Content uploaded by Kimberly C. Young
Author content
All content in this area was uploaded by Kimberly C. Young on May 08, 2024
Content may be subject to copyright.
Paired-Associate Learning and Statistical Learning as Predictors of
Variance in Orthographic Learning
Kimberly Younga* and Ariel M. Cohen-Goldbergb
aDepartment of Psychology, Tufts University, Medford, United States; bDepartment of
Psychology, Tufts University, Medford, United States
Contact Kimberly Young kimberly.louis_jean@tufts.edu, Department of Psychology, Tufts
University, Medford, Massachusetts, United States.
2
Paired-Associate Learning and Statistical Learning as Predictors of
Variance in Orthographic Learning
Purpose: The purpose of this study was to examine if there is a relationship between
paired associate learning, statistical learning, and learning new orthographies.
Method: 73 adults were tested on a visual-visual paired associate learning (PAL) task,
visual statistical learning (SL) task, and two artificial orthography learning tasks
(explicit and implicit) over two days.
Results: PAL was significantly correlated with learning orthographies both explicitly
and implicitly taught.
Conclusion: These results suggest that PAL supports learning letter-sound
correspondences regardless of whether they are taught explicitly or implicitly. These
findings also suggest that SL may play a more limited role in reading acquisition and
that further research is needed to determine what aspects of reading acquisition it may
support if any.
Keywords: reading, language acquisition, statistical learning, paired-associate learning,
domain-general learning, learning mechanisms
3
Reading is a crucial aspect to everyday life and our ability to do so affects our future
successes. Therefore, it’s important to understand what predicts reading acquisition. One
approach has been to focus on general cognitive mechanisms that support learning. In two
separate literatures, paired associate learning (PAL) and statistical learning (SL) have been
proposed to support reading. Evidence suggests a link with both PAL (e.g. Hulme et al.,
2007; Litt et al., 2013; Wang et al., 2017) and SL (e.g. Arciuli & Simpson, 2012; Frost et al.,
2013; Pavlidou, 2019; Qi et al., 2019). Although, numerous studies have purported a
relationship between these cognitive mechanisms, none to our knowledge have compared
these mechanisms together and few have looked at specific aspects of reading. This study
therefor expands on the literature by examining the learning of letter-sound correspondences
and comparing to both PAL and SL.
Paired associate learning is the learning of arbitrary connections between two items.
Several studies have established a relationship between PAL and reading ability (Hulme et
al., 2007; Litt et al., 2013; Warmington & Hulme, 2012). For example, Hulme et al. (2007)
looked at three PAL tasks (visual-visual, auditory-auditory, and visual-auditory) and
compared performance to reading ability in English speaking children. Results showed
correlations between the PAL tasks and reading measures; with visual-auditory having the
strongest relationship.
In a similar line of investigation, Statistical learning (SL) has been suggested to
support reading. SL is usually characterized as an implicit cognitive learning mechanism that
extracts frequency and probabilistic co-occurrence information from the environment (Arciuli
& Simpson, 2012; Turk-Browne, 2012b). Because SL is often thought of as an implicit
ability, and because writing systems have patterns that are not likely explicitly taught (Arciuli
& Simpson, 2012; Treiman & Kessler, 2022; Qi et al., 2019), numerous studies have
investigated their relationship. Arcuili and Simpson (2012) investigated this relationship
4
using a visual statistical learning (VSL) task and general reading measures, in both adults and
children. They found that performance on the SL task correlated with reading in both children
and adults. Subsequent studies like Qi et al. (2019) expanded on this by utilizing other
modalities and found that auditory statistical leaning (ASL) was more predictive than VSL.
The current study aimed to compare PAL ability and SL ability and how they relate to
learning letter-sound correspondences. To achieve this, we used two artificial orthography
learning tasks with an explicit learning condition and an implicit learning condition. We
reasoned that since PAL is characterized by explicit learning, it would support explicitly
learning correspondences whereas SL may support more implicit learning of
correspondences.
Method
Participants
Participants were 80 native speakers (35 women) of American English between the
ages of 19 and 36 (M = 28.04) who were right-handed, with normal hearing and vision, and
with no known speech or language impairments. All participants were recruited online
through Prolific. Only participants that finished all tasks were included in the analyses. After
data collection, several participants did not have audio recordings available for analysis and
were excluded. Overall, 7 participants were excluded leaving 73 for analysis.
Materials
Orthographic Learning
The design was modelled after Taylor, Davis, and Rastle (2017). There were two
artificial orthography learning conditions; explicitly taught sound to symbol correspondences
and implicitly taught sound to symbol correspondences. Two different orthographies were
used (Hungarian Runes, Georgian Mkhedruli) with a left-to-right orientation. The assignment
of orthography to condition was counterbalanced across participants.
5
Phonemes consisted of 12 consonants (/b/, /d/, /f/, /g/, /k/, /m/, /n/, /p/, /s/, /t/, /v/, /z/)
and 8 vowels (/ɛ/, /ʌ/, /ɑɪ/, /o/, /æ/, /ɑ/, /i/, /u/). Stimuli consisted of six lists of 24
monosyllabic CVC nonwords for a total of 144 nonwords (see appendix A for full list of
stimuli). Within each list, consonants occurred twice in the onset position and twice in the
coda position. Vowels occurred six times within each list. The six lists were split in half,
creating two different sets. Both sets had the same 12 consonants but one set had the vowels
ɛ, ʌ, ɑɪ, o and the other set had the vowels æ, ɑ, i, u. 18 symbols were mapped to each set
(Taylor, Davis, and Rastle, 2017). The assignment of each set to orthography was
counterbalanced. Nonwords were recorded by a female native speaker of American English.
Figure 2 shows examples of items.
In the explicit condition there were three phases: familiarization, training, and test.
Participants were told that they would be learning pronunciations for letters in a new writing
system. The familiarization phase was self-paced where participants were first presented with
each symbol and simultaneously heard its sound (e.g. ზ = /b/). The training phase consisted
of 4 blocks where participants saw each symbol from the familiarization phase and were
asked to read aloud the appropriate sound. Participants were instructed to say them as quickly
and accurately as possible and then press a key to hear the correct pronunciation before
moving on to the next item. During testing, participants saw nonwords from two lists of one
of the sets and were asked to read them aloud with a maximum of 9 seconds to respond
before progressing to the next item. Responses were transcribed and coded for accuracy
afterwards with accuracy measured by number of letters correct
1
out of attempted trials.
Figure 2
1
The letter scoring was adopted from Goldrick, Folk, & Rapp (2010) and was designed to align target and
response to capture as much letter learning as possible
6
The implicit procedure was identical except instead of seeing each symbol and sound,
participants were familiarized and trained on one list of nonwords from a set. During testing,
participants were given the two remaining lists they were not trained on. Responses were
transcribed and coded for accuracy afterwards with accuracy measured by number of letters
correct out of attempted trials.
Paired Associate Learning task
A visual paired associate learning task was adapted from Schmalz et al. (2021).
Stimuli consisted of 20 shapes (see appendix A for list of stimuli). Two lists of 10 shapes
were created; one list contained the “cue” shapes and one list contained the “response”
shapes. Shape pairings between the lists were randomized across participants.
There were three phases: familiarization, training, and test. In the familiarization
phase, Participants saw each shape from the “cue” list and its paired shape from the
“response” list. Following this, training consisted of 4 blocks where each shape from the
“cue” list was presented one at a time in random order for a total of 40 trials. Below each
“cue” shape was a choice between two shapes (the correct shape and a foil shape that was
taken from the “response” list). Participants were asked to select the paired shape and press a
key to see the correct response before moving on to the next trial. During testing, participants
7
performed the same task as training except without feedback and with only one trial run for a
total of 10 responses. Performance was based on accuracy out of 10.
Nonlinguistic Visual Statistical Learning Task
The visual statistical learning (VSL) task was adapted from Siegelman and Frost
(2015). Stimuli consisted of 24 complex black shapes (see Appendix A for list of stimuli).
Shapes were randomly organized into eight triplets per participant. There were two phases:
familiarization and test. In the familiarization phase, the eight triplets appeared in random
order with each shape being presented one at a time in sequence with each triplet appearing
24 times within a 10 minute stream (see Figure 4). The only constraint was that no triplet
could appear twice in a row within that stream. Each shape was presented in the center of the
screen for 800ms with a 200ms blank screen between shapes. Participants were instructed to
attentively watch the stream.
In the test phase, participants were given 32 trials comprising both a target and a foil.
Targets were triplets from the familiarization phase with a transitional probability (TP) of 1
between shapes (always appear together). Foils were a sequence of three shapes that never
appeared together in the familiarization phase where the TP between shapes was 0. Foils
were randomly selected for each participant. For each trial, participants were presented with a
target and a foil and were asked to pick the familiar pattern in a two alternative forced choice
task. Both the targets and foils were presented sequentially. Each shape appeared for 800ms
with a 200ms break between shapes and a 1000ms break between the first sequence and the
second. The order of target-foil pairs in each test trial was randomized for each participant,
where in some trials the target appeared first and in others the foil appeared first.
Performance was based on accuracy out of 32.
8
Digit Span
General working memory was measured using the digit span subtests (forward and
backward) from Wechsler Adult Intelligence Scale-Revised (WAIS-R; Weschler, 1981).
Raven’s Matrix Reasoning
General intelligence was measured using a matrix reasoning subtest developed by
Germine et al. (2012) and adapted from the Wechsler Abbreviated Scale of Intelligence II
(WASI-II; Wechsler & Hsiaopin, 2011).
Lexical Decision
General reading ability was measured using the Rapid Online Assessment of Reading
Ability (ROAR) task (Yeatman et al., 2021) which measured word recognition ability using a
lexical decision task.
Procedure
Participants were tested remotely through Gorilla. The study took place over two
separate sessions within a five day period. During session 1, participants completed the
reading cognitive measures which averaged about 30 minutes. During session 2, participants
completed the artificial orthography learning tasks, the PAL task, and the SL task which
averaged about 1.5 hours. Tasks were counterbalanced across participants.
Data Analysis
Correlational and mixed-effects regression analyses were performed using JASP
(2020) software (Dienes, 2014; Schmalz et al., 2019; 2021; Wagenmakers et al., 2018),
Correlations included both Pearson’s coefficient r and Bayes Factors (BFs). For the
9
regressions, the dependent variable was letter accuracy in both the explicit and implicit
conditions. Predictors of interest were digit span, Raven’s matrices, ROAR, VSL, and PAL.
Results
Table 2 shows the overall descriptive statistics for all measures. For AOL tasks, letter
accuracy was used to measure learning at a more fine-grained level
2
. Figure 5 shows the
distributions in performance across tasks. Performance across tasks generally showed
variation in performance, however, Raven’s and ROAR showed performance almost at
ceiling making it difficult for clear correlations with other measures.
Correlational analyses are shown in Table 3. Both Pearson's correlation coefficients
(r) and Bayes factors (BFs) are reported in order to look at evidence in both directions. BFs
greater than 1 show anecdotal evidence in favor of the alternative hypothesis with BFs less
than 1 showing anecdotal evidence for the null hypothesis (Dienes, 2014; Jeffreys, 1961;
Rouder et al., 2009; Wetzels & Wagenmakers, 2012). Based on standards described by
Wetzels & Wagenmaker (2012), BFs greater than 1 but less than 3 are anecdotal evidence,
BFs greater than 3 but less than 10 are substantial evidence, BFs greater than 10 but less than
30 are strong evidence, BFs greater than 30 but less than 100 are very strong evidence, and
BFs greater than 100 are decisive evidence in favor of the alternative hypothesis.
Focusing on the correlations between PAL and VSL, there is anecdotal evidence of a
relationship (r = 0.27, BF = 1.88). VSL shows a substantial correlation with explicit learning
(r = 0.3, BF = 3.46) and anecdotal relationship with implicit learning (r = 0.28, BF = 2.26).
PAL shows a decisive correlation with explicit learning (r = 0.48, BF = 1238.38) and a very
strong correlation with implicit learning (r = 0.40, BF = 65.6). Additionally, the explicit
2
Performance on word accuracy for both the Explicit Learning and Implicit Learning tasks was calculated,
however this measure of accuracy was too course-grained to be an accurate representation of learning. Because
of this, performance on letter accuracy was used to determine if any participants were excluded.
10
learning condition decisively correlated with the implicit learning condition (r = 0.62, BF =
2195000).
Predictors of Paired Associate Learning and Statistical Learning
Regression analyses of PAL and VSL are shown in Tables 4 and 5. Only digit span
(backward) significantly predicted variance (b = 0.02, t = 2.11, p = .04) in PAL, while digit
span (forward), Raven’s, ROAR, and VSL contributed no additional variance. This shows
that participants with higher working memory capacity had greater ability to make
associations between visual objects. Only Raven’s (b =0.32, t = 2.9, p = .01) and ROAR
(accuracy; b = -0.51, t = -2.25, p = .03) predicted variance in VSL. These results suggest that
participants with better general intelligence and word recognition ability are better at visual
statistical learning. The lack of VSL being a predictor in the regression model of PAL and the
lack of PAL being a predictor in the regression model of VSL suggest that they are supported
by different underlying mechanisms. However, performance on the Raven’s and ROAR tasks
(See Figure 5) were almost at ceiling, making it difficult to determine how they contribute to
these types of learning.
Predictors of Explicit Learning and Implicit Learning
Table 6 shows predictors of explicit learning. PAL (b = 0.29, t = 2.58, p = .01) and
Raven’s (b = 0.37, t =2.22, p = .03) were significant predictors. Similarly, Table 7 shows that
PAL (b = 0.34, t = 2.11, p = .04) and Raven’s (b = 0.64, t = 2.7, p = .01) also predicted
variance in the implicit condition
3
.
3
When removing Raven’s and PAL from the model, VSL did become significant, however a model comparison
still indicated that the model with all predictors included had the best fit.
11
Discussion
Recent studies have found a relationship between both SL (Arcuili & Simpson, 2012;
Qi et al., 2019, Zhao et al., 2018) and PAL (Hulme et al., 2007; Litt et al., 2013; Litt &
Nation, 2014; Wang et al., 2017) and reading ability. The current study aimed to expand this
knowledge by investigating their relationship with letter-sound correspondences. Results
showed a relationship between PAL in both explicit and implicit learning giving support for
its role in reading acquisition. The absence of a relationship between VSL and either of the
learning conditions suggests that SL may not play as significant a role in reading as has been
suggested in previous studies. Additionally, no significant relationship between PAL and
VSL was found when accounting for other cognitive measures, suggesting that these are
distinct learning mechanisms.
The lack of correlation between SL and orthographic learning support findings of
Schmalz et al. (2019) who similarly did not find a relationship between SL and reading
ability in adults. However, these findings do not line up with numerous studies that did find a
correlation between SL and reading (e.g. Arciuli & Simpson, 2012; Qi et al., 2019, Zhao et
al., 2018). One possibility is that type of learning measured in the artificial orthography tasks
isn’t supported by SL in adults, but may be different if this study were repeated with children.
It may be that these types of learning are more prominent in the early stages of reading
acquisition in children and may lessen over time in adult learners whereby other tactics in
learning are employed. Another possibility is that the mappings between sound and symbol
had 1:1 mappings. SL may be more supportive of learning less regular mappings (Schmalz et
al., 2019; Steacy et al., 2017; 2018). This may indicate that SL, as it’s measured here, is
limited in how it supports reading acquisition.
This study found some support for a correlation between PAL and orthographic
learning similar to Wang et al. (2017) and Litt and colleagues (Litt et al., 2013; Litt & Nation,
12
2014). However, in those studies they did not find a relationship with visual-visual PAL,
which is unlike the current findings. They found that only tasks that required a verbal output
(visual-auditory and auditory-auditory) were linked with reading ability. Although Schmalz
et al. (2021) did not find any correlation between visual-auditory PAL and AOL which shows
that there are inconsistent results across studies looking at these types of measures. However,
the results from this study may suggest that PAL tasks without a verbal output can predict
orthographic learning which could indicate that there is a relationship with more general
associative learning that is non-linguistic.
As little research has been done on the correlation between SL and PAL, the current
findings suggest that these may be separate learning mechanisms. Both mechanisms have
been described by different memory systems, with PAL being an episodic memory task
utilizing the declarative memory system (Scorpio et al., 2018) and SL utilizing procedural
memory due to its implicit nature (Poldrack & Rodriguez, 2004; Squire, 1992; Qi et al.,
2019). However, future research is needed to investigate how these mechanisms uniquely
support reading acquisition and whether and how they overlap.
Some limitations to the study should be mentioned. First, this study was entirely
online without a researcher to proctor. It is possible that aspects of the study were unclear or
there were technical difficulties which affected performance. Second, this was a two-day
within-group design which makes it likely that participants were not attending to all tasks
equally throughout.
Conclusion
In summary, our findings suggest a prominent role of PAL in learning orthographies,
specifically in learning letter-sound correspondences. Additionally, we show that SL may
play a limited role in reading which requires further investigation to determine what aspects it
13
may contribute to more specifically. Finally, this study shows how AOL tasks can provide a
controlled environment for investigating specific aspects of writing systems.
14
References
Apfelbaum, K. S., Hazeltine, E., & McMurray, B. (2012). Statistical learning in reading:
Variability in irrelevant letters helps children learn phonics skills. Developmental
Psychology, 49(7), 1348.
Arciuli, J. (2018). Reading as statistical learning. Language, Speech, and Hearing Services in
Schools, 49(3S), 634-643.
Arciuli, J., & Simpson, I. C. (2011). Statistical learning in typically developing children: The
role of age and speed of stimulus presentation. Developmental science, 14(3), 464-
473.
Arciuli, J., & Simpson, I. C. (2012). Statistical learning is related to reading ability in
children and adults. Cognitive science, 36(2), 286-304.
Arciuli, J.,Monaghan, P., & Seva, N. (2010). Learning to assign lexical stress during reading
aloud: Corpus, behavioral, and computational investigations. Journal of Memory and
Language, 63(2), 180–196. https://doi.org/10.1016/J.Jml.2010.03.005
Arnon, I. (2019). Statistical learning, implicit learning, and first language acquisition: A
critical evaluation of two developmental predictions. Topics in cognitive science,
11(3), 504-519.
Batterink, L. J., Reber, P. J., Neville, H. J., & Paller, K. A. (2015). Implicit and explicit
contributions to statistical learning. Journal of memory and language, 83, 62-78.
Bertels, J., Franco, A., & Destrebecqz, A. (2012). How implicit is visual statistical learning?.
Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(5), 1425.
Bogaerts, L., Frost, R., & Christiansen, M. H. (2020). Integrating statistical learning into
cognitive science. Journal of Memory and Language, 115, 104167.
Bogaerts, L., Frost, R., & Christiansen, M. H. (2020). Integrating statistical learning into
cognitive science. Journal of Memory and Language, 115, 104167.
15
Cassar, M., & Treiman, R. (1997). The beginnings of orthographic knowledge: Children’s
knowledge of double letters in words. Journal of Educational Psychology, 89(4), 631.
https://doi.org/10.1037/0022-0663.89.4.631
Castles, A., Rastle, K., & Nation, K. (2018). Ending the reading wars: Reading acquisition
from novice to expert. Psychological Science in the Public Interest, 19(1), 5-51.
Chater, N., & Manning, C. D. (2006). Probabilistic models of language processing and
acquisition. Trends in cognitive sciences, 10(7), 335-344.
Christiansen, M. H. (2019). Implicit statistical learning: A tale of two literatures. Topics in
Cognitive Science, 11(3), 468-481.
Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route
cascaded model of visual word recognition and reading aloud. Psychological Review,
108, 204–256. doi:10.1037//0033-295x.108.1.204
Conway, C. M., & Christiansen, M. H. (2005). Modality-constrained statistical learning of
tactile, visual, and auditory sequences. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 31(1), 24.
Conway, C. M., Bauernschmidt, A., Huang, S. S., & Pisoni, D. B. (2010). Implicit statistical
learning in language processing: Word predictability is the key. Cognition, 114(3),
356-371.
Dancey, C. P., & Reidy, J. (2007). Statistics without maths for psychology. Pearson
education.
Deacon, S. H., Conrad, N., & Pacton, S. (2008). A statistical learning perspective on
children’s learning about graphotactic and morphological regularities in spelling.
Canadian Psychology, 49(2), 118–124.
Dienes, Z. (2014). Using Bayes to get the most out of non-significant results. Frontiers in
psychology, 5, 781.
16
Elleman, A. M., Steacy, L. M., & Compton, D. L. (2019). The role of statistical learning in
word reading and spelling development: More questions than answers. Scientific
Studies in Reading, 23(1), 1-7. https://doi.org/10.1080/10888438.2018.1549045
Ellis, E. M., Gonzalez, M. R., & Deák, G. O. (2014). Visual prediction in infancy: What is
the association with later vocabulary?. Language Learning and Development, 10(1),
36-50.
Emberson, L. L., Conway, C. M., & Christiansen, M. H. (2011). Timing is everything:
Changes in presentation rate have opposite effects on auditory and visual implicit
statistical learning. Quarterly Journal of Experimental Psychology, 64(5), 1021-1040.
Endress, A. D., & Mehler, J. (2009). The surprising power of statistical learning: When
fragment knowledge leads to false memories of unheard words. Journal of Memory
and Language, 60(3), 351-367.
Erickson, L. C., Kaschak, M. P., Thiessen, E. D., & Berry, C. A. (2016). Individual
differences in statistical learning: Conceptual and measurement issues. Collabra, 2(1).
Esper, E. A. (1925). A technique for the experimental investigation of associative
interference in artificial linguistic material. Language Monographs No. 1.
Evans, J. L., Saffran, J. R., & Robe-Torres, K. (2009). Statistical learning in children with
specific language impairment. Journal of Speech, Language, and Hearing Research,
52, 321–335. https://doi.org/10.1044/1092-4388(2009/07-0189)
Farmer, T. A., Christiansen, M. H., & Monaghan, P. (2006). Phonological typicality
influences on-line sentence comprehension. Proceedings of the National Academy of
Sciences, 103(32), 12203-12208.
Fiser, J., & Aslin, R. N. (2001). Unsupervised statistical learning of higher-order spatial
structures from visual scenes. Psychological science, 12(6), 499-504.
17
Frost, R., Armstrong, B. C., & Christiansen, M. H. (2019). Statistical learning research: A
critical review and possible new directions. Psychological Bulletin, 145(12), 1128.
Frost, R., Armstrong, B. C., Siegelman, N., & Christiansen, M. H. (2015). Domain generality
versus modality specificity: the paradox of statistical learning. Trends in cognitive
sciences, 19(3), 117-125.
Frost, R., Siegelman, N., Narkiss, A., & Afek, L. (2013). What predicts successful literacy
acquisition in a second language?. Psychological science, 24(7), 1243-1252.
Gebhart, A. L., Newport, E. L., & Aslin, R. N. (2009). Statistical learning of adjacent and
nonadjacent dependencies among nonlinguistic sounds. Psychonomic bulletin &
review, 16(3), 486-490.
Georgiou, G., Liu, C., & Xu, S. (2017). Examining the direct and indirect effects of visual–
verbal paired associate learning on Chinese word reading. Journal of experimental
child psychology, 160, 81-91.
Germine, L., Nakayama, K., Duchaine, B., Chabris, C., Chatterjee, G., & Wilmer, J. (2012).
Is the web as good as the lab? Comparable performance from web and lab in
cognitive/perceptual experiments. Psychonomic Bulletin & Review, 19(5): 847-857.
Gingras, M., & Sénéchal, M. (2019). Evidence of statistical learning of orthographic
representations in grades 1–5: The case of silent letters and double consonants in
French. Scientific Studies of Reading, 23(1), 37-48.
Hirshorn, E. A., & Fiez, J. A. (2014). Using artificial orthographies for studying cross-
linguistic differences in the cognitive and neural profiles of reading. Journal of
Neurolinguistics, 31, 69-85.
Hulme, C., Goetz, K., Gooch, D., Adams, J., & Snowling, M. J. (2007). Paired-associate
learning, phoneme awareness, and learning to read. Journal of experimental child
psychology, 96(2), 150-166.
18
Hulme, C., Goetz, K., Gooch, D., Adams, J., & Snowling, M. J. (2007). Paired-associate
learning, phoneme awareness, and learning to read. Journal of experimental child
psychology, 96(2), 150-166.
Hung, Y. H., Frost, S. J., Molfese, P., Malins, J. G., Landi, N., Mencl, W. E., ... & Pugh, K.
R. (2019). Common neural basis of motor sequence learning and word recognition
and its relation with individual differences in reading skill. Scientific Studies of
Reading, 23(1), 89-100.
Huron, D. (2006). Sweet anticipation: Music and the psychology of expectation. Cambridge,
MA: MIT Press.
Isbilen, E. S., McCauley, S. M., Kidd, E., & Christiansen, M. H. (2017). Testing statistical
learning implicitly: A novel chunk-based measure of statistical learning. In the 39th
Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 564-569).
Cognitive Science Society.
Jared, D. (2002). Spelling–sound consistency and regularity effects in word naming. Journal
of Memory and Language, 46(4), 723–750. https://doi.org/10.1006/jmla.2001.2827.
JASP Team (2020). JASP (Version 0.14.1)[Computer software].
Jones, M. W., Kuipers, J. R., Nugent, S., Miley, A., & Oppenheim, G. (2018). Episodic traces
and statistical regularities: Paired associate learning in typical and dyslexic readers.
Cognition, 177, 214-225.
Kahta, S., & Schiff, R. (2019). Deficits in statistical leaning of auditory sequences among
adults with dyslexia. Dyslexia, 25(2), 142-157.
Karuza, E. A., Farmer, T. A., Fine, A. B., Smith, F. X., & Jaeger, T. F. (2014). On-line
measures of prediction in a self-paced statistical learning task. In Proceedings of the
annual meeting of the Cognitive Science Society (Vol. 36, No. 36).
19
Kidd, E., & Arciuli, J. (2016). Individual differences in statistical learning predict children’s
comprehension of syntax. Child Development, 87(1), 184.
https://doi.org/10.1111/cdev.12461
Kidd, E., Arciuli, J., Christiansen, M. H., Isbilen, E. S., Revius, K., & Smithson, M. (2020).
Measuring children’s auditory statistical learning via serial recall. Journal of
experimental child psychology, 200, 104964.
Kim, R., Seitz, A., Feenstra, H., & Shams, L. (2009). Testing assumptions of statistical
learning: is it long-term and implicit?. Neuroscience letters, 461(2), 145-149.
Knowlton, B. J., & Squire, L. R. (1994). The information acquired during artificial grammar
learning. Journal of Experimental Psychology: Learning, Memory, and Cognition,
20(1), 79.
Laine, M., Polonyi, T., & Abari, K. (2014). More than words: Fast acquisition and
generalization of orthographic regularities during novel word learning in adults.
Journal of psycholinguistic research, 43(4), 381-396.
Lelonkiewicz, J. R., Ktori, M., & Crepaldi, D. (2020). Morphemes as letter chunks:
Discovering affixes through visual regularities. Journal of Memory and language,
115, 104152.
Litt, R. A., & Nation, K. (2014). The nature and specificity of paired associate learning
deficits in children with dyslexia. Journal of Memory and Language, 71(1), 71-88.
Litt, R. A., de Jong, P. F., van Bergen, E., & Nation, K. (2013). Dissociating crossmodal and
verbal demands in paired associate learning (PAL): What drives the PAL–reading
relationship?. Journal of experimental child psychology, 115(1), 137-149.
Litt, R. A., Wang, H. C., Sailah, J., Badcock, N. A., & Castles, A. (2019). Paired associate
learning deficits in poor readers: The contribution of phonological input and output
processes. Quarterly Journal of Experimental Psychology, 72(3), 616-633.
20
Liu, C., Chung, K. K. H., Wang, L. C., & Liu, D. (2020). The relationship between paired
associate learning and Chinese word reading in kindergarten children. Journal of
Research in Reading, 44(2), 264-283.
Maye, J., Werker, J. F., & Gerken, L. (2002). Infant sensitivity to distributional information
can affect phonetic discrimination. Cognition, 82(3), B101-B111.
McCauley, S. M., & Christiansen, M. H. (2015, July). Individual Differences in Chunking
Ability Predict On-line Sentence Processing. In CogSci.
Meade, G. (2020). The role of phonology during visual word learning in adults: An
integrative review. Psychonomic bulletin & review, 27(1), 15-23.
Merkx, M., Rastle, K., & Davis, M. H. (2011). The acquisition of morphological knowledge
investigated through artificial language learning. Quarterly Journal of Experimental
Psychology, 64(6), 1200-1220.
Messbauer, V. C., & de Jong, P. F. (2003). Word, nonword, and visual paired associate
learning in Dutch dyslexic children. Journal of experimental child psychology, 84(2),
77-96.
Misyak, J. B., & Christiansen, M. H. (2012). Statistical learning and language: An individual
differences study. Language Learning, 62(1), 302-331.
Misyak, J. B., Christiansen, M. H., & Bruce Tomblin, J. (2010b). Sequential expectations:
The role of prediction‐based learning in language. Topics in Cognitive Science, 2(1),
138-153.
Misyak, J. B., Christiansen, M. H., & Tomblin, J. B. (2010a). On-line individual differences
in statistical learning predict language processing. Frontiers in psychology, 1, 31.
Nation, K. (2009). Form–meaning links in the development of visual word recognition.
Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1536),
3665-3674.
21
Oliva, A., & Torralba, A. (2007). The role of context in object recognition. Trends in
cognitive sciences, 11(12), 520-527.
Paap, K. R., & Noel, R. W. (1991). Dual-route models of print to sound: Still a good horse
race. Psychological research, 53(1), 13-24.
Pacton, S., Perruchet, P., Fayol, M., & Cleeremans, A. (2001). Implicit learning out of the
lab: The case of orthographic regularities. Journal of experimental psychology:
General, 130(3), 401.
Pavlidou, E. V., & Bogaerts, L. (2019). Implicit statistical learning across modalities and its
relationship with reading in childhood. Frontiers in psychology, 10, 1834.
Pelucchi, B., Hay, J. F., & Saffran, J. R. (2009). Statistical learning in a natural language by
8‐month‐old infants. Child development, 80(3), 674-685.
Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: One
phenomenon, two approaches. Trends in cognitive sciences, 10(5), 233-238.
Poldrack, R. A., & Rodriguez, P. (2004). How do memory systems interact? Evidence from
human classification learning. Neurobiology of learning and memory, 82(3), 324-332.
Qi, Z., Sanchez Araujo, Y., Georgan, W. C., Gabrieli, J. D., & Arciuli, J. (2019). Hearing
matters more than seeing: A cross-modality study of statistical learning and reading
ability. Scientific Studies of Reading, 23(1), 101-115.
Rahmanian, S., & Kuperman, V. (2019). Spelling errors impede recognition of correctly
spelled word forms. Scientific Studies of Reading, 23(1), 24-36.
Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and
Verbal Behavior, 6(6), 855–863. https://doi.org/10.1016/S0022-5371(67)80149-X
Romberg, A. R., & Saffran, J. R. (2010). Statistical learning and language acquisition. Wiley
Interdisciplinary Reviews: Cognitive Science, 1(6), 906-914.
22
Rothe, J., Schulte-Körne, G., & Ise, E. (2014). Does sensitivity to orthographic regularities
influence reading and spelling acquisition? A 1-year prospective study. Reading and
Writing, 27(7), 1141–1161. https://doi.org/10.1007/s11145-013-9479-7
Saffran, J. R. (2001). The use of predictive dependencies in language learning. Journal of
Memory and Language, 44(4), 493-515.
Saffran, J. R., & Wilson, D. P. (2003). From syllables to syntax: multilevel statistical
learning by 12‐month‐old infants. Infancy, 4(2), 273-284.
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old
infants. Science, 274(5294), 1926-1928.
Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996). Word segmentation: The role of
distributional cues. Journal of memory and language, 35(4), 606-621.
Sawi, O. M., & Rueckl, J. (2019). Reading and the neurocognitive bases of statistical
learning. Scientific Studies of Reading, 23(1), 8-23.
Schapiro, A., & Turk-Browne, N. (2015). Statistical learning. Brain mapping, 3, 501-506.
Schmalz, X., Marinus, E., Robidoux, S., Palethorpe, S., Castles, A., & Coltheart, M. (2014).
Quantifying the reliance on different sublexical correspondences in German and
English. Journal of Cognitive Psychology, 26(8), 831–852.
https://doi.org/10.1080/20445911.2014.968161
Schmalz, X., Moll, K., Mulatti, C., & Schulte-Körne, G. (2019). Is statistical learning ability
related to reading ability, and if so, why?. Scientific Studies of Reading, 23(1), 64-76.
Schmalz, X., Schulte-Körne, G., de Simone, E., & Moll, K. (2021). What Do Artificial
Orthography Learning Tasks Actually Measure? Correlations Within and Across
Tasks. Journal of cognition, 4(1).
23
Schmalz, X., Schulte-Körne, G., de Simone, E., & Moll, K. (2021). What Do Artificial
Orthography Learning Tasks Actually Measure? Correlations Within and Across
Tasks. Journal of Cognition, 4(1), 1–14. DOI: https://doi.org/10.5334/joc.144
Schneider, J. M., Hu, A., Legault, J., & Qi, Z. (2020). Measuring statistical learning across
modalities and domains in school-aged children via an online platform and
neuroimaging techniques. JoVE (Journal of Visualized Experiments), (160), e61474.
Schubert, T. M., Cohen, T., & Fischer-Baum, S. (2020). Reading the written language
environment: Learning orthographic structure from statistical regularities. Journal of
Memory and Language, 114, 104148.
Schubert, T., & McCloskey, M. (2013). Prelexical representations and processes in reading:
Evidence from acquired dyslexia. Cognitive Neuropsychology, 30(6), 360-395. DOI:
10.1080/02643294.2014.880677
Scorpio, K. A., Islam, R., Kim, S. M., Bind, R., Borod, J. C., Bender, H. A., ... & Caplan, B.
(2018). Paired-associate learning. Encyclopedia of Clinical Neuropsychology.
Springer.
Seidenberg, M. S., & MacDonald, M. C. (2018). The impact of language experience on
language and reading. Topics in Language Disorders, 38(1), 66-83.
Shafto, C. L., Conway, C. M., Field, S. L., & Houston, D. M. (2012). Visual sequence
learning in infancy: Domain‐general and domain‐specific associations with language.
Infancy, 17(3), 247-271.
Siegelman, N. (2020). Statistical learning abilities and their relation to language. Language
and Linguistics Compass, 14(3), e12365.
Siegelman, N., Bogaerts, L., & Frost, R. (2017). Measuring individual differences in
statistical learning: Current pitfalls and possible solutions. Behavior research
methods, 49(2), 418-432.
24
Siegelman, N., Bogaerts, L., Christiansen, M. H., & Frost, R. (2017). Towards a theory of
individual differences in statistical learning. Philosophical Transactions of the Royal
Society B: Biological Sciences, 372(1711), 20160059.
Siegelman, N., Bogaerts, L., Kronenfeld, O., & Frost, R. (2018). Redefining “learning” in
statistical learning: What does an online measure reveal about the assimilation of
visual regularities?. Cognitive science, 42, 692-727.
Snell, J., & Theeuwes, J. (2020). A story about statistical learning in a story: Regularities
impact eye movements during book reading. Journal of Memory and Language, 113,
104127.
Spencer, M., Kaschak, M. P., Jones, J. L., & Lonigan, C. J. (2015). Statistical learning is
related to early literacy-related skills. Reading and writing, 28(4), 467-490.
Squire, L. R. (1992). Declarative and nondeclarative memory: Multiple brain systems
supporting learning and memory. Journal of cognitive neuroscience, 4(3), 232-243.
Steacy, L. M., Compton, D. L., Petscher, Y., Elliott, J. D., Smith, K., Rueckl, J., … Pugh, K.
(2019). Development and prediction of context-dependent vowel pronunciation in
elementary readers. Scientific Studies of Reading, 23(1), 49–63.
https://doi.org/10.1080/10888438.2018.1466303
Steacy, L. M., Elleman, A. M., & Compton, D. L. (2017). Opening the “black box” of
learning to read: Inductive learning mechanisms supporting word-learning
development with a focus on interventions for children who struggle to read. In K.
Cain, D. L. Compton, & R. K. Parrila (Eds.), Theories of reading development (pp.
99–121). Amsterdam, The Netherlands: John Benjamins Publishing.
Tamminen, J., Newbury, C. R., Crowley, R., Vinals, L., Cevoli, B., & Rastle, K. (2020).
Generalisation in language learning can withstand total sleep deprivation.
Neurobiology of Learning and Memory, 173, 107274.
25
Taylor, J. S. H., Davis, M. H., & Rastle, K. (2017). Comparing and validating methods of
reading instruction using behavioural and neural findings in an artificial orthography.
Journal of Experimental Psychology: General, 146(6), 826.
Taylor, J. S. H., Plunkett, K., & Nation, K. (2011). The influence of consistency, frequency,
and semantics on learning to read: an artificial orthography paradigm. Journal of
Experimental Psychsology: Learning, Memory, and Cognition, 37(1), 60.
Torgeson, J. K., Wagner, R. K., & Rashotte, C. A. (1999). Test Review: Test of Word
Reading Efficiency (TOWRE). Pro-ed.
Treiman, R., Kessler, B., & Bick, S. (2003). Influence of consonantal context on the
pronunciation of vowels: A comparison of human readers and computational models.
Cognition, 88(1), 49–78. https://doi.org/10.1016/s0010-0277(03)00003-9
Treiman, R., Kessler, B., Zevin, J. D., Bick, S., & Davis, M. (2006). Influence of consonantal
context on the reading of vowels: Evidence from children. Journal of Experimental
Child Psychology, 93(1), 1–24. https://doi.org/10.1016/J.Jecp.2005.06.008
Turk-Browne, N. B. (2012a). Statistical learning and its consequences. In The influence of
attention, learning, and motivation on visual search (pp. 117-146). Springer, New
York, NY.
Turk-Browne, N. B., Jungé, J. A., & Scholl, B. J. (2005). The automaticity of visual
statistical learning. Journal of Experimental Psychology: General, 134(4), 552.
Turk-Browne, N.B. (2012b) Statistical Learning in Perception. In: Seel N.M. (eds)
Encyclopedia of the Sciences of Learning. Springer, Boston, MA.
https://doi.org/10.1007/978-1-4419-1428-6_1707
Ulicheva, A., Harvey, H., Aronoff, M., & Rastle, K. (2020). Skilled readers’ sensitivity to
meaningful regularities in English writing. Cognition, 195, 103810.
26
Ulicheva, A., Marelli, M., & Rastle, K. (2020). Sensitivity to meaningful regularities
acquired through experience. Morphology, 1-22.
van der Kleij, S. W., Groen, M. A., Segers, E., & Verhoeven, L. (2018). Sequential implicit
learning ability predicts growth in reading skills in typical readers and children with
dyslexia. Scientific Studies of Reading, 23(1), 77–88.
https://doi.org/10.1080/10888438.2018.1491582
van Witteloostuijn, M., Boersma, P., Wijnen, F., & Rispens, J. (2019). Statistical learning
abilities of children with dyslexia across three experimental paradigms. Plos one,
14(8), e0220041.
van Witteloostuijn, M., Boersma, P., Wijnen, F., & Rispens, J. (2021). The contribution of
individual differences in statistical learning to reading and spelling performance in
children with and without dyslexia. Dyslexia, 27(2), 168-186.
Vellutino, F. R., Steger, J. A., Harding, C. J., & Phillips, F. (1975). Verbal vs non-verbal
paired-associates learning in poor and normal readers. Neuropsychologia, 13(1), 75-
82.
von Koss Torkildsen, J., Arciuli, J., & Wie, O. B. (2019). Individual differences in statistical
learning predict children's reading ability in a semi-transparent orthography. Learning
and individual differences, 69, 60-68.
Wagenmakers, E. J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., ... & Morey, R.
D. (2018). Bayesian inference for psychology. Part II: Example applications with
JASP. Psychonomic bulletin & review, 25(1), 58-76.
Wang, H. C., Wass, M., & Castles, A. (2017). Paired-associate learning ability accounts for
unique variance in orthographic learning. Scientific Studies of Reading, 21(1), 5-16.
27
Warmington, M., & Hulme, C. (2012). Phoneme awareness, visual-verbal paired-associate
learning, and rapid automatized naming as predictors of individual differences in
reading ability. Scientific Studies of Reading, 16(1), 45-62.
Wechsler, D., & Hsiao-pin, C. (2011). WASI-II: Wechsler abbreviated scale of intelligence:
San Antonio. TX: Psychological Corporation.
Wechsler, D. (1981). WAIS-R: Manual: Wechsler adult intelligence scale-revised. Harcourt
Brace Jovanovich Psychological Corp., New York.
Wetzels, R., & Wagenmakers, E. J. (2012). A default Bayesian hypothesis test for
correlations and partial correlations. Psychonomic bulletin & review, 19(6), 1057-
1064.
Wilkinson, G. S., & Robertson, G. J. (2006). Wide range achievement test – Fourth edition:
Professional manual. Lutz, FL: Psychological Assessment Resources.
Windfuhr, K. L., & Snowling, M. J. (2001). The relationship between paired associate
learning and phonological skills in normally developing readers. Journal of
experimental child psychology, 80(2), 160-173.
Wolak, M. E., Fairbairn, D. J., & Paulsen, Y. R. (2012). Guidelines for estimating
repeatability. Methods in Ecology and Evolution, 3(1), 129–137.
Wolf, M., & Stoodley, C. J. (2008). Proust and the squid: The story and science of the
reading brain (pp. 147-148). New York: Harper Perennial.
Woodcock, R. (1998). Woodcock reading mastery test - Revised/Normative Update. Circle
Pines, MN: American Guidance Service.
Woodcock, R., McGrew, K., & Mather, N. (2001). Woodcock-Johnson tests of achievement.
Retrieved from https://pdfs.
semanticscholar.org/b4b0/7e51f6ecb9fa52b9a43310fb1675d9a099ee.pdf
28
Yeatman, J. D., Tang, K. A., Donnelly, P. M., Yablonski, M., Ramamurthy, M., Karipidis, I.
I., ... & Domingue, B. W. (2021). Rapid online assessment of reading ability.
Scientific reports, 11(1), 6396.
Zhao, J., Li, T., Elliott, M. A., & Rueckl, J. G. (2018). Statistical and cooperative learning in
reading: An artificial orthography learning study. Scientific Studies of Reading, 22(3),
191-208.
29
Appendix A
Tables and Figures
Table 2. Descriptive Statistics for individual difference measures
Mean
SD
Minimum
Maximum
Digit Span
Forward
8.56
2.63
1
14
Backward
8.39
2.98
2
14
Ravens
Accuracy
0.78
0.16
0.2
1
ROAR
Accuracy
0.91
0.07
0.6
1
RT
571.74
164.93
373.71
1643.54
Statistical Learning
0.55
0.15
0.28
1
Paired Associate Learning
0.76
0.23
0.2
1
Explicit Learning
Letter Accuracy
0.43
0.24
0.06
0.97
Implicit Learning
Letter Accuracy
0.41
0.31
0.05
0.97
30
Table 3
Spearman Correlation Matrix of individual measures
Note. Coefficient r is the Pearson's r correlation coefficient. Bayes factor values that are greater than 3 are highlighted in
bold and show evidence for the presence of a correlation. Bayes factor values that are less than 1/3 are marked in italic and
provide evidence against the presence of a correlation. Coefficient r values that have p-values 0.05 or less are bolded with
the corresponding asterisk indicating relative p-values. VSL = visual statistical learning; PAL = paired associate learning.
Table 4
Linear regression analysis predicting PAL
31
Table 5
Linear regression analysis predicting VSL
Table 6
Linear regression analysis predicting explicit learning
Table 7
Linear regression analysis predicting implicit learning
32
Figure 3
Paired associate learning task
Note. (A) Shows the familiarization phase; (B) shows the training phase.
Figure 4
Statistical learning task
Note. Example of a stream of
visual shapes from Siegelman et
al., (2017)
33
Figure 5
Distribution of scores for all tasks measured.
Figure 6
Relationships between PAL, VSL, Explicit learning, and Implicit Learning
34
Note. The top row shows the relationship between PAL and the explicit and implicit learning conditions. The second row
shows the relationship between PAL and the explicit and implicit learning conditions. The final row shows the relationship
between PAL and VSL.
