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Meter without rhythmic pattern repetitions increases pre-attentive processing
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Processing musical meter – the organization of time into regular cycles of strong and weak beats – requires abstraction from the varying rhythmic surface. Several studies investigated whether meter processing requires attention, or if it can be both pre-attentive and attentive. While findings on temporal expectation (processing meter per se) indicated benefits of attention, studies on meter processing in a more complex, dual-task context (meter used for temporal orientation) consistently reported pre-attentive processing. Also, while surface-based approaches to meter (meter aided by pattern repetition) showed some benefits of attention, structural approaches (meter not aided by pattern repetition, increased complexity) found pre-attentive-only processing. Therefore, in the present study we hypothesized that pre-attentive processing increases with cognitive load, and we compared surface with structural meter processing. Supporting our hypothesis, we saw improved behavioral performance for surface meter, as well as EEG evidence that structural meter elicits pre-attentive processing (pre-attentive P1) while surface meter does not (attentive-only P1). Our findings highlight the need for increased awareness in approaches to meter processing and support the idea that increased cognitive demand may recruit pre-attentive processing of temporal structure.
Title: Meter without rhythmic pattern repetitions increases pre-attentive processing
Description:
Processing musical meter – the organization of time into regular cycles of strong and weak beats – requires abstraction from the varying rhythmic surface.
Several studies investigated whether meter processing requires attention, or if it can be both pre-attentive and attentive.
While findings on temporal expectation (processing meter per se) indicated benefits of attention, studies on meter processing in a more complex, dual-task context (meter used for temporal orientation) consistently reported pre-attentive processing.
Also, while surface-based approaches to meter (meter aided by pattern repetition) showed some benefits of attention, structural approaches (meter not aided by pattern repetition, increased complexity) found pre-attentive-only processing.
Therefore, in the present study we hypothesized that pre-attentive processing increases with cognitive load, and we compared surface with structural meter processing.
Supporting our hypothesis, we saw improved behavioral performance for surface meter, as well as EEG evidence that structural meter elicits pre-attentive processing (pre-attentive P1) while surface meter does not (attentive-only P1).
Our findings highlight the need for increased awareness in approaches to meter processing and support the idea that increased cognitive demand may recruit pre-attentive processing of temporal structure.
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