Estimating averages from distributions of tone durations

Schweickert, Richard; Han, Hye Joo; Yamaguchi, Motonori; Fortin, Claudette

doi:10.3758/s13414-013-0591-1

Cited by 10 publications

(9 citation statements)

References 44 publications

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“…Similarly, our perception system is able to extract central tendency and variability information from an overwhelming amount of sensory input to form ensemble statistics (for a review, see Alvarez, 2011). This capability has been tested using different types of stimuli in multiple sensory modalities (recent examples: Schweickert, Han, Yamaguchi, & Fortin, 2014;Sweeny & Whitney, 2014), and the results suggest that it may be a common mechanism in human perception.…”

Section: Introductionmentioning

confidence: 99%

Unstable mean context causes sensitivity loss and biased estimation of variability

Ke¹,

Ji²,

Chen³

et al. 2015

Journal of Vision

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A recent study has suggested that statistical representations of ensemble objects may provide contextual stability to facilitate perception. The present study investigated whether facilitating such perception occurs in the extraction of variability information and how the stability of context mean values influences variability perception. We designed two tasks in which participants directly judged the variability of stimuli. In Experiment 1, we manipulated both the stability of the mean values and the exposure time to observe the time course of stability facilitation. In Experiment 2, we decomposed the stability of the context mean values into between-trials and within-trial levels to further investigate the mechanism of such facilitation. The results revealed that stable mean contexts do facilitate variability perception. In particular, unstable long-term mean context causes loss of sensitivity to variability whereas response bias is determined by the interaction between long-term and transient mean stability.

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Section: Introductionmentioning

confidence: 99%

Unstable mean context causes sensitivity loss and biased estimation of variability

Ke¹,

Ji²,

Chen³

et al. 2015

Journal of Vision

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“…Intuitive averaging has been demonstrated for various features in the visual domain 29 , from primary ensembles such as object size 30,31 , color and grayness 32,33 , to high-level ensembles such as facial expression and lifelikeness [34][35][36] . Rather than being confined to the (inherently 'parallel') visual domain, ensemble perception has also been demonstrated for sequentially presented items, such as auditory frequency, tone loudness, tone duration, and weight [37][38][39][40] . In a cross-modal temporal integration study, Chen and colleagues 41 showed that the average interval of a train of auditory intervals can quickly capture a subsequently presented visual interval, influencing visual motion perception.…”

Section: Introductionmentioning

confidence: 99%

Logarithmic encoding of ensemble time intervals

Ren¹,

Allenmark²,

Müller³

et al. 2020

Preprint

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Although time perception is based on the internal representation of time, whether the subjective timeline is scaled linearly or logarithmically remains an open issue. Evidence from previous research is mixed: while the classical internal-clock model assumes a linear scale with scalar variability, there is evidence that logarithmic timing provides a better fit to behavioral data. A major challenge for investigating the nature of the internal scale is that the retrieval process required for time judgments may involve a remapping of the subjective time back to the objective scale, complicating any direct interpretation of behavioral findings. Here, we used a novel approach, requiring rapid intuitive, 'ensemble' averaging of a whole set of time intervals, to probe the subjective timeline. Specifically, observers' task was to average a series of successively presented, auditory or visual, intervals in the time range 300-1300 ms. Importantly, the intervals were taken from three sets of durations, which were distributed such that the arithmetic mean (from the linear scale) and the geometric mean (from the logarithmic scale) were clearly distinguishable. Consistently across the three sets and the two presentation modalities, our results revealed subjective averaging to be close to the geometric mean, indicative of a logarithmic timeline underlying time perception. ENSEMBLE PERCEPTION IN TIME DOMAIN

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“…The ability to extract the average time interval information in the action-sensory feedback sequence demonstrates the individual timing sensitivity (“temporal window” for sensory integration) and help us adapt to the environmental changes (Repp, 2005). The computation of the “mean,” i.e., temporal averaging process, has been realized in a number of contexts, including crossmodal interaction in recent studies (Cheng et al, 1996; Matell and Henning, 2013; Schweickert et al, 2014; De Corte and Matell, 2016a; Chen et al, 2018). One compelling example for temporal averaging is the central tendency effect within the broader framework of Bayesian optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Temporal averaging entails the empirical inquiries with regards to the distribution of irregular (unequal) time intervals (De Corte and Matell, 2016a; Chen et al, 2018; Wan and Chen, 2018), selective averaging one of the sequences (Overduin et al, 2008), as well as potential capacity limits of simultaneous temporal processing (Cheng et al, 2014). Schweickert et al (2014) demonstrated that observers estimated the average of tone durations and their performance was influenced by the distribution of the tone durations. In general the estimated averages were a linear function of the stimulus means.…”

Section: Introductionmentioning

confidence: 99%

Robust Temporal Averaging of Time Intervals Between Action and Sensation

Zeng

Chen

2019

Front. Psychol.

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Perception of the time interval between one’s own action (a finger tapping) and the associated sensory feedback (a visual flash or an auditory beep) is critical for precise and flexible control of action and behavioral decision. Previous studies have examined temporal averaging for multiple time intervals and its role for perceptual organization and crossmodal integration. In the present study, we extended the temporal averaging from sensory stimuli to the coupling of action and its sensory feedback. We investigated whether and how temporal averaging could be achieved with respect to the multiple intervals in a sequence of action-sensory feedback events, and hence affect the subsequent timing behavior. In unimodal task, participants voluntarily tapped their index finger at a constant pace while receiving auditory feedback (beeps) with varied intervals as well as variances throughout the sequence. In crossmodal task, for a given sequence, each tap was accompanied randomly with either visual flash or auditory beep as sensory feedback. When the sequence was over, observers produced a subsequent tap with either auditory or visual stimulus, which enclose a probe interval. In both tasks, participants were required to make a two alternative forced choice (2AFC), to indicate whether the target interval is shorter or longer than the mean interval between taps and their associated sensory events in the preceding sequence. In both scenarios, participants’ judgments of the probe interval suggested that they had internalized the mean interval associated with specific bindings of action and sensation, showing a robust temporal averaging process for the interval between action and sensation.

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Estimating averages from distributions of tone durations

Cited by 10 publications

References 44 publications

Unstable mean context causes sensitivity loss and biased estimation of variability

Unstable mean context causes sensitivity loss and biased estimation of variability

Logarithmic encoding of ensemble time intervals

Robust Temporal Averaging of Time Intervals Between Action and Sensation

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