Trends in Neurosciences
OpinionThe planum temporale as a computational hub
Section snippets
The model: computational analysis of sound patterns
The segregation and matching of spectrotemporal patterns could be achieved in the PT using similar computational mechanisms and neuronal architecture. In this scheme, the PT is a crucial computational interface between incoming sound patterns that are segregated in the PT, and the previously stored patterns with which these are matched. The output after such computation provides information about the acoustic environment that is not immediately available either in the acoustic input or as a
Spatial perception
Auditory spatial analysis is the prototypical application of our computational model (Fig. 3). The PT or its homologues have been implicated in acoustic spatial analysis in both electrophysiological studies in monkeys 19., 30., 31. and functional imaging in humans 16., 17., 32., 33.. Recent functional imaging experiments using broadband stimuli have demonstrated PT activation when the computation of sound movement requires segregation of the effect of movement from the intrinsic structure of
Attention
Attentional (‘top-down’) influences might modulate PT computation via its connections to other association areas (Table 1). Activation of the PT in studies that specifically assess the effect of attention [51] and in dichotic listening [52] might be interpreted in this way. However, functional imaging studies of auditory spatial and object processing have demonstrated that PT activation does not depend on whether a task is employed [17]. Moreover, one generator for the pre-attentive mismatch
Predictions
Fig. 3 illustrates a scheme for the identification and localization of single sound objects in space. One of the most challenging tasks for the auditory system is to execute this task for multiple objects, in the ‘cocktail party effect’ (where we perceive and attend to the voice of one speaker when many speakers are present). We predict a crucial involvement of the PT in this task.
Our model is based on the properties of local networks within the PT, and could be tested experimentally using
Acknowledgements
Our work is supported by the Wellcome Trust. We thank G. Green, K. Friston and A. Rees for helpful discussion.
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