An important aspect in timing and time perception research is investigating the ability to perceive and compare temporal intervals, that is, the study of duration discrimination (Bindra & Waksberg 1956; Grondin 2010; Matthews & Meck 2016). Just as in every perceptual domain, a central problem in this field is how the relation between physical stimulus input (e.g., a tone lasting for 500 ms) and the sensation evoked by this input (the perceived duration of this stimulus) can be quantified. The scientific study of this relation is called psychophysics (Fechner 1889; Gescheider 1997).
One fundamental issue in psychophysics is the measurement of the difference threshold (just noticeable difference, JND; difference limen, DL), or in other terms, discrimination sensitivity. It is often loosely defined as the minimal physical difference between two stimuli (e.g., a 500 ms vs. a 550 ms interval) that a participant can just notice. A second important concept in psychophysics concerns the magnitude of the sensation evoked by a given stimulus. Typically, this sensation magnitude is determined by identifying the physical magnitude of a stimulus that is judged to be equal to the magnitude of another stimulus defined as the standard stimulus. For example, one might pinpoint that an auditorily presented temporal interval must be 480 ms to appear as having the same duration as a visually presented standard interval of 500 ms duration. This point along the duration dimension is termed the point of subjective equality (PSE), and just as in the example above, it often does not correspond to the point of objective equality (POE), which indexes physical equality with the standard stimulus.
Although these definitions appear simple, the experimental determination of these indices of discrimination performance can be quite cumbersome. For example, PSE can be influenced by perceptual and decisional biases, and this may even depend on the specific procedures employed for data collection. For example, when a participant is asked to compare the duration of two
In this chapter, we review several of these tools and methods that are especially useful for measuring duration discrimination performance. Numerical examples are provided to illustrate these psychophysical procedures. In the first section, we introduce the standard psychometric function for comparative judgments and its associated parameters. We discuss various experimental paradigms, which are typically used to collect such data for assessing discrimination performance. In the second section, we present data collection and analysis methods based on equality judgments. For each type of judgment, we introduce several parametric and non-parametric procedures for computing indices of discrimination performance from these data, including exemplary Matlab scripts implementing these procedures (see book’s GitHub repository). In the final conclusion, we briefly review several advanced toolboxes available for assessing discrimination performance.
2 Comparative Judgments
Several of the experimental paradigms, which are typically employed in timing research, involve comparative judgments. Specifically, these judgments require that participants decide whether a given stimulus duration is longer or shorter than a certain target duration. For example, in the so-called reminder task, the participant receives two successive durations in each experimental trial. One of the two durations is the target duration that is kept constant across a block of trials. This duration is traditionally called the standard or reference duration s (Guilford 1954; Woodworth & Schlosberg 1954). The other duration varies randomly from trial to trial and is usually called the comparison or test duration c.
In most experiments, several different comparison durations are used, some larger than s and some smaller than s. Typically, between 6 and 12 different values of c are arranged symmetrically around s. It is convenient to index these
2.1 Fixed Order of Standard and Comparison Stimuli
Presumably the most elementary psychophysical approach uses a fixed order of s and c (e.g., Luce & Galanter 1963). For example, in the classical reminder task, s precedes c in every trial. Participants are typically asked whether the first or second stimulus appears longer, and consequently select the response R1 or R2, respectively. It is important to note that participants have to choose one of the two response alternatives in every trial – if a judgment cannot be made with certainty, the subject is asked to choose the alternative that seems most appropriate or simply to guess an alternative. After each trial, the experimenter simply records whether the participant responded with R1 or R2.
Table 3.1 contains an outcome example of such a psychophysical experiment comprising k = 9 comparison durations centered symmetrically around s = 500 ms. For these data, the relative frequency fi of responding with R2 as a
2.1.1 Probit Analysis
In order to enable a more comprehensive analysis of the data emerging from such an experiment, one typically fits a psychometric function Ψ(c) to the relative frequencies of R2 responses per c level (e.g., Luce & Galanter 1963).
where Φ denotes the cdf of a standard normal distribution, μ is the location parameter, and σ represents the slope of Ψ. This approach of modeling the psychometric function is also called probit analysis (Finney 1952).2
The parameter μ denotes the level of c at which the probability of responding with R2 is equal to 0.5, that is, at this level the two responses R1 and R2 are equally likely. This level is often called the PSE, because it denotes the duration of c, which is judged to have the same duration as s. The PSE needs not to be equal to s. For example, the PSE is often smaller than s because participants usually tend to overestimate the second duration compared to the first one, a phenomenon termed the time-order error (Eisler, Eisler, & Hellström, 2008; Köhler 1923). In general, the difference between objective physical equality and subjective equality has been termed constant error (CE) and has been defined as cE = PSE – s in the psychophysical literature. Shifts of the PSE away from the POE may reflect a perceptual or a decisional bias.
A second parameter of major importance that can be computed from a psychometric function is the DL or JND. This parameter indexes the discrimination sensitivity of a participant, with smaller values of DL indicating a higher level of sensitivity. The DL is related to the steepness of the psychometric function. It is typically defined as half its interquartile range, that is, DL =(c0.75 −c0.25)/ 2 where c0.75 and c0.25 represent the stimulus levels at which the response R2 is elicited with probability 0.75 and 0.25, respectively (Luce & Galanter 1963). Consequently, DL indexes the duration difference between s and c, which enables the subject to identify c as being either shorter or longer than s with an accuracy level of 75%. For the function embodied in Equation 1, the DL is given by DL −s σ·z0.75 (2)
where z0.75 is the 75% percentile of the standard normal distribution, i.e., z0.75 ≈ 0.6745.3
An especially efficient method for estimating the parameters PSE and DL is Fisher’s maximum-likelihood procedure. In brief, one uses Equation 1 to compute the likelihood of the observed data,
where n1,i and n2,i denote the frequencies of observed R1 and R2 responses at each comparison level (compare Table 3.1). The maximum likelihood estimates of μ and σ are those numerical values that maximize this likelihood function. The maximum of this function can be found numerically using a computer, a procedure known as numerical optimization.
A simple Matlab (R2016b) script (“MLEPsyProbit.m”) for performing this optimization is available (see book’s GitHub repository). It finds the parameters μ and σ at which the function L(Data | μ,σ) has its extremum. This script requires as input the vectors c = (c1,…,ck ) , ( ) 1 = 1,1,…, 1, ,n n nk and ( ) 2 = 2,1,…, 2,n and provides the maximum-likelihood estimates of PSE and DL together with their standard errors and their corresponding 95% confidence intervals as outputs. This script computes the standard errors from the observed Fisher information. Applying the script to the data in Table 3.1, one obtains PSE = 430.9 ms, Se = 13.3 ms with a 95%-confidence interval of CI = [404.9, 457.0], and DL = 57.6 ms, Se = 8.8 ms with CI = [40.5, 74.8]. On the basis of the PSE result, the script computes CE = −69.1 ms, Se = 13.3 ms with CI = [−95.1, −43.0]. The CE indicates a systematic overestimation of the comparisons relative to the standard duration s = 500 ms, which might be attributed, for example, to a negative time-order error. Figure 3.1 depicts the relative
2.1.2 Pseudo-Gaussian Function
Killeen, Fetterman, and Bizo (1997) proposed an alternative to Equation 1 that often provides an excellent fit to observed data (Allan & Gerhardt 2001; Birngruber, Schröter, & Ulrich, 2014; Grondin 2001). This approach takes Weber’s law into account, according to which variability in perceived duration should linearly increase with physical duration. Specifically, let S and C represent the internal representations of the standard s and the comparison c, respectively. In addition, assume that the internal difference ∆ = C − S follows a normal distribution with mean E[∆|c] = c − (ɛ + s), where the parameter ɛ has the status of a constant error. If the standard deviation of the difference ∆ follows Weber’s law σc =w Ã‚Â· c,w > 0 then the psychometric function is given by the Pseudo-Gaussian function,
where Φ again denotes the cumulative density function of a standard normal variable, and the parameters are the constant error ɛ and the Weber fraction w.4 This Pseudo-Gaussian function is actually not a genuine psychometric function because it does not converge to 1. However, this deviation from 1 is negligible for realistic values of w. The supplementary Matlab script “MLEPSyPseudoGaussian.m” (see book’s GitHub repository) provides maximum likelihood estimates of the parameters ɛ and w. Applying this script to the data in Table 3.1 yields for ɛ an estimate of −86.8 ms, Se = 12.3 ms, CI = [−110.8, −62.7] and for w an estimate of 0.190, Se = 0.026, CI = [0.138, 0.241].
Moreover, for this Pseudo-Gaussian function, it can be shown that the PSE is given by
and the DL by(6)
with z0.75 ≈ 0.6745. Inserting the above estimates into these equations yields PSE = 413.2 ms and DL = 53.7 ms. It can be noticed that these estimates differ numerically from the ones of the standard approach embodied by Equation 1, which must be attributed to the different assumptions underlying both models.
Figure 3.2 depicts the relative response proportions fi from Table 3.1 and the psychometric function resulting from the Pseudo-Gaussian model. A potential
2.1.3 Spearman-Kärber Method
In addition to the parametric approaches discussed above, one can also use a nonparametric approach, the Spearman-Kärber method (Kärber 1931; Spearman 1908), for estimating the location and the spread of the psychometric function (Miller & Ulrich 2001; Sternberg, Knoll, & Zukofsky, 1982). This method has several advantages. In contrast to parametric approaches, the Spearman-Kärber method does not require specific assumptions about the functional family of the true underlying psychometric functions. Also, it allows for estimating higher-order moments as skewness and kurtosis, in addition to location and spread of the psychometric function. Moreover, this method is computationally efficient compared to others, because it does not require an iterative fitting procedure. Finally, parameter estimates obtained with this method are often even less biased and less variable than parameter estimates obtained by employing parametric approaches (Miller & Ulrich 2001; Ulrich & Miller 2004).
In the Spearman-Kärber method, the range of comparison stimuli is subdivided into bins, each ranging from ci−1 to ci, for i = 1,...,k. The relative response frequencies fi associated with each stimulus level ci are assumed to be uniformly distributed within each corresponding bin. Thus, the probability density within each bin is estimated as ( fi – fi–1)/(ci – ci–1). The resulting histogram of probability densities approximates the continuous true cumulative distribution function underlying the data. Each rth raw moment m' r of this psychometric function can then be calculated as (7)
It must be noted that in this calculation, the values of the most extreme comparison levels c0 and ck+1 are not included in the actual experimental design but must be determined such that true values of f0 = 0 and f k+1 = 1 can be assumed.
This step is crucial whenever f1 > 0 or f
k < 1, that is, whenever the observed psychometric function is truncated (i.e., it does not start at 0 or reach 1). For example, this may be the case if the chosen range of comparison levels for testing was not broad enough to cover the whole range of the psychometric function. Similarly, lapses, finger errors, or simply binomial random error might cause such truncated psychometric functions. In this case, the specific values chosen for c0 and ck+1 will affect the cdf’s raw moments, and consequentially the
From the raw moments, one can derive estimates of location, spread, skewness and kurtosis (Miller & Ulrich 2001). For example, the first raw moment m' 1 corresponds to the arithmetic mean and, thus, indexes the location of the psychometric function (i.e., it serves as an estimate of PSE). The standard deviation of the underlying cdf can be estimated with .
The provided Matlab script “SpearmanKaerber.m” (see book’s GitHub repository) monotonizes the observed psychometric function and then computes the Spearman-Kärber estimates of PSE, σ, and DL for the example data contained in Table 3.1 (see Figure 3.3). By default, the extreme values c0 and ck+1 are set such that c1 – c0 = c2 – c1, and ck+1 – ck = ck – ck–1, that is, equidistance between the first 3 and the last 3 comparison levels is assumed. The script outputs the observed response frequencies fi and the monotonized response frequencies as well as a vector containing estimates of PSE, σ, DL, and CE. For the example data given in Table 3.1, the corresponding estimates are PSE = 433.5 ms, σ = 82.1 ms, DL = 55.4 ms, and CE = −66.5 ms. These parameter estimates correspond quite well with the estimates derived by the probit analysis described above. In addition, this function provides bootstrap estimates of these parameters based on 1000 replications, including standard errors and CIs. For example, for pse: Se = 12.1 ms, CI = [408.3, 455.4], for σ : Se = 10.6 ms, CI = [58.4, 100.4], for dl: Se = 7.2 ms, CI = [39.4, 67.7], and for CE: Se = 12.1 ms, CI = [−91.7, −44.6].5
2.1.4 Variants of Data Collection
In the preceding sections, it is assumed that in each trial a standard s is presented before the comparison duration c (i.e., reminder task). Especially in the domain of timing research, several variants of this basic task have been proposed (for an overview, see Grondin 2010).
First, in the single-stimulus method only the comparison is presented in each trial. The participant then classifies each comparison as either short or long, presumably against an internal standard that is quickly formed from experiencing the comparisons during the course of the experiment (Bausenhart, Bratzke, & Ulrich, 2016; Dyjas, Bausenhart, & Ulrich, 2012; Nachmias 2006; Woodworth & Schlosberg 1954). Sometimes, researchers also present a standard s for several times at the beginning of the experiment, in order to provide a more explicit reference for classifying the duration of each comparison as short or long. In either case, when the proportion of “long” responses is plotted against comparison duration, an ogive psychometric function will emerge. Estimating PSE and DL then can proceed in the same manner as in the standard approach outlined above.
Second, a further methodological variant of the standard approach is the bisection method. Here, at the beginning of the experiment the shortest (i.e., c1) and the longest (i.e., ck ) comparisons are presented several times as anchor stimuli. During the experiment, only comparisons are presented (as in the single-stimulus method) and the participant must classify each comparison as more similar to the short or to the long anchor duration (Allan & Gibbon 1991; Wearden, Rogers, & Thomas, 1997). The data analysis again proceeds as outlined above.
Third, in comparative judgments, researchers may allow for a third response option besides R1 and R2, i.e., an “uncertain” or “same” response (Woodworth & Schlosberg 1954, pp. 212–217). Historically, two response categories have been preferred over three response categories in psychophysics (Woodworth & Schlosberg 1954, p. 217). Nevertheless it is sometimes useful to employ three categories for theoretical reasons (e.g., Rammsayer & Ulrich 2001; Ulrich 1987) and more complex models of discrimination performance may be fitted to the data emerging from three-response categories to identify the relevant parameters indicating discrimination performance (García-Pérez 2014; García-Pérez & Alcalá-Quintana 2013).
Finally, all data collection variants as described above may be regarded as instances of the method of constant stimuli, in which the researcher preselects a range of comparison levels and typically presents each comparison level for a predetermined number of repetitions, with all trials presented in random order. This has sometimes been criticized as relatively inefficient, since many points along the psychometric function are sampled with an equal and large number of trials. Yet, some of these points, typically those demarcating threshold values as PSE and DL, are of especially high interest to the researcher, and an efficient data collection procedure might focus on assessing these points with high precision instead. Since the threshold values are of course not known in advance of testing, but depend on the participants’ performance, comparison levels then cannot be specified in advance. Rather, the experimenter’s decision about which comparison level should be presented in a given trial must depend on the participant’s responses given in previous trials. There is a vast number of data collection schemes and analysis variants for such adaptive testing procedures (see Kaernbach 1991; Leek 2001; Treutwein 1995), although some caution is required when applying these procedures (e.g., García-Pérez 1998).
2.2 Random Order of Standard and Comparison Stimuli
In the methodological variants for data collection described in the preceding section, the temporal order of s and c is either the same in each experimental
In both cases outlined above, however, the common practice of collapsing the raw data across the two orders of s and c can lead to loss of information and even to severe distortions in the estimated parameters of the psychometric function. To avoid such distortions, data from the two stimulus orders 〈sc〉 or 〈cs〉 should be plotted and analyzed separately (Ulrich 2010; Ulrich & Vorberg 2009). Consequently, two order-dependent psychometric functions emerge in the 2AFC design (cf. Figure 3.4). Specifically, let S1 and S2 denote the stimulus in the first or second position, respectively. Define F1(c) ≡ P(R1 | 〈cs〉) and F2(c) ≡ P(R2 | 〈sc〉) as the conditional probability with which the participant judges the comparison c as the larger of the two stimuli when it was presented first or second, respectively. Note that the two conditional psychometric functions monotonically increase with c.
Importantly, these two conditional psychometric functions can differ in their location (“Type A order effect”) and in their spread (“Type B order effect”). A prominent example for a Type A order effect is the typically observed negative time-order error, in which the duration of the first of two subsequently presented intervals is underestimated compared to the second one (Eisler et al. 2008; Hellström 1985; Köhler 1923). Specifically, this would correspond to
The preceding explanation assumes that researchers choose the stimuli presented in a 2AFC task such that they vary only along a single stimulus dimension. In a duration discrimination task, for example, s and c would be identical in all respects, except for their duration. In this case, a restriction emerges for the estimated psychometric functions. Specifically, averaging the two conditional functions results in an aggregated psychometric function,(8)
At the POE, defined as s = c, this equation simplifies to(9)
Since R1 and R2 are the only response alternatives, their associated response proportions must sum to one. Consequently,(10)
that is, the average of the two order-conditional psychometric functions must pass through the point (s, 0.5). This restriction must be considered when fitting psychometric functions to the order-conditional data. Specifically, instead of estimating two independent psychometric functions, they must be fitted simultaneously and the number of the free parameters to be estimated for these two functions reduces to three (Ulrich 2010; Ulrich & Vorberg 2009). Matlab and R code for fitting logistic order-conditional psychometric functions under this restriction is provided by Bausenhart, Dyjas, Vorberg, and Ulrich (2012).
If a researcher chooses to let s and c vary along more than one dimension (e.g., in duration and stimulus size), then of course the constraint implied by Equation 10 does not hold, and the average function will pass through the point (
PSE, 0.5) instead (García-Pérez & Alcalá-Quintana 2011). Then, the two order-conditional psychometric functions can be estimated independently from each other, just as outlined in the section on fixed order of standard and comparison stimuli above. The routines provided by Bausenhart et al. (2012) also provide the option to release the constraint at s = c and therefore can also be employed for the analysis of order-conditional data coming from 2AFC tasks which vary along multiple stimulus dimensions.
3 Equality Judgments
Besides the comparative judgment task employed in the preceding methods, equality judgments as in the temporal generalization method are also often used in the domain of temporal cognition (e.g., Wearden 1992; Wearden, Edwards, Fakhri, & Percival, 1998). In the temporal generalization method, the standard s is usually presented for several times at the beginning of an experiment. After s has been initially presented, the participant receives in each trial a comparison duration ci, as before spaced below and above the standard. After each presentation of a comparison duration, the participant has to judge whether this duration was the same as the standard or different, by responding with Rsame or Rdifferent, respectively. Alternatively, the standard and the comparison may be presented in each trial, and the participant is also asked to judge whether the two stimulus durations are equal, Rsame, or not equal, Rdifferent (see Birngruber et al. 2014; Dyjas & Ulrich 2014). Table 3.2 contains example data for such an equality judgment task. When the relative frequency of a same response is plotted against comparison duration, an approximately bell-shaped psychometric function emerges. As before, there are various methods available to summarize such data.
3.1 Same-different Model with Constant Standard Deviation
First, a parametric method has been suggested by Schneider and Komlos (2008). These authors have assumed that subjects base their judgment on the difference ∆ = C – S between the internal representation of the comparison and the standard and respond with Rsame if | ∆ + ɛ | < γ and otherwise with Rdifferent. The parameter γ denotes a constant threshold value and ɛ the constant error.
Again the maximum likelihood method can be used to obtain estimates of γ, ɛ, and σ from the observed data. The supplementary Matlab script “MLESameDifferent.m” performs this analysis (see book’s GitHub repository). Applying this procedure to the data of Table 3.2 yields γ = 62.0 ms, Se = 7.1 ms, CI = [48.1, 75.9], ɛ = −42.9 ms, Se = 9.1 ms, CI = [−60.6, −25.1], and σ = 53.7 ms, Se = 7.6 ms, CI = [38.8, 68.7]. Figure 3.5 depicts the resulting psychometric
3.2 Same-different Model with Standard Deviation Dependent on Comparison Level
The model underlying Equation 11 implies a symmetrical bell-shaped psychometric function. However, experiments employing the temporal generalization method or the standard procedure of presenting s and c in fixed order in each trial, typically generate asymmetrical psychometric functions with a positive skew (Birngruber et al. 2014; Wearden et al. 1998; Wearden 1992). In order to account for this asymmetrical shape, one may as before (i.e., Pseudo-Gaussian Model) assume that the standard deviation σ in the preceding Equation 11 increases with the comparison level c, i.e., σc = w • c (see Birngruber et al. 2014),(12)
Figure 3.6 displays the estimated function for this model variant when it is applied to the example data in Table 3.2. The parameter estimates, derived by the supplementary Matlab script “MLESameDifferent2.m” (see book’s GitHub repository), are γ = 61.5 ms, Se = 7.1 ms, CI = [47.7, 75.3], ɛ = −52.3 ms, Se = 9.0 ms, CI = [−69.9, −34.6], and w = 0.118, Se = 0.016, CI= [0.086, 0.150]. Because the predicted shape of this psychometric function is asymmetrical and influenced by the Weber fraction, it is difficult to properly define a measure of PSE. However, similar to the previous definition, one may again compute PSE = s + ɛ . Discrimination sensitivity is reflected in the parameter w, i.e., the Weber fraction. Due to the asymmetry of the underlying psychometric function, this measure should be used to index sensitivity.
3.3 Waveform Moment Analysis
The preceding two procedures involved a parametric approach to the analysis of data emerging from equality judgments. The Waveform Moment Analysis enables a non-parametric approach (Cacioppo & Dorfman 1987). Let fi be the observed relative frequency of a Rsame response associated with comparison level ci. In a first step, these frequencies are converted to a probability distribution pi, i = 1,…,k, by the following transformation,
In a second step, the mean μ and the standard deviation σ are computed for this “probability distribution”, that is,
The parameter m assesses the location of the psychometric function on the abscissa and thus can be interpreted as PSE, whereas the parameter σ captures the spread of this function and thus reflects discrimination performance with smaller values of σ indicating a higher level of discrimination sensitivity. Applications of the waveform moment analysis in temporal discrimination have been reported by Birngruber et al. (2014) and by Dyjas and Ulrich (2014). A Matlab script for performing this analysis (“WaveformMoment.m”) is available as supplementary material (see book’s GitHub repository). For the data in Table 3.2, one obtains μ = 456.1 ms, σ = 63.8 ms, and thus CE = −43.9 ms. This script also computes the standard error and confidence intervals for these parameters by the bootstrap method. For example, one obtains for μ: Se = 8.7 ms, CI = [438.5, 472.5], for σ : Se = 5.2 ms, CI = [52.6, 72.8], and for CE: Se = 8.7 ms, CI = [−61.5, −27.5] (see also Figure 3.7).
It must be kept in mind, however, that perceived duration still can only be indirectly inferred from changes in the PSE, since the PSE reflects not only changes in perceived duration, but also decisional and response biases, and, therefore, this parameter should be cautiously interpreted in terms of judged duration rather than perceived duration. The use of a 2AFC task has the additional advantage that one can isolate the effects of secondary experimental manipulations from the time-order error by analyzing the order-conditional psychometric functions. Also, unbiased estimates of DL can be achieved by assessing the slope of the order-conditional functions.
Traditionally, comparative judgments have been used most often to measure both DL and PSE. However, equality judgments may of course also be employed, and might be especially useful to assess the robustness of experimental effects. For example, consider that one is interested in whether an experimental manipulation influences perceived duration. If similar PSE effects can be observed for comparative and equality judgments, this might strengthen the notion that the manipulation affects perceived duration rather than decisional processes (e.g. Birngruber et al. 2014; Dyjas & Ulrich 2014).
Supplementing this chapter, we provided basic Matlab scripts to illustrate the various psychophysical procedures for newcomers to the field of time perception (see book’s GitHub repository). It must be mentioned, however, that elaborated psychophysical toolboxes are available for data analysis (see Table 3.3). We also refer the reader to comprehensive manuals on psychophysical methods for information about details of these toolboxes (Kingdom & Prins 2010; Lu & Dosher 2014). For example, the toolbox developed by Wichmann and Hill (2001) also allows the estimation of lapses in designs with comparative judgments. The toolbox Palamedes described in Kingdom and Prins (2010) also includes Matlab scripts for adaptive psychophysical procedures. Finally, the Matlab script by Bausenhart et al. (2012) is recommended for fitting psychometric functions conditional on stimulus order in 2AFC tasks.
In this chapter, we focused on psychophysical tools and procedures to obtain and analyze psychometric functions. This is sometimes considered as the classical psychophysical approach. An alternative approach for characterizing discrimination performance is offered by Signal Detection Theory (sdt; Green & Swets, 1966). Interestingly, in the domain of time perception, the psychophysical tools from sdt are much less often used than the classical tools described in this chapter. One major reason why time perception researchers usually prefer the classical tools is that sdt does not provide a parameter like the PSE that would allow to estimate judged duration. This is perhaps not surprising since sdt was mainly developed to identify near-threshold stimuli, an issue that does not apply to time perception. Furthermore, we did not address duration scaling methods as temporal reproduction, production, or verbal estimation, which are also often used to investigate duration perception (e.g., Allan 1979; Bindra & Waksberg 1956; see also Chapter 4 of this book). However,
In sum, we hope that the present chapter will direct beginners with little or no background in psychophysics to the most important paradigms and
Usually the two extreme values in the range, c1 and ck, are selected in such a way that the comparisons cover the full range of the psychometric function from 0 to 1. Weber fractions may help to select these values. For example, assume that s = 500 ms and the participant is asked to discriminate auditory intervals, for which the Weber fraction typically amounts to approximately 0.1 (Rammsayer 2010; Rammsayer & Ulrich 2012). As a rule of thumb, c1 may be selected as s · (1 − 4 · 0.1) and ck as s · (1 + 4 · 0.1). For s = 500 ms, this would yield c1 = 300 ms and ck = 700 ms.
Other functional families than the normal distribution are often used to model the psychometric function, such as the logistic or the Weibull function. However, the logistic and the probit model produce virtually the same results (Lord, Novick, & Birnbaum, 1968, p. 399).
Several researchers (Treutwein 1995; Treutwein & Strasburger 1999; Wichmann & Hill 2001) have suggested to include also lapse parameters in the estimation of psychometric functions to account for trials in which the participant commits stimulus-independent lapses due to phasic inattention or “finger errors”. These events will result in scaled psychometric functions, which do not cover the full range from 0 to 1. Even though such processing failures are rare events, typically estimated to occur in between 0% and 5% of trials (Wichmann & Hill 2001), their presence can nonetheless distort the estimation of DL. Therefore, if empirical evidence suggests the presence of lapses, corresponding extended psychometric functions should be used for data analysis (Wichmann & Hill 2001, also see Table 3.3 for a list of tools available for performing such advanced analyses). Models comparison statistics can be used as a principled way of choosing the function with or without lapses.
As a further extension, one may replace ￼ by the generalized Weber’s law￼ (see Killeen et al. 1997). A similar model has been proposed by García-Pérez (2014). Also note that for w1 = w2 = 0 this extended model becomes a special case of the probit model discussed above.
Naturally, these bootstrapped values will randomly fluctuate with each execution of the provided Matlab function.
Participants are usually not aware that there is a constant standard, which appears first or second.