![matlab 2008 psychportaudio matlab 2008 psychportaudio](https://europepmc.org/articles/PMC7667244/bin/fpsyg-11-585437-g005.jpg)
2011 Lewis and Miall 2003 Rammsayer and Pichelmann 2018). These observations can be accounted for by models proposing that the timing of brief intervals (< 500 ms) is supported by an automatic, modality-specific timing system, whereas the timing of longer intervals is sub-served by a distinct cognitive timing mechanism (Gooch et al. For instance, the discrimination of very brief auditory stimuli is superior to that of the same intervals in the visual modality however, this difference disappears once the interval length increases and more amodal higher-order cognitive resources are recruited (Rammsayer and Pichelmann 2018). 1998) and it has helped to generate novel insights into how our experience of time is formed and how the brain may represents time. The temporal discrimination task has been extensively applied to investigate a range of factors modulating temporal cognition (Allman and Meck 2012 Benau and Atchley 2020 Oliveri et al. Both models predict that the physical difference between two long intervals must be larger than that between two brief intervals if the perceptual systems were to discriminate them as a pair of different interval lengths. A more common pattern, however, is larger WFs for brief intervals plateauing at longer intervals in accordance with a generalized form of Weber’s law based on a square root relationship (Burr et al. The WF that is constant across different interval lengths reflects the linear dependency of the noticeable difference on physical interval magnitudes according to Weber’s law (Gibbon 1977). This “just-noticeable difference” proportional to the actual interval length is known as Weber’s fraction (WF). By contrast, the proportion of accurate responses will steadily increase when the stimulus intervals begin to noticeably differ.
![matlab 2008 psychportaudio matlab 2008 psychportaudio](https://image1.slideserve.com/1502791/introduction-to-psychtoolbox-in-matlab-l.jpg)
The more similar the stimulus intervals are, the more difficult their discrimination becomes, which is reflected in a near-chance level discrimination performance. In temporal discrimination tasks, participants are typically presented with a pair of successive stimuli and asked to judge whether the second stimulus was longer or shorter compared to the first stimulus. Temporal discrimination is widely used to index interval timing (Bausenhart et al.
![matlab 2008 psychportaudio matlab 2008 psychportaudio](https://media.springernature.com/lw685/springer-static/image/art%3A10.3758%2Fs13428-017-0961-z/MediaObjects/13428_2017_961_Fig3_HTML.gif)
An understudied issue in the temporal discrimination of interval pairs is when the context imposed by a stimulus interval ceases to affect the processing of a successive stimulus. Advances in our understanding of these timing mechanisms have been facilitated by studying these systems across a variety of stimulus ranges and contextual factors including emotional and attentional states, non-temporal stimulus properties (for a review, see van Wassenhove 2009) or the interval context arising from previous stimuli (Burr et al. The human brain uses multiple systems to process with various degrees of precision temporal information spanning timescales over ten orders of magnitude (Buhusi and Meck 2005). These results suggest that state-dependent networks sub-serving sub-second timing require approximately 250–333 ms for the network to reset to maintain optimal interval timing. This effect corroborates previous findings of a breakpoint in the discrimination performance for sub-second stimulus interval pairs as a function of an incremental inter-stimulus delay but more precisely localizes the minimal inter-stimulus delay range. We found that discrimination thresholds improved with the introduction of a 333 ms inter-stimulus interval relative to a 250 ms inter-stimulus interval in both duration discrimination tasks, but not in the control task.
![matlab 2008 psychportaudio matlab 2008 psychportaudio](https://pubs.asha.org/cms/asset/c638ffa2-3691-4171-a0a1-016cf3f28242/jslhr-s-16-0234cler_featimage.jpg)
Here, we probed the interval specificity of this reset boundary by manipulating the inter-stimulus interval between standard and comparison intervals in two sub-second auditory duration discrimination tasks (100 and 200 ms) and a control (pitch) discrimination task using adaptive psychophysics. However, the estimated boundary of this reset interval is broad (250–500 ms) and remains under-specified with implications for the characteristics of state-dependent network dynamics sub-serving interval timing. Previous research has shown that the approximate boundary of this reset interval can be inferred by varying the inter-stimulus interval between two to-be-timed intervals. State-dependent network models of sub-second interval timing propose that duration is encoded in states of neuronal populations that need to reset prior to a novel timing operation to maintain optimal timing performance.