Temporal regularity facilitates higher‐order sensory predictions in fast auditory sequences. (18th November 2013)

• Record Type: Journal Article
• Authors: Tavano, Alessandro; Widmann, Andreas; Bendixen, Alexandra; Trujillo‐Barreto, Nelson; Schröger, Erich
• Is Part Of: European journal of neuroscience. Volume 39:Number 2(2014:Jan.)
• Page Start: 308
• Page End: 318
• Publication Date: 2013-11-18
• Subjects: Nervous system -- Periodicals; 612.8
• DOI: 10.1111/ejn.12404
• Languages: English
• ISSNs: 0953-816X

Abstract

<abstract abstract-type="main" id="ejn12404-abs-0001"> <title>Abstract</title> <p>Does temporal regularity facilitate prediction in audition? To test this, we recorded human event‐related potentials to frequent standard tones and infrequent pitch deviant tones, pre‐attentively delivered within isochronous and anisochronous (20% onset jitter) rapid sequences. Deviant tones were repeated, either with high or low probability. Standard tone repetition sets a first‐order prediction, which is violated by deviant tone onset, leading to a first‐order prediction error response (Mismatch Negativity). The response to highly probable deviant repetitions is, however, attenuated relative to less probable repetitions, reflecting the formation of higher‐order sensory predictions. Results show that temporal regularity is required for higher‐order predictions, but does not modulate first‐order prediction error responses. Inverse solution analyses (Variable Resolution Electrical Tomography; VARETA) localized the error response attenuation to posterior regions of the left superior temporal gyrus. In a control experiment with a slower stimulus rate, we found no evidence for higher‐order predictions, and again no effect of temporal information on first‐order prediction error. We conclude that: (i) temporal regularity facilitates the establishing of higher‐order sensory predictions, i.e. 'knowing what next', in fast auditory sequences; (ii) first‐order prediction error relies predominantly on stimulus feature mismatch, reflecting the adaptive fit of fast deviance detection processes.</p> </abstract>