566 research outputs found

    Tonic and Phasic Dopamine Fluctuations as Reflected in Beta-power Predict Interval Timing Behavior

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    It has been repeatedly shown that dopamine impacts interval timing in humans and animals (for a review, see Coull, Cheng, & Meck, 2012). Particularly, administration of dopamine agonists or antagonists speeds-up or slows down internal passage of time, respectively (Meck, 1996). This co-variations in the dopamine level and clock speed has been typically induced by pharmacological manipulations (e.g., Lustig & Meck, 2005). However, it has not been assessed whether naturally occurring fluctuations in dopamine level are sufficient for altering interval timing performance. Recent advances in neurophysiology suggest that tonic and phasic changes of dopamine level in cortical-basal ganglia loop can be traced by fluctuations of beta power (Jenkinson & Brown, 2011). We assessed dopamine levels by measuring the beta power while participants were asked to produce time intervals by two key presses. Both tonic levels of dopamine as measured by the beta power before interval initiation and phasic level of dopamine as measured after the first key-press predict timing performance. These positive correlations between beta power and length of produced intervals support the notion that dopamine plays an important role in interval timing, even in the range of naturally occurring fluctuations

    Slow Potentials in Time Estimation: The Role of Temporal Accumulation and Habituation

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    Numerous studies have shown that contingent negative variation (CNV) measured at fronto-central and parietal–central areas is closely related to interval timing. However, the exact nature of the relation between CNV and the underlying timing mechanisms is still a topic of discussion. On the one hand, it has been proposed that the CNV measured at supplementary motor area (SMA) is a direct reflection of the unfolding of time since a perceived onset, whereas other work has suggested that the increased amplitude reflects decision processes involved in interval timing. Strong evidence for the first view has been reported by Macar et al. (1999), who showed that variations in temporal performance were reflected in the measured CNV amplitude. If the CNV measured at SMA is a direct function of the passing of time, habituation effects are not expected. Here we report two replication studies, which both failed to replicate the expected performance-dependent variations. Even more powerful linear-mixed effect analyses failed to find any performance related effects on the CNV amplitude, whereas habituation effects were found. These studies therefore suggest that the CNV amplitude does not directly reflect the unfolding of time

    An Evaluation of the Effect of Auditory Emotional Stimuli on Interval Timing

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    Emotions modulate cognitive processes, including those involved in the perception of time. A number of studies have demonstrated that the emotional modulation of interval timing can be described in terms of an attentional or an arousal-based mechanism, depending on the exact task setup. In this paper, two temporal generalization experiments with auditory emotional stimuli as distractors are presented. These experiments are modeled after the work by Lui et al. (PLoS One, 2011, 6, e218292011) who, using visual distractors, provided evidence for an attentional account of emotion-regulated modulation of the perception of time. Experiment 1 replicates the findings of Lui et al., and thus generalizes their work to auditory stimuli. However, Experiment 2, in setup highly similar to Experiment 1, failed to find any effects of emotional modulation on interval timing. These results indicate that emotional effects on interval timing, although often reported, might not be as ubiquitous as earlier research has (implicitly) suggested

    Exploration of the Rate of Forgetting as a Domain-Specific Individual Differences Measure

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    Learners differ in their learning aptitude. Modern computerized fact-learning systems take these individual differences into account by adapting repetition schedules to the learner's characteristics. Adaptation is based on monitoring responses during learning and using these responses to inform the model's decisions about when to introduce and repeat material by updating the model's internal parameters. Typically, adaptive systems start a learning session with a set of default parameters, with these parameters being updated and adapted to the learner's characteristics when responses are collected. Here we explore whether domain-general individual differences such as working-memory capacity or measures of general intelligence, which can be assessed prior to learning sessions, can inform the choice of initial model parameters. Such an approach is viable if the domain-general individual differences are related to the model parameters estimated during learning. In the current study, we asked participants to learn factual information, and assessed whether their learning performance, operationalized as (1) a model-parameter that captures the rate of forgetting, and (2) the results on an immediate and delayed post-test, was related to two common measures of individual differences: working memory capacity (WMC) and general cognitive ability (GCA). We failed to find evidence in favor for such relations, suggesting that, at least in this relatively small and homogeneous sample, executive functioning and attentional control did not play important roles in predicting delayed recall. The model parameters estimated during learning, on the other hand, are highly correlated with delayed recall of the studied material

    Attention Does Not Affect the Speed of Subjective Time, but Whether Temporal Information Guides Performance:A Large-Scale Study of Intrinsically Motivated Timers in a Real-Time Strategy Game

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    Many prepared actions have to be withheld for a certain amount of time in order to have the most beneficial outcome. Therefore, keeping track of time accurately is vital to using temporal regularities in our environment. Traditional theories assume that time is tracked by means of a clock and an "attentional gate" (AG) that modulates subjective time if not enough attentional resources are directed toward the temporal process. According to the AG theory, the moment of distraction does not have an influence on the subjective modulation. Here, we show, based on an analysis of 28,354 datasets, that highly motivated players of the online multiplayer real-time strategy game StarCraft2 indeed respond later to timed events when they are distracted by other tasks during the interval. However, transient periods of distraction during the interval influence the response time to a lesser degree than distraction just before the required response. We extend the work of Taatgen, van Rijn, and Anderson (2007) and propose an alternative active check theory that postulates that distracted attention prevents people from checking their internal clock; we demonstrate that this account better predicts variance observed in response time. By analyzing StarCraft2 data, we assessed the role of attention in a naturalistic setting that more directly generalizes to real-world settings than typical laboratory studies

    Single trial beta oscillations index time estimation

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    AbstractRecent work shows that putamen-originating beta power oscillations serve as a carrier for temporal information during tapping tasks, with higher beta power associated with longer temporal reproductions. However, given the nature of tapping tasks, it is difficult to determine whether beta power dynamics observed in these tasks are linked to the generation or execution of motor programs or to the internal representation of time. To assess whether recent findings in animals generalize to human studies we reanalyzed existing EEG data of participants who estimated a 2.5s time interval with self-paced onset and offset keypresses. The results showed that the trial-to-trial beta power measured after the onset predicts the produced duration, such that higher beta power indexes longer produced durations. Moreover, although beta power measured before the first key-press also influenced the estimated interval, it did so independently from post-first-keypress beta power. These results suggest that initial motor inhibition plays an important role in interval production, and that this inhibition can be interpreted as a biased starting point of the decision processes involved in time estimation

    Temporal Expectation Indexed by Pupillary Response

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    Forming temporal expectations plays an instrumental role for the optimization of behavior and allocation of attentional resources. Although the effects of temporal expectations on visual attention are well-established, the question of whether temporal predictions modulate the behavioral outputs of the autonomic nervous system such as the pupillary response remains unanswered. Therefore, this study aimed to obtain an online measure of pupil size while human participants were asked to differentiate between visual targets presented after varying time intervals since trial onset. Specifically, we manipulated temporal predictability in the presentation of target stimuli consisting of letters which appeared after either a short or long delay duration (1.5 vs. 3 s) in the majority of trials (75%) within different test blocks. In the remaining trials (25%), no target stimulus was present to investigate the trajectory of preparatory pupillary response under a low level of temporal uncertainty. The results revealed that the rate of preparatory pupillary response was contingent upon the time of target appearance such that pupils dilated at a higher rate when the targets were expected to appear after a shorter as compared to a longer delay period irrespective of target presence. The finding that pupil size can track temporal regularities and exhibit differential preparatory response between different delay conditions points to the existence of a distributed neural network subserving temporal information processing which is crucial for cognitive functioning and goal-directed behavior.</p
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