Procedural learning

In studies with speech perception tests, procedural learning is usually defined as improvements that are not related to learning the content of the speech material, but are, instead, due to familiarization with the test procedure, the listening environment, the talker’s voice, the characteristics of the test material (e.g., types of test words or sentences or properties of the test noise), or answer tactics (Theodoridis and Schoeny, 1990; Yund and Woods, 2010)

For speech intelligibility tests that use words or a fixed set of everyday sentences as speech material, procedural learning can be estimated by evaluating improvements in performance when listeners are presented with unique, unfamiliar test items of equal intelligibility. Alternatively, procedural learning can be estimated using a restricted set of familiar test

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items (i.e., digits), provided that all the test items are equally intelligible and familiar enough that any content learning is unlikely. When evaluating procedural learning, prior experience with different speech intelligibility tests should also be considered. Even though the test material may be new to the participant, prior familiarity with psychoacoustic test procedures may affect the amount of procedural learning.

To reliably capture the full extent of procedural learning, the reference point should be the listener’s very first exposure to the test material and test procedure. Table 5 presents an overview of studies on procedural learning for speech perception tests in noise that use words or everyday sentences and clearly state the first test measurement as the reference point. The discussion in the following chapters will concentrate on these studies while leaving out studies with extensive or undefined pre-test training (e.g., (Munro and Lutman, 2005; Nielsen and Dau, 2011; Simonsen, 2016)) or studies where the reported SRTs were the means of multiple measurements (Vaillancourt et al., 2008), as this is likely to conceal or confound the improvements related to procedural learning.

While most of the studies included in Table 5 were designed specifically to assess learning effects for a speech perception test, some data are byproducts of other studies. For example, Theodoridis and Schoeny (Theodoridis and Schoeny, 1990) conducted re-analyses on data that was originally collected to assess the effect of context on speech perception in noise to assess learning effects in naïve, NH university students.

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Table 5. Procedural learning for some word- and sentence-level speech perception tests in noise. Reference Test Listeners

No. and timing of test sessions

No. of test items in a test session

Test protocol Reference point Total No. of test items presented

Procedural learning Theodoridis and Schoeny, 1990

CID W-22 words

144 NH, no prior experience 2 sessions 0-6 days apart 38-54 test words Word or sentences presented at 6 increasing SNRs

Compound score from first test session

456-648 words 1.71 dB Theodoridis and Schoeny, 1990

CID W-22 words

45 NH, no prior experience 3 sessions, timing not specified 39-44 test words Word or sentences presented at 6 increasing SNRs

Compound score from first test session 756 words

1.03 dB (1st -2nd session), 1.25 dB (2nd -3rd session) Burns and Rajan, 2008 BKB(A)300 NH, no prior experience

6 sessions, different timing for each group, range 2 min to 21 d

1 list

Adaptive test procedure (keyword scoring)

First test list90 sentences

Yes, irrespective of the length of test session intervals

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Table 5. Continued. Reference Test Listeners

No. and timing of test sessions

No. of test items in a test session Test protocol Reference point

Total No. of test items presented

Procedural learning Cainer, James and Rajan, 2008 BKB(A)

58 NH, prior experience not specified 6 sessions 15 min apart 1 list Adaptive test procedure (keyword scoring), babble noise or speech- shaped noise

First test list90 sentences

1.6 dB SNR (babble noise), 0.9 dB SNR (speech-shaped noise) Rhebergen, Versfeld and Dreschler, 2008

Sentences 8 NH, no prior experience 1 20 lists: 10 with male and 10 with female speaker (5 in stationary and 5 in interrupted noise)

Adaptive test procedure, four types of sentences in balanced blocks First test list of each block of 5 test lists

260 sentences (65 sentences per test block)

3.4 dB SNR (fluctuating noise), 0.1 dB SNR (steady noise) Yund and Woods, 2010QuickSIN

23 NH, experience with at least CVC test 3 sessions in 2 weeks 6 lists 6 QuickSIN lists with standard QuickSIN procedure

First test list108 sentences0.14 dB SNR

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Table 5. Continued. CID W-22, Central Institute for the Deaf W-22 lists; BKB(A), Australian Bamford-Kowal-Bench speech perception test; QuickSIN, Quick Speech in Noise Test; HINT, Hearing in Noise Test; AzBio, AzBio speech perception test in noise; CVC, consonant-vowel- consonant speech perception test; NH, normal-hearing; SNR, signal-to-noise ratio; dB SNR, dB signal-to-noise ratio.

Reference Test Listeners

No. and timing of test sessions

No. of test items in a test session

Test protocol Reference point Total No. of test items presented

Procedural learning Yund and Woods, 2010HINT

23 NH, experience with at least CVC, QuickSIN

3 sessions in 2 weeks 4 lists 4 HINT lists, standard HINT procedure First test list240 sentences0.4 dB SNR Stuart and Butler, 2014HINT

25 NH, prior experience not specified

5 sessions, timing not specified 2 list: 1 in interrupted and 1 in continuous noise

Standard HINT procedure with two different noises First test list 50 sentences in interrupted and continuous noise, each

No Buss, Calandruccio and Hall, 2015

AzBio

37 NH, no prior experience with AzBio, 2 sessions, timing not specified

Not specified 5 lists: Each sentence repeated at 5 ascending fixed SNRs until recognized. 5 lists: Each sentence presented once at varying SNRs

First test block On average 450- 525 sentences, the number depended on the ease of speech perception

0.3 dB SNR per test block

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All the studies in Table 5 were conducted with NH listeners and the number of participants ranged from eight to 300. Studies with a large number of participants included multiple different test conditions, and the participants were divided into smaller groups with each group assigned to a different test condition (Theodoridis and Schoeny, 1990; Burns and Rajan, 2008). Therefore, most of the data on procedural learning come from groups of 10–20 listeners. The typical number of test sessions was two or three, and only a few studies had five or more (Burns and Rajan, 2008; Cainer, James and Rajan, 2008; Stuart and Butler, 2014). In all studies, the test sessions were separated only by days or a few weeks, leaving the test intervals considerably shorter than typical appointment intervals in the clinic.

The HINT (Nilsson, Soli and Sullivan, 1994) is one of the most widely translated speech perception tests in noise, and it also has the most data on procedural learning. Interestingly, no studies have been specifically designed to assess learning effect for the AzBio (Spahr et al., 2012), even though it is currently one of the most widely used sentence-level speech perception tests in the US (Carlson et al., 2018). The only data on learning effect for the AzBio are accidental findings of procedural learning in a study by Buss, Calandruccio, and Hall (Buss, Calandruccio and Hall, 2015) that was designed to evaluate the use of ascending, fixed SNR levels on speech intelligibility test performance, not learning effects, per se.

In regard to the amount of procedural learning, the results from the studies are mixed (see Table 5), since while some studies detected significant procedural learning (e.g., (Theodoridis and Schoeny, 1990;

Rhebergen, Versfeld and Dreschler, 2008)), others failed to detect any (Yund and Woods, 2010; Stuart and Butler, 2014). Differences in participants’ prior experience could explain some of the discrepancy.

Procedural learning requires the listener to learn the listening and answer tactics best suited for a certain test, and it is, to a point, test specific.

However, listeners with prior experience with other speech perception tests and psychoacoustic testing may reach their optimal performance

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level quicker, which can affect the amount and rate of observed procedural learning. No conclusions on the role of prior experience can be made based on the available data, since while some studies state clearly that participants had no prior experience with psychoacoustic tests, other studies included no reports on previous experience.

Based on the available data, procedural learning does not appear to be specific to certain speech perception tests or test types, since it was

observed for multiple different tests with different speech material (words, simple everyday sentences, complex everyday sentences), different

background noise (speech-shaped noise or babble noise), and different test protocols (adaptive or fixed level presentation) (Table 5). It seems plausible that a study protocol where the same speech perception test is administered during multiple, closely scheduled test sessions, would generate more procedural learning than a study protocol, where the test sessions are scheduled far apart, and the test material is presented only once during the test session. However, data from the only study that specifically assessed the effect of different inter-session intervals (Burns and Rajan, 2008) do not corroborate this. During each test session, Burns and Rajan (Burns and Rajan, 2008) presented a single BKB(A) test list, and observed that changing the inter-session interval from 2 minutes to 21 days had no effect on the overall amount of procedural learning.

Theodoridis and Shoeny had also tested their participants at different intervals (of less than 24 hours to up to six days) and found that the length of the inter-session interval had no effect on the amount of procedural learning (Theodoridis and Schoeny, 1990).

For MSTs, dividing the learning effect into procedural and content learning is not possible. The same word matrix is used for each test list, and repeating the test lists results inevitability in content learning.

However, procedural learning seems likely for MSTs, as the listeners may, for example, learn to take advantage of the regular syntax and rhythm of the sentences to better guess partially heard words. The closed-set test protocol especially can predispose to procedural learning, as listeners are

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likely to become familiar with the visual presentation of the word matrix and use it more efficiently to guess partially heard words. Only a few studies have assessed the learning effect for closed-set application of the matrix sentence tests, and the studies contain data only from one test session (Hochmuth et al., 2012; Puglisi et al., 2015; Warzybok, Zokoll, et al., 2015). None of the studies detected any statistically significant differences in the learning effect between the closed-set and open-set test protocol, but long-term learning effects were not evaluated.

Since the test material for DTTs is familiar, clearly defined, and easy to keep active in the memory during the test, content learning is unlikely, and all learning related improvements in the DTT results can be attributed to procedural learning. Learning has been reported for DTTs within one test session, but it has either been non-significant or statistically and clinically significant only between the first two test lists (Smits, Theo Goverts and Festen, 2013; Vlaming et al., 2014; Han et al., 2020; Vroegop et al., 2021).

When procedural learning was observed, the pattern was similar for both DTTs and tests that use everyday sentences as test material;

improvements were most marked during the first test measurements and plateaued quickly thereafter (Burns and Rajan, 2008; Yund and Woods, 2010; Smits, Theo Goverts and Festen, 2013). In general, the studies did not quantify the amount of learning in relation to the reference value or the SD. Comparing the amount of learning between different tests is difficult, since SRTs and speech recognition scores are test-specific and, therefore, not directly comparable. Contrasting the learning effect to the test-specific SD could help in comparing the amount of learning between studies, but unfortunately SDs were not available for all the tests. While most studies only stated the size of the learning related improvements or their absence, Rhebergen, Versfeld, and Dreschler (Rhebergen, Versfeld and Dreschler, 2008) developed a mathematical model to estimate the procedural learning based on their studies.

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Procedural learning has been evaluated for HI listeners in some studies, for example by Simonsen (Simonsen, 2016) and Nielsen and Dau (Nielsen and Dau, 2011). Neither study detected significant procedural learning effects for the analyzed test lists, but both studies included multiple training lists that were excluded from the analysis.