Low-Frequency Neural Oscillations Support Dynamic Attending in Temporal Context

in Timing & Time Perception
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Behaviorally relevant environmental stimuli are often characterized by some degree of temporal regularity. Dynamic attending theory provides a framework for explaining how perception of stimulus events is affected by the temporal context within which they occur. However, the precise neural implementation of dynamic attending remains unclear. Here, we provide a suggestion for a potential neural implementation of dynamic attending by appealing to low-frequency neural oscillations. The current review will familiarize the reader with the basic theoretical tenets of dynamic attending theory, and review empirical work supporting predictions derived from the theory. The potential neural implementation of dynamic attending theory with respect to low-frequency neural oscillations will be outlined, covering stimulus processing in regular and irregular contexts. Finally, we will provide some more speculative connections between dynamic attending and neural oscillations, and suggest further avenues for future research.

Low-Frequency Neural Oscillations Support Dynamic Attending in Temporal Context

in Timing & Time Perception



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See Giraud & Poeppel (2012) Ghitza (2011) or Ghitza Giraud & Poeppel (2013) for such a suggestion in the speech domain.


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    (Left) Evolution of attentional energy over time based on entrainment of an internal oscillator to events in contexts with varying degrees of temporal regularity. The peak of an attentional pulse defines the expected onset of an event. Adjustment of period and phase of the oscillator in response to event onsets is particularly emphasized in the initial part of the sequences where the attentional pulse peak and the event onsets are unaligned. The amount of attentional energy at actual event onsets (color-coded by the gray-scale) determines quality of stimulus perception. Based on the preceding context, the internal oscillation also anticipates events in absence of stimulation (labeled ‘anticipated event onset’); this reflects the self-sustaining nature of the attentional oscillation. Note that with decreasing temporal regularity, the attentional pulse becomes more diffuse. (Right) Time course of relative phase (phase distance between attentional pulse peak and event onset). For periodic stimulation (strong regularity), relative phase quickly converges to zero. However, with increasing irregularity, phase alignment between the attentional pulse and event onsets is slower overall and more erratic.

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    Experimental setups commonly used to investigate the influence of temporal context on behavioral performance (left) and schematic results (right). (A) An interval-comparison task: participants must indicate whether a comparison-interval duration is shorter or longer than a standard-interval duration. Results comprise a psychometric curve as a function of comparison duration, from which discrimination thresholds can be estimated. (B) When the interval-comparison task is preceded by an isochronous context sequence, discrimination thresholds measured for the comparison duration improve. (C) Changing the duration of the standard interval relative to the isochronous context sequence causes the standard to end early, on time, or late. The pattern of results, termed an expectancy profile, reveals better performance for on-time standard endings than for early or late standard endings. (D) Manipulations causing the comparison to begin either early or late also reduce accuracy, but the effect is weaker than for standard-ending manipulations. (E) Preceding the to-be-compared intervals by a temporally irregular context wipes out the effects of standard-ending manipulations, as the standard ending cannot be anticipated well based on entrainment to the sequence. (F) Doubling the context rate leads to an expectancy profile similar to the one in (B), since the isochronous context sequence and the standard interval are harmonically related. Note: Bidirectional arrows below comparison endings event indicate varying comparison-interval durations corresponding to the task.

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    Schematic illustration of neural oscillations. Rhythmic (left) and continuous processing modes (right), and hypothesized nesting of neural oscillations. The phase of low-frequency oscillations (delta–theta band) reflects alternation between low and high excitability periods that affects gamma-band amplitude. In the continuous mode, low-frequency oscillations are suppressed (i.e., smaller amplitude), but remain in a high excitability state to facilitate processing of temporally unexpected events.


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