PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Reinforced statistical learning of auditory categories: A preliminary
report of cognitive, cortical and computational mechanisms
Bob MCMURRAY1; Samantha CHIU1; John FREEMAN1
1 University of Iowa, USA

ABSTRACT
Learning to categorize speech sounds is crucial for language acquisition and a struggle for second language
learners. Most research has examined supervised learning in which learners receive feedback. This offers a
useful model for second language acquisition. However, work in first language acquisition suggests people
may acquire categories by tracking the statistics of the input without feedback. Few studies examine howthese forms of learning work together and little cognitive neuroscience examines unsupervised learning. To
study this interaction, we developed the reinforced statistical learning paradigm in which learners are first
exposed to statistically structured stimuli in an unsupervised paradigm and then receive supervised training.
We present preliminary data from two studies using this paradigm. It suggests that the unsupervised phasebenefits later supervised learning, but only in limited conditions that may vary according to whether items
are blocked or interleaved. We examined the cortical consequence of learning with electro-corticographic
recordings from the surface of the brains of two epilepsy patients. A machine learning analysis suggests somesurprising ways in which auditory cortical representations change with learning. This suggests the need fortheories that explain the varied effects of unsupervised learning on auditory processing.
Keywords: Hearing, Categorization, Learning, Unsupervised Learning

1. INTRODUCTION
Speech perception is built on categorization. Phonemes are defined by many variable acoustic cues,and listeners must generalize across talkers and contexts. Understanding how auditor y categories arelearned is crucial for real-world problems. Deficits in phonological processing (based in part on
auditory categorization) have been linked to developmental language disability and dyslexia (1).
Additionally, in second language (L2) acquisition, many adults never learn to discriminate non-native
sounds (2). Finally, hearing remediations like cochlear implants distort the input, requiring users torelearn auditory categories. Thus, auditor y category learning has broad impacts.
Substantial work has investigated the acquisition of auditory categories (3). Most examinessupervised learning (via reinforcement) (4-7). This is a useful model for second language acquisition,
where sound categories can be explicitly taught, and learners have access to vocabulary, orthography
and articulation to serve as implicit supervisory signals. However, in first language acquisition, infants
may not have access to such signals, and may learn via unsupervised statistical learning (8).
Supervised and unsupervised learning may need to work together. Adult L2 learners can use
unsupervised statistics (9). Moreover, L1 acquisition extends through adolescence (10), a time when
top-down knowledge (e.g. spelling, vocabulary) is available to guide learning. However, there is little
work on how supervised and unsupervised learning relate. This manuscript describes preliminar y
results from ongoing investigations of this interaction. We develop a new paradigm, reinforced
statistical learning to investigate the ways in which these modes of learning interact. We start byoffering a brief background on both forms of learning before presenting results from three experiments.
1.1 Mechanisms of Learning
Supervised learning (using feedback or reinforcement) has been studied as a model of L2 learning1

bob-mcmurray@uiowa.edu

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Figure 1. A) Stimuli are two-tone complexes. A category consists of a set of stimuli whose
frequencies come from a Gaussian distribution. B) In square configurations, boundaries are drawn in one
dimension. C) In diamond configurations, boundaries require integrating both. D) Reinforced statisticallearning starts with passive exposure to stimuli from one configuration, followed by supervised training
on a configuration that either matches (top) or mismatches (bottom) the unsupervised phase. E) As acontrol, we hold the boundaries constant during supervised learning and change the prototypes.
in adults. It has been implemented in operant paradigms in which learners hear a stimulus, choose acategory and receive feedback (4-7), and in incidental paradigms where learners associate sounds withunambiguous visual cues (11). The type of feedback matters (12), and operant training is more robust
than incidental (11). One theoretical model that has been recently been applied to auditory categories
is COVIS (13), which posits separate learning systems for categories defined by a boundar y along asingle feature (rule-based or RB categories), and for categories whose boundaries integrate several
dimensions (information integration or II categories). II categories are more difficult to acquire (7,11), and unlike RB categories, they may require supervision.
In contrast, work in first language acquisition has focused on unsupervised statistical learning (8,14). Infants rapidly tune into the categories of their language before they can produce speech or know
many words that could serve as a source of feedback. Thus, most accounts suggest infants learn
categories by tracking the statistics of the individual acoustic cues. For example, Voice Onset Time
(VOT) is the most important cue for distinguishing /b/ and /p/. Across utterances the distribution of
VOTs in English displays a bimodal distribution with one cluster corresponding to /b/ and the otherto /p/. Infants could thus track the frequency of individual VOTs and identify such clusters to learn
the categories of their language. Experiments show that passive exposure to stimuli that form suchstatistical clusters can alter discrimination in infants and adults (8, 15, 16).
1.2 Reinforced Statistical LearningThis manuscript presents preliminary results from work on the Reinforced Statistical Learning
paradigm, which combines unsupervised and supervised learning. Our initial implementation captures
a sequence characteristic of L1 acquisition, in which supervised learning builds on prior unsupervised
learning. Natural speech categories are difficult to fully control and hard for adults to learn due tointerference from existing categories. Thus, we use non-speech categories (11, 16) which offer a usefulmodel. Stimuli are two-tone complexes (Figure 1A). For any exemplar, the frequency of each tone is
randomly generated from a Gaussian distribution reflecting the prototype structure of the category.
We taught subjects four-categories in a two dimensional space. Categories can be arranged in multiple
ways (c.f., 11). For example, categories can be arranged in a “square” with boundaries along individual
dimensions (RB categories; Figure 1B), or in a diamond with diagonal boundaries (II, Figure 1C).
Learners start with passive exposure to a series of stimuli from one configuration. They next lear n
the labels for each category in a supervised paradigm. The boundaries during this phase can match or
mismatch the unsupervised configuration (Figure 1D). We infer unsupervised learning as a differencein performance as a function of whether the boundaries match across phases. Of course, a mismatch
effect could also reflect the fact that the statistical distribution of the stimuli has shifted (not just themismatch between the boundaries and the unsupervised distributions). We thus manipulate a third
factor, shifting the locations of the prototypes across phases but not the boundaries (Figure 1E).

2. Experiment 16588

2.1 Methods
Subjects. 159 normal hearing adults were recruited from the University of Iowa community. The
intended sample size is 240 (30 / condition), so this should be treated as a preliminar y report.
Design. Subjects were randomly assigned to one of 8 conditions that crossed three factors: 1) the
configuration of the prototypes during the unsupervised phase (square or diamond); 2) the nature of
the boundaries during supervised training (horizontal/vertical vs. diagonal); 3) the distribution of
tokens during supervised training (which was included to control for fact that a mismatch between
unsupervised and supervised learning could reflect either the boundaries or the prototypes).
During the unsupervised training, participants heard 700 tokens (175 / category) over about 30
minutes. Stimuli were blocked by category. This was followed by 336 trials of supervised training inwhich categories were fully interleaved (156 / category). Every 72 trials, subjects had 12 test trials(termed periodic test trials) on the prototypes of each category (defined by the boundaries).
Stimuli. Stimuli consisted of two tone complexes. Each was 750 msec long with a 50 msec ramp.
Stimuli were generated from a multi-modal Gaussian distribution which specified the mean of eachof the two tones. Stimuli were unique on each trial for each subject and generated in bark space. In
the square configuration, these means were [3, 9.5], [3, 11.5], [5, 9.5], [5, 11.5]. In the diamondconfiguration [4, 9.09], [2.59, 10.5], [4, 11.91], [5.41, 10.5]. The standard deviation in bothconditions was set to such that the means in each dimension were 5.5 standard deviations apart.
Procedure. After providing informed consent, participants underwent unsupervised training. Thiswas conducted in a sound attenuated room with stimuli delivered at a comfortable listening level overhigh quality headphones. They were given no particular instructions, and colored in a book during
this time. After passive training, the supervised portion began. On each trial, participants heard asingle stimulus and saw two novel objects on the screen – one corresponding to the target category,
and the other randomly selected from the other three. They then clicked on the matching object, andheard a buzz or a soft beep to indicate if their response was incorrect or correct, respectively.
2.2 Results
Figure 2A shows accuracy over the course of supervised training with trials binned into blocks.
Subjects learned this task very rapidly reaching asymptotic performance by around 100 trials.
Contrary to the predictions of COVIS (13) subjects learning diagonal (II) boundaries learned morerobustly than those learning vertical/horizontal (RB) boundaries.
Figure 2B shows performance on the periodic testing trials (which were equated on difficulty) as
a function of condition. There was little change in accuracy over time (since the first periodic testingtrial occurred at trial 72, at which point subjects were near asymptote). Thus we collapse across block.
Figure 2B again shows stronger performance for the diagonal than vert/horz boundaries: the two setsof bars on the left exceed those for the right. There is also an effect of the supervised configuration –when learning the diagonal boundaries, the diamond configuration (which matched the prototypes for
those boundaries) exceeded the square condition (where the training exemplars overlapped theboundaries). The reverse was seen for the vert/horz boundaries. Finally, when learning the diagonalboundaries, there was a small benefit when the unsupervised exposure used the matching, diamondconfiguration. However, there appeared to be no effect of unsupervised learning for square boundaries.
This was evaluated statistically in a logistic mixed effects model that evaluated the effect of test
block, boundar y, unsupervised configuration and supervised configuration (all centered) on
performance. This model included random intercepts of subject, and a random slope of block (themaximal model). This model should be interpreted cautiously as planned sample sizes have not beenachieved. Thus, we only report the most important results. We discuss results pertaining to thesupervised and unsupervised portion separately.
Effects on Supervised Learning. We found a highly significant main effect of boundary (B=-.55,
SE=.092, Z=5.93, p<.0001): diagonal boundaries were learned better than square. We also found an
effect of the location of the supervised prototypes relative to the boundary (e.g., Figure 1E), thesupervised configuration × boundary interaction (B=.77, SE=0.19, Z=4.18, p<0.0001). This was dueto the fact that diagonal boundaries showed better learning when the supervised configuration was
diamond than square, and while vert/horz boundaries showed better learning with square. Learning
was better when the frequently trained regions were in the center of the categor y defined by theboundary (Figure 1E, top), than the when they were along the boundary (Figure 1E, bottom).
Unsupervised learning. Contrary to our predictions, unsupervised configuration did not interact
with the boundaries (B=0.146, SE=0.19, Z=0.78, p=0.43). While the means suggested a benefit when

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Figure 2. A) Performance on all trials during supervised learning as a function of boundary and trial
block. B) Performance on periodic testing trials as a function of unsupervised configuration (red vs. bluebars), supervised configuration, and boundary. Inset boxes show the supervised configuration andboundary for each pair of bars. Error bars are standard error of the mean.
the unsupervised prototypes match the boundary, there was little statistical support for this.
Given that the unsupervised training was blocked by category, one possible explanation for theseweak effects is a recency effect whereby only the last category shows any evidence for unsupervised
learning (the earlier exposed ones either decayed or were overwritten by the later one). Indeed, Figure
3A suggests this might be the case. In panel A, the final category shows a large benefit of unsupervised
exposure: when learning diagonal boundaries, performance showed a 10% gain for unsupervised
learning in the diamond configuration; whereas no such effect was seen with square. In contrast, forthe earlier three categories (Figure 3B), there is no effect of unsupervised training.
To assess this we conducted an additional mixed effects model which was structured similarly tothe prior one, but with an additional term indicating whether the tested category was the final categor yor not. This was an exploratory (unplanned) analysis so results should be interpreted cautiously. Here
we observed a significant three way interaction between boundary, the unsupervised configuration
and whether or not we were testing the final category in the unsupervised phase (B=0.58, SE=0.27,
Z=2.17, p=0.030). Thus, when unsupervised learning is blocked, learning may be limited to the last
category presented, and may further only appear in the diamond configuration.

Accuracy

Accuracy

2.3 Discussion
People quickly and easily learned the categories during the supervised training. Further, when we
consider the factors affecting supervised learning only we saw two key patterns. First, contrary to
COVIS, learning was better when the boundaries were diagonal (II). Second, we found an unexpected
effect of the supervised configuration (e.g., Figure 1E). In particular, participants learned better whensupervised training focused on stimuli in the middle of the categories, rather than when they split theboundary. Recall that the periodic testing trials were the same in all conditions (and equally easy –they were in the center of the categories defined by the boundary). Thus, this effect represents agenuine effect on learning, not a confound of difficulty. This pattern of results is consistent with theidea that learners may be acquiring prototypes and not boundaries (regardless of the type of boundary).

Figure 3. A) Performance on periodic testing trials as a function of unsupervised and supervised
configurations, and boundary (diagonal vs. vert/horz), for the final category in the unsupervised phase.
B) For all other categories. Error bars represent standard error of the mean.

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That is, if people were learning boundaries or rules, one would expect that training which focused on
the region of the space near the boundary would yield better performance (e.g. the bottom
configuration in Figure 1E). However, instead, training that focused on the region of the space at thecenter of the category seemed to yield better performance.
When we turn to the unsupervised learning, we were surprised that there were few effects overall.
However, when we considered the order of presentation, we found strong effects of the most recently
presented category. (Figure 3A). This should be interpreted with caution as data collection is notcomplete, and these analyses were exploratory. However, these took the form of a consistency effectwhereby unsupervised exposure to what would be center of the category (defined by the boundaries)
improved performance. The fact that this was only observed in the diagonal categories is inconsistent
with COVIS which posits that unsupervised learning is only possible with RB (here, Vert/Horz)boundaries. We return to this in the general discussion.

3. Experiment 2: Interleaved unsupervised training
Given these analyses, we initiated a second protocol in which all four categories were randomlyinterleaved during unsupervised training. We present preliminary results here, though the sample size
is limited so these results should be treated cautiously.
3.1 Methods
Subjects. 74 normal hearing young adults were subjects. These were randomly assigned to oneof the same 8 conditions as in Experiment 1 (~9.25 / condition). Our intended sample size is 30 /condition, so these results are preliminary.
Design, Stimuli and Procedures. All methods are identical to Experiment 1 with the exception thatthe order of stimuli in the unsupervised portion was completely random.
3.2 Results
Figure 4A shows performance during supervised training. Again, subjects learned rapidly andshowed a clear benefit for diagonal boundaries. Figure 4B shows performance on the periodic testingtrial. Contrary to Experiment 1, there was no effect of unsupervised training on diagonal boundaries,
but a large (6%) gain with vert/horz boundaries. Again this is a consistency benefit, but now
supervised learning was better if unsupervised learning was in the matching, square configuration.
This was examined with a mixed model using a similar structure to Experiment 1. We again founda significant effect of boundary (B=-0.51, SE=.15, Z=3.50, p=.00047). There was also a three-way
interaction between boundary, unsupervised and supervised configuration (B=1.71, SE=0.58, Z=2.95,
p=.0032). This supports the benefit of square unsupervised learning for vert/horz boundaries; however,
the three way interaction suggests it may have been moderated by the supervised configuration (it wasmuch stronger when the supervised configuration was square [matched the boundaries]). Given thesmall sample size, we are cautious about interpreting this moderation.
3.3 Discussion
Contrary to COVIS and to (11), Experiment 2 again showed stronger learning for diagonalboundaries than vertical/horizontal ones. More importantly, we also observe an influence of

Figure 4. A) Performance on all trials in the supervised learning task as a function of trial block andboundary. B) Performance on periodic testing trials as a function of unsupervised configuration (red vs.
blue bars), and boundary (diagonal vs. vert/horz). Error bars represent standard error of the mean.

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unsupervised training, but one that differed from Experiment 1. While this influence again took theform of a consistency effect (a benefit to learning when the unsupervised prototypes matched theprototypes defined by the boundaries), it only appeared in the square configuration.
So why would the unsupervised benefit be limited to vert/horz boundaries in this experiment, but
appear only with diagonal boundaries in Experiment 1 (although in a limited way)? Theoretical workemphasizes the utility of unsupervised processes for learning categories (e.g., the location and extent
of the categories in cue space). However, during unsupervised exposure, learners have no goal, andno task; thus it is possible that unsupervised learning is shaping other levels of the system. In particular,
unsupervised learning could serve to scaffold the pre-categorical representation, utilization andweighting of the auditory cues. Here, blocked exposure (Experiment 1) leads listeners to identify
features that are characteristic or defining of a categor y. This would be enhanced in the diamondconfiguration, where individual frequencies of one tone or the other can be used to identify individual
categories. For example, in Figure 1C, the green category is uniquely associated with the lowest valuesof Tone 1 (the X-axis), while the red category is associated with the lowest values of Tone 2 (the Yaxis). In contrast, unsupervised learning in the square configuration may promote attention topotentially contrastive features. This is supported by Carvahlo and Goldstone (17) who showed in asupervised learning paradigm that interleaving exemplars from different categories promotes attention
to contrastive features, while blocked training promotes attention to defining features.

4. Experiment 3: Cortical processes
Experiments 1 and 2 suggest unsupervised learning may alter the pre-categorical representational
space. However, this is difficult to assess with behavioral measures. In contrast, neuroscientifictechniques may be able to isolate them. Thus, we have been conducting experiments using electrocorticography. In this technique, electrodes are implanted directly on the surface of the brain o fpatients awaiting treatment for epilepsy. During this time, patients are awake and can complete
experiments. The temporal lobes is a common locus of seizures, thus many of these patients receive
electrodes on auditory and related areas offering an excellent opportunity to address these questions
by targeting the representation of the stimuli in auditory space.
4.1 Methods
Subjects. Subjects were two adult patients (pt. 405 and 409) undergoing treatment for epilepsy.
Both were monolingual English speakers with normal hearing, and neurologically normal other thanepilepsy. Both were implanted on the left hemisphere.
Procedures. Both patients first conducted a pre-test in which they heard an 8×8 grid spanning theentire Tone1×Tone2 space in a passive listening procedure. Here the goal was to simply collect neuraldata for the entire range of stimuli. On three subsequent days they then underwent three days of
unsupervised learning in the square configuration (768 stimuli/day). On day 5 they underwent an
identical post-test. On day 6, patient 409 underwent supervised training (in the square configuration)with post-test on day 7.
Neural Recordings. Recordings were made from subdural electrodes in a 4x8 grid (5 mm spacing)
centered on the temporal lobes, and from a depth electrode centered along Heschl’s gyrus. See (18)for complete details on recording, and patient care issues.
4.2 Results and Discussion
Our preliminary analyses focused on the degree of activity at targeted electrodes along the Superior
Temporal Gyrus (STG) and Heschl’s Gyrus (HG). To quantify localized activity, we used a timefrequency approach to quantify activity in the high gamma frequency band (75-150 Hz) (19). Figure
5 shows the results for characteristic electrodes. For all recording sites that were responsive to thestimuli (in both patients), we saw a reduction in activity after unsupervised learning. This suggeststhat one of the consequences of unsupervised learning may be adaptation. Such a mechanism could
play out quite differently in the diamond configuration, where some frequencies (e.g., the middle
frequencies of Tone 1 and 2 in Figure 1E), occur more often than others. In contrast, in the squareconfiguration, each frequency will be equally likely (and adapt equally).
In order to capture the representational space, we are conducting machine learning analyses (20)to recover the tone that was heard from the neural recordings. In this analysis, a support vector
regression was trained to predict the frequencies of the each tones of the stimulus from the distributed
pattern of activity on that trial. The predicted frequencies serve as a metric representation of the6592

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Figure 5. High gamma band activity (in dB) for selected recording sites for each patient.
stimulus space. Figure 4 shows predicted tone frequencies of each point in the grid. There were highcorrelations between the predicted and actual tones for both patients (Pt405: r=0.89; Pt409: r=0.88).
When we compared this space before and after unsupervised learning we found that this space
expanded with training (in Pt405; Figure 6), but no such changes were observed in Pt 409. Thissuggests that one consequence of learning may be a non-specific enhancement of the space, though
this awaits confirmation in other subjects.

5. Conclusions
The results of these studies are preliminary and await confirmation. However, they support several
conclusions. First, Experiments 1 and 2 suggest that listeners acquire categories with diagonalboundaries (II) better than those with square boundaries (RB). This conflicts with predictions from
COVIS (11), and it remains to be seen why the benefit for RB categories was not observed with thesestimuli. Second, learners benefit from training focused on the center of the space rather than theboundary regions. This suggests that for auditory categories, people primarily learn prototypes which
define the central tendencies of the category rather than boundaries. Third, the effects of unsupervised
learning (if present) are weak relative to the much stronger effects of supervised training –unsupervised exposure offered only a 5-10% gain over supervised training and only in some subconditions. Fourth, the small effects of unsupervised learning appeared to be moderated by whether
stimuli were blocked or interleaved: when unsupervised learning was blocked by category, a small
benefit was seen for diagonal boundaries, but when unsupervised learning interleaved all categories,
the benefit was seen for vert/horz boundaries. This is consistent with the idea that interleaved training
promotes the extraction of discriminative features that contrast categories, while blocked training
promotes identification of characteristic features that identify a category (17). Here we extend this tounsupervised learning. Finally, our preliminary ECOG results suggest that unsupervised exposure may
offer alter the system in several ways, including local adaptation of the frequency detectors, andpotentially an expansion of the representational space.

Figure 6. Reconstructed perceptual space for one patient before and after unsupervised learning. X andY axes represent the predicted tone value; the actual tone is indicated by the color (green = high Tone 2,
low tone 1, red = high tone 1, low tone 2).

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ACKNOWLEDGEMENTS
The authors would like to acknowledge Matthew Howard, Kirill Nourski and McCall Sarrett forassistance with the ECOG experiments, and Ted Abel for helpful discussions on auditory category
learning and Rodica Curtu for suggesting adaptation as a mecahnism. This project was supported by
P50DC000242 awarded to B. Gantz, R21DC017596 awarded to B. McMurray and b y the Iowa
Neuroscience Institute.

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