Neurosciences
Volume 27, Issue 8, 1 August 2004, Pages 496-503
Journal home page for Trends in Neurosciences

Viewing and doing: similar cortical mechanisms for perceptual and motor learning

https://doi.org/10.1016/j.tins.2004.04.013Get rights and content

Abstract

Historically, different groups of researchers have investigated the mechanisms of perceptual learning and motor learning. For sensory cortex, neurophysiological and psychophysical findings have linked changes in perception with altered neuronal tuning properties. However, less information has been forthcoming from motor cortex. This review compares recent findings on perceptual and motor learning, and suggests that similar mechanisms govern both. These mechanisms involve changes in both the center of neuronal tuning functions and their width or slope. The former reflects the values of the sensory or motor parameters that a neuron encodes, and the latter adjusts the encoding sensitivity. These similarities suggest that specific unifying principles for neural coding and computation exist across sensory and motor domains.

Section snippets

Learning-related changes in tuning values

The description of neuronal activity is commonly reduced from an equation specifying the full tuning curve (Figure 1) to a single ‘tuning value’, which commonly corresponds to the greatest discharge rate and is referred to as the ‘preferred’ or ‘best’ value. For example, A1 neurons have a best frequency (BF) for responding to tones, V1 cells have a preferred orientation (PO) for responding to lines and bars, and M1 cells have a preferred direction (PD) for reaching movements. This compact

Which neurons change?

Another aspect of learning involves the selection of a subpopulation of neurons involved in a change in tuning properties. In V1, observed shifts in PO after adaptation to one orientation occur only in cells with nearby POs. Moreover, the larger the difference between the PO of a cell and the training orientation, the smaller the shift (Figure 2a) [25]. In the auditory system, the situation is similar: maximal shifts in BF were observed for cells with BFs close to the training BF (but not for

Shape of the tuning curve and implications for improved coding

Learning in sensory systems might affect the shape of tuning curves. For example, the slope of the curve might change at a particular point along the curve, even without a significant change in the tuning value (the center or peak of the curve) or the amplitude of the peak. This possibility, which is illustrated in Figure 1b, has several implications for neural coding. In one view, neuronal tuning curves encode the value of a stimulus by signifying their preferred value and the population

Contextual specificity and complexity of responses

Although many neurons in sensory areas are tuned to the types of low-level stimulus features discussed so far, they can also be tuned to complex interactions of low-level features that cannot be predicted from their linear combinations, including specific visual objects [43] and natural, complex acoustic signals 44, 45. In the motor system, an example of this phenomenon comes from studies of bimanual arm movements. The responses of M1 neurons cannot be explained as a linear combination of their

Concluding remarks

William of Ockham (c. 1285–1349), the medieval philosopher famous for formulating the principle of parsimony (‘Ockham's razor’), would have wanted a unified account of perceptual and motor learning. The brain must solve problems within the constraints of its hardware: neurons and plastic synapses. It should not be surprising, therefore, that the solutions it has found for perceptual and motor learning resemble each other at Marr's level of implementation. The similarities discussed here for the

Acknowledgements

We thank Reza Shadmehr and Paul Cisek for their comments on an earlier version of this paper. This work was supported in part by a Center for Excellence grant (8006/00) administered by the Israel Science Foundation (ISF), by the Bundesministerium für Bildung und Forschung–Deutsch-Israelische Projektkooperation(BMBF–DIP), by grant 2001073 administrated by the Binational Science Foundation (BSF), and by a special contribution of the Golden Charitable Trust. R.P. was supported by a Constantiner

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