People can use temporally structured sensory information to anticipate future events. Temporal information can be implicitly presented through probability manipulation without participants’ awareness of the manipulation, or explicitly conveyed through instructions. We examined how implicit and explicit temporal information established temporal expectations that influenced choice response times and response conflicts (measured as flanker effects). We implicitly manipulated temporal structure by block-wise varying the likely timing of a target. In the short-interval block, a target was presented frequently (80% of trials) after a short (400ms) cue-to-target interval and infrequently (20% of trials) after a long (1200ms) interval; the probability assignment was reversed in the long-interval block. Building on this baseline condition (Experiment 1), we augmented the temporal information by filling the cue-to-target intervals with tones (Experiment 2), explicitly informed participants of the prevalent time interval (Experiment 3), and provided trial-by-trial reminders of the prevalent time interval (Experiment 4). The temporal probability manipulation alone (of which participants were unaware) influenced choice response times but only when the temporal information was augmented with tones, whereas providing the explicit knowledge of the temporal manipulation, with or without trial-by-trial reminders, robustly influenced choice response times. Response conflict was unaffected by these conditions. These results suggest that temporal expectation can be established by the implicit learning of a temporal structure given that sufficiently strong temporal information is presented as well as by the explicit knowledge of the temporal structure. This established temporal expectation influences choice response times without necessarily affecting the strength of response conflict.
Oscillatory neural activity is dynamically controlled to coordinate perceptual, attentional and cognitive processes. On the macroscopic scale, this control is reflected in the U-shaped deviations of EEG spectral-power dynamics from stochastic dynamics, characterized by disproportionately elevated occurrences of the lowest and highest ranges of power. To understand the mechanisms that generate these low- and high-power states, we fit a simple mathematical model of synchronization of oscillatory activity to human EEG data. The results consistently indicated that the majority (~95%) of synchronization dynamics is controlled by slowly adjusting the probability of synchronization while maintaining maximum entropy within the timescale of a few seconds. This strategy appears to be universal as the results generalized across oscillation frequencies, EEG current sources, and participants (N = 52) whether they rested with their eyes closed, rested with their eyes open in a darkened room, or viewed a silent nature video. Given that precisely coordinated behavior requires tightly controlled oscillatory dynamics, the current results suggest that the large-scale spatial synchronization of oscillatory activity is controlled by the relatively slow, entropy-maximizing adjustments of synchronization probability (demonstrated here) in combination with temporally precise phase adjustments (e.g., phase resetting generated by sensorimotor interactions). Interestingly, we observed a modest but consistent spatial pattern of deviations from the maximum-entropy rule, potentially suggesting that the mid-central-posterior region serves as an “entropy dump” to facilitate the temporally precise control of spectral-power dynamics in the surrounding regions.
Timing is an integral part of physical activities. Walking as a routine form of physical activity might affect interval timing primarily in two different ways within the pacemaker–accumulator timing-theoretic framework: (1) by increasing the speed of the pacemaker due to its physiological effects; (2) by decreasing attention to time and consequently slowing the rate of temporal integration by serving as a secondary task. In order to elucidate the effect of movement on subjective time, in two different experiments we employed a temporal reproduction task conducted on the treadmill under four different encoding–decoding conditions: (1) encoding and reproducing (decoding) the duration while standing (rest); (2) encoding the duration at rest and reproducing it while moving: (3) both encoding and reproducing the duration while moving; and (4) encoding the duration while moving and reproducing it at rest. In the first experiment, participants were tested either in the 4 or the 8 km/h movement condition, whereas in the second experiment a larger sample was tested only in the 4 km/h movement condition. Data were de-trended to control for long-term performance drifts. In Experiment 1, overall durations encoded at rest and reproduced during motion were under-reproduced whereas durations encoded during motion and reproduced at rest were over-reproduced only in the 8 km/h condition. In Experiment 2, the same results were observed in the 4 km/h condition with a larger sample size. These effects on timing behavior provide support for the clock speed-driven effect of movement and contradicts the predictions of attention-based mediation.
Orienting attention in time enables us to prepare for forthcoming perception and action (e.g., estimating the duration of a yellow traffic light when driving). While temporal orienting can facilitate performance on simple tasks, its influence on complex tasks involving response conflict is unclear. Here, we adapted the flanker paradigm to a choice-reaching task where participants used a computer mouse to reach to the left or right side of the screen, as indicated by the central arrow presented with either the congruent or incongruent flankers. We assessed the effects of temporal orienting by manipulating goal-driven temporal expectation (using probabilistic variations in target timing) and stimulus-driven temporal priming (using sequential repetitions versus switches in target timing). We tested how temporal orienting influenced the dynamics of response conflict resolution. Recent choice-reaching studies have indicated that under response conflict, delayed movement initiation captures the response threshold adjustment process, whereas increased curvature toward the incorrect response captures the degree of coactivation of the response alternatives during the controlled response selection process. Both temporal expectation and priming reduced the initiation latency regardless of response conflict, suggesting that both lowered response thresholds independently of response conflict. Notably, temporal expectation, but not temporal priming, increased the curvature toward the incorrect response on incongruent trials. These results suggest that temporal orienting generally increases motor preparedness, but goal-driven temporal orienting particularly interferes with response conflict resolution, likely through its influence on response thresholds. Overall, our study highlights the interplay between temporal orienting and cognitive control in goal-directed action.Keywords Temporal processing . Perception and action . Goal-directed movements Just as we can orient attention in space and to certain object features, recent research has shown that we can also orient attention in time (for reviews, see Nobre et al., 2007;Nobre & van Ede, 2018). Temporal orienting enables us to efficiently prepare for forthcoming perception and action in many everyday situations-for instance, when estimating the duration of a yellow traffic light when driving, approximating when to
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