Filling the blanks in temporal intervals: the type of filling influences perceived duration and discrimination performance

AbstractOur ability to discriminate temporal intervals can be improved with practice. This learning is generally thought to reflect an enhancement in the representation of a trained interval, which leads to interval-specific improvements in temporal discrimination. In the present study, we asked whether temporal learning is further constrained by context-specific factors dictated through the trained stimulus and task structure. Two groups of participants were trained using a single-interval auditory discrimination task over 5 days. Training intervals were either one of eight predetermined values (FI group), or random from trial to trial (RI group). Before and after the training period, we measured discrimination performance using an untrained two-interval temporal comparison task. Our results revealed a selective improvement in the FI group, but not the RI group. However, this learning did not generalize between the trained and untrained tasks. These results highlight the sensitivity of TPL to stimulus and task structure, suggesting that mechanisms of temporal learning rely on processes beyond changes in interval representation.

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Variable Foreperiods and Temporal Discrimination

The Quarterly Journal of Experimental Psychology Section A ◽

10.1080/02724980244000611 ◽

2003 ◽

Vol 56 (4) ◽

pp. 1-35 ◽

Cited By ~ 44

Author(s):

Simon Grondin ◽

Thomas Rammsayer

Keyword(s):

Information Processing ◽

Temporal Discrimination ◽

Discrimination Performance ◽

Temporal Information ◽

Temporal Information Processing ◽

Visual Markers ◽

Dependent Variables ◽

Series Of Experiments ◽

Base Duration ◽

Perceived Duration

Temporal judgements are often accounted for by a single-clock hypothesis. The output of such a clock is reported to depend on the allocation of attention. In the present series of experiments, the influence of attention on temporal information processing is investigated by systematic variations of the period preceding brief empty intervals to be judged. Two indicators of timing performance, temporal sensitivity, reflecting discrimination performance, and perceived duration served as dependent variables. Foreperiods ranged from 0.3 to 0.6 s in Experiments 1 to 4. When the foreperiod varied randomly from trial to trial, perceived duration was longer with increasing length of foreperiod (Experiments 1 and 3 with brief auditory markers and Experiment 4 with brief visual markers), an effect that disappeared with no trial-to-trial variations (Experiment 2). Longer foreperiods also enhanced performance on temporal discrimination of auditory empty intervals with a base duration of 100 ms (Experiments 1 and 5), whereas discrimination performance was unaffected for auditory intervals with a base duration of 500 ms (Experiment 3). The variable-foreperiod effect on perceived duration also held when foreperiods ranged from 0.6 to 1.5 s (Experiments 5—7). Findings suggest that foreperiods appear to effectively modulate attention mechanisms necessary for temporal information processing. However, alternative explanations such as assimilation or compatibility effects cannot be totally discarded.

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A Temporal Model of Aural Frequency Discrimination

10.26686/wgtn.16945990 ◽

2021 ◽

Author(s):

◽

Kaye McAulay

Keyword(s):

Signal To Noise Ratio ◽

Discrimination Performance ◽

Frequency Discrimination ◽

Signal To Noise ◽

Low Frequencies ◽

Human Data ◽

Temporal Intervals ◽

Temporal Model ◽

Noise Ratio ◽

Positive Axis

<p>The importance of temporal information versus place information in frequency analysis by the ear is a continuing controversy. This dissertation developes a temporal model which simulates human frequency discrimination. The model gives guantitative measures of performance for the discrimination of sinusoids in white gaussian noise. The model simulates human frequency discrimination performance as a function of frequency and signal-to-noise ratio. The model's predictions are based on the temporal intervals between the positive axis crossings of the stimulus. The histograms of these temporal intervals were used as the underlying distributions from which indices of discriminability were calculated. Human freguency discrimination data was obtained for five observers as a function of frequency and signal-to-noise ratio. The data were analysed using the method of Group-operating-characteristic (GOC) Analysis. This method of analysis statistically removes unique noise from data. The unique noise was removed by summing observers' ratings for identical stimuli. This method of analysis gave human frequency discrimination data with less unigue noise than any existing frequency data. The human data were used for evaluating the model. The GOC Analysis was also used to study the improvement in d' as a function of stimulus replications and signal-to-noise ratio. The model was a good fit to the human data at 250 Hz, for two signal-to-noise ratios. The model did not fit the data at 1000 Hz or 5000 Hz. There was some evidence of a transition occuring at 1000 Hz. This investigation supported the idea that human frequency discrimination relies on a temporal mechanism at low frequencies with a transition to some other mechanism at about lO00 Hz.</p>

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A Temporal Model of Aural Frequency Discrimination

10.26686/wgtn.16945990.v1 ◽

2021 ◽

Author(s):

◽

Kaye McAulay

Keyword(s):

Signal To Noise Ratio ◽

Discrimination Performance ◽

Frequency Discrimination ◽

Signal To Noise ◽

Low Frequencies ◽

Human Data ◽

Temporal Intervals ◽

Temporal Model ◽

Noise Ratio ◽

Positive Axis

<p>The importance of temporal information versus place information in frequency analysis by the ear is a continuing controversy. This dissertation developes a temporal model which simulates human frequency discrimination. The model gives guantitative measures of performance for the discrimination of sinusoids in white gaussian noise. The model simulates human frequency discrimination performance as a function of frequency and signal-to-noise ratio. The model's predictions are based on the temporal intervals between the positive axis crossings of the stimulus. The histograms of these temporal intervals were used as the underlying distributions from which indices of discriminability were calculated. Human freguency discrimination data was obtained for five observers as a function of frequency and signal-to-noise ratio. The data were analysed using the method of Group-operating-characteristic (GOC) Analysis. This method of analysis statistically removes unique noise from data. The unique noise was removed by summing observers' ratings for identical stimuli. This method of analysis gave human frequency discrimination data with less unigue noise than any existing frequency data. The human data were used for evaluating the model. The GOC Analysis was also used to study the improvement in d' as a function of stimulus replications and signal-to-noise ratio. The model was a good fit to the human data at 250 Hz, for two signal-to-noise ratios. The model did not fit the data at 1000 Hz or 5000 Hz. There was some evidence of a transition occuring at 1000 Hz. This investigation supported the idea that human frequency discrimination relies on a temporal mechanism at low frequencies with a transition to some other mechanism at about lO00 Hz.</p>

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Duration Comparisons for Vision and Touch Are Dependent on Presentation Order and Temporal Context

Frontiers in Integrative Neuroscience ◽

10.3389/fnint.2021.664264 ◽

2021 ◽

Vol 15 ◽

Author(s):

Yi Gao ◽

Kamilla N. Miller ◽

Michael E. Rudd ◽

Michael A. Webster ◽

Fang Jiang

Keyword(s):

Discrimination Performance ◽

Presentation Order ◽

Temporal Context ◽

Active Perception ◽

Temporal Domain ◽

Visual Events ◽

Standard Duration ◽

Temporal Intervals ◽

Forced Choice Task ◽

Duration Comparison

Integrating visual and tactile information in the temporal domain is critical for active perception. To accomplish this, coordinated timing is required. Here, we study perceived duration within and across these two modalities. Specifically, we examined how duration comparisons within and across vision and touch were influenced by temporal context and presentation order using a two-interval forced choice task. We asked participants to compare the duration of two temporal intervals defined by tactile or visual events. Two constant standard durations (700 ms and 1,000 ms in ‘shorter’ sessions; 1,000 ms and 1,500 ms in ‘longer’ sessions) were compared to variable comparison durations in different sessions. In crossmodal trials, standard and comparison durations were presented in different modalities, whereas in the intramodal trials, the two durations were presented in the same modality. The standard duration was either presented first (<sc>) or followed the comparison duration (<cs>). In both crossmodal and intramodal conditions, we found that the longer standard duration was overestimated in <cs> trials and underestimated in <sc> trials whereas the estimation of shorter standard duration was unbiased. Importantly, the estimation of 1,000ms was biased when it was the longer standard duration within the shorter sessions but not when it was the shorter standard duration within the longer sessions, indicating an effect of temporal context. The effects of presentation order can be explained by a central tendency effect applied in different ways to different presentation orders. Both crossmodal and intramodal conditions showed better discrimination performance for <sc> trials than <cs> trials, supporting the Type B effect for both crossmodal and intramodal duration comparison. Moreover, these results were not dependent on whether the standard duration was defined using tactile or visual stimuli. Overall, our results indicate that duration comparison between vision and touch is dependent on presentation order and temporal context, but not modality.

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