The ability of Statistical
Learning
an individual differences study
Noam Siegelman- Hebrew U.
Ram Frost
The Hebrew University
Haskins Laboratories & BCBL
Starting point – SL and L2
learning (Frost et al., 2013)
Individual Differences in Visual Statistical
Learning task predict L2 literacy learning
The theoretical stand regarding L2
learning
 In any language, all linguistic domains (e.g.,
syntax, gender marking, writing system,
morphological structure) display some level of
quasi-regularity.
 Language learning is primarily a process of picking
up and assimilating the statistical regularities of a
linguistic environment.
 The fundamental cognitive faculty of implicit
correlation-learning which underlies any form of
learning plays a primary role in second language
acquisition.
Second language literacy acquisition
theoretical basic assumptions:
1. The organization of any reading system reflects the
overall structure of the language (Frost, BBS, 2012).
2. Reading in L2 requires not only the acquisition of
new graphemes and GTP correspondences, but
mostly the implicit assimilation of deep linguistic
structure (e.g., morphology).
3. L2 literacy eventually reflects the statistical
properties of the language; the correlations of
orthographic, phonologic, and semantic units. These
are implicitly (or explicitly) picked up by the
readers.
In a nutshell:
This theoretical approach assumes that:
 There is a general cognitive capacity for
statistical learning.
 Like any human capacity it would have a
normal distribution of individual differences.
 This capacity predicts, at least to some
extent, individual differences in the ease or
difficulty in acquiring the new set of
regularities that determine reading in L2.
Past and current research on S.L
As Romberg and Saffran (2010) highlight:
 Most research in S.L has demonstrated the ability
of infants/adults (and even rodents!) to extract
elements out of linguistic or non-linguistic input
using the underlying statistics,
 Focusing only at the group level,
 Using only one S.L/I.L task.
This is not enough for our interest in the
domain of individual differences !
The capacity for statistical learningfour theoretical questions:
 Is S.L a unified capacity or a unified mechanism
responsible for all possible detection of
correlations, or is it a componential capacity?
 If it is not (and probably it is not) a unified
capacity, how are the different components of S.L
interrelated?
 How does S.L relates to other general cognitive
capacities such as intelligence, memory, executive
functions, etc?
 Is S.L a “stable” capacity of the individual that
remains more or less constant across time, such
as intelligence, etc?
Statistical Learning: A preliminary mapping
sentence
(Facet theory, Gutman 1959)
Statistical learning is the ability to implicitly
pick up regularities of {verbal/non-verbal}
information in the {visual/auditory}modality,
when contingencies are
{adjacent/non adj.},
thereby shaping behavior.
Preliminary mapping
Type of
Information
Modality:
 Visual

Verbal
 Non-verbal  Auditory
Type of
contingencies
 Adjacent
 Non-adjacent
The present research project
48 students of the Hebrew university were
tested in a series of experimental tasks that
monitored general cognitive abilities and
verbal abilities. Participants were also tested
with various form of statistical learning tasks
that cover some of the SL theoretical space,
and these were repeated twice at T1 and T2.
Aims:
1. To examine in a within-subject design whether
performance in a given S.L task predicts
performance in another S.L task. This will tell us
something about the unity/componentiality of S.L
as a cognitive ability.
2. To examine how S.L is related to other general
cognitive or verbal abilities. This will tell us whether
S.L is a subset (nested) of a more general ability
such as intelligence or memory.
3. To examine whether S.L is a stable (therefore
reliable) capacity of individuals. Reliability is a
necessary condition to validity of predictions!
Investigated tasks
Statistical learning tasks
VSL (visual modality, non 
verbal, adjacent
contingencies).
ASL (auditory modality,

verbal, adjacent
contingencies).
ANA (auditory, non-verbal, 
adjacent)
AVN (auditory, verbal, non- 
adjacent)
SRT (Visual, adjacent, 
probabilistic serial RT)
Cognitive abilities tasks
Raven advanced-(IQ) 
Digit span Wais-R (WM) 
Verbal WM (NITE) 
Switch task (executive func.) 
Syntactic processing 
Rapid naming (RAN). 
Design and procedure
A within-subject design.
4 separate testing sessions.
Each participant is tested once in all tasks of
cognitive abilities.
Each participant is tested twice in all tasks of
statistical learning, test-retest.
Testing sessions of S.L are separated by at least
three months.
Visual Statistical Learning-VSL task
(adapted from Turk-Browne et al. 2005, by Glicksohn & Cohen, 2011)
24 shapes:
VSL- implicit TPs
8 Triplets
1
1
Foils
0 0
The 8 triplets are presented in a random order to
…
create a 10 minutes familiarization stream.
…
Q: Can S’ pick-up the rules regarding the TPs of the
visual shapes?
The experimental setting
Familiarization:
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
Try again…
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
Try again…
The experimental setting
Test: Which triplet belongs to the sequence?
Auditory Statistical Learning – Adjacent
Endress & Mehler, 2009
12 words
0.5
0.5
Part-Words
0.5
0.18
bo de sa
de sa le
le ka ti
ka ti lu
lu ri vo
sa lu ri
…
…
The 12 words are presented in a random order to
create a 10 minutes familiarization stream.
The experimental setting
Familiarization:
(10 minutes long…)
Test: Which word belongs to the language?
The experimental setting
Familiarization:
(10 minutes long…)
Test: Which word belongs to the language?
Try again…
The experimental setting
Familiarization:
(10 minutes long…)
Test: Which word belongs to the language?
The experimental setting
Test: Which word belongs to the language?
The experimental setting
Test: Which word belongs to the language?
Try again…
The experimental setting
Test: Which word belongs to the language?
The experimental setting
Test: Which word belongs to the language?
The experimental setting
Test: Which word belongs to the language?
Try again…
The experimental setting
Test: Which word belongs to the language?
Statistical Learning: Non-Linguistic
Gebhart, Newport & Aslin (2009)
Exactly the same as the adjacent linguistic statistical
learning experiment, with the only difference that the
syllables are replaced with18 non-linguistics noises (A,B …
R).
12 Triplets
0.5
0.5
Part-Triplets
0.5
0.187
ABM
BMJ
JQL
0.187 0.5
GQI
IJQ
…
…
The experimental setting
Familiarization:
(10 minutes long…)
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
Try again…
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
Try again…
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
The experimental setting
Test: Which triplet belongs to the sequence?
Try again…
The experimental setting
Test: Which triplet belongs to the sequence?
Statistical Learning - Non Adjacent
Subjects can extract “words” based upon the non-adjacent
statistics: Words have consonant “roots”.
Adjacent TP between syllables: p=0.5
Non-adjacent TP between consonant: within words: p=1.
between words: p=0.5
12 words
Part-words
pa ve gu
du ka be
me tu sa
ve gu da
pa vo ga
du ki bo
me ta si
tu sa du
pi ve ga
da ka bo
mo tu si
pi vo gu
da ki be
mo ta sa
…
Statistical Learning - Non Adjacent
During the test, Participants hear legal “words” that are
composed of a consonant root and a new vowel patterns,
and nonwords that are assembled from “part-roots” and the
same vowel patterns, and are requested to choose which
belongs to “language”.
Legal Words
Foils – part roots
pu ve gi
vu ge di
po vi ga
bo pi va
di ku bo
gi du ko
du ke ba
ku be ma
me to sa
te so da
ma tu se
ga mu te
The experimental setting
Familiarization:
(10 minutes long…)
Test: Which word belongs to the language?
The experimental setting
Test: Which word belongs to the language?
Try again…
The experimental setting
Test: Which word belongs to the language?
The experimental setting
Test: Which word belongs to the language?
The experimental setting
Test: Which word belongs to the language?
Try again…
The experimental setting
Test: Which word belongs to the language?
Probabilistic Serial Learning Task
Kaufman et al., 2010
In each trial, stimulus is appearing at one of four
locations on the screen, and subjects are asked to
press a corresponding key.
Press ‘1’
Press ‘2’
Press ‘3’
Press ‘4’
Probabilistic Serial Learning Task
Kaufman et al., 2010
Subjects are unaware that the sequence of the successive
stimuli is determined probabilistically by the last two
presented stimuli:
•
•
P= 0.85 for sequence A
(1-2-1-4-3-2-4-1-3-4-2-3).
P= 0.15 for sequence B
(3-2-3-4-1-2-4-3-1-4-2-1).
Last two stimuli
p=0.85
p=0.15
Probabilistic Serial Learning Task
Kaufman et al., 2010
Total
of
960
trials
in
8
blocks.
•
The dependant variable for this task is the
•difference
between the average RT in the
trials taken from the 85% sequence
(probable trials), and the average RT in the
trials taken from the 15% sequence
(improbable trials) over the last 6 blocks (720
trials). This shows whether S’ implicitly
learned the probability of sequences.
Distribution of scores in the five
S.L/I.L tasks
In order for the tasks to reliably predict
cognitive abilities, the task has to:
 Have a level of difficulty that is not at floor
or at ceiling.
 Have a variance that is large enough to
allow for a wide distribution of individual
differences.
Visual Statistical Learning Distribution
18
n=179
16
14
12
10
8
6
4
2
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Mean=22.2 (of 32, 69.4%), SD=5.56
Auditory Statistical Learning
(Adjacent) Distribution
12
n=102
10
8
6
4
2
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Mean=21.6 (of 36, 59.1%), SD=5.64
Auditory Statistical Learning (NonLinguistic, Adjacent) Distribution
16
14
n=103
12
10
8
6
4
2
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Mean=20.56 (of 36, 57.1%), SD=3.33
Auditory Statistical Learning
(Linguistic, Non-Adjacent) Distribution
16
14
n=102
12
10
8
6
4
2
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Mean=20.58 (of 36, 57.1%), SD=3.98*
*but without the two extreme observations: SD = 3.52
Serial Reaction Time (SRT) Distribution
12
n=48
10
8
6
4
2
0
-15 -10
-5
0
5
10
15
20
25
30
35
40
45
50
55
Mean=17.54 ms, SD = 17.36
60
65
70
75
80
Summary – SL tasks Distributions
Task
Floor/Ceiling
Variance
VSL
V
V
ASL adjacent
V
V
ASL
nonadjacent
V
X
SL
nonlinguistic
V
X
SRT
V
V
Q1:
Is statistical learning
a subset (nested) of general
cognitive abilities?
What are the inter-correlations of scores in
the various S.L tasks and the scores
obtained in the cognitive tasks?
Cognitive Tasks






Raven advanced- IQ
RAN (dig., let., obj.)- Output speed
Digit span- WM
Verbal working memory
Syntactic processing- verbal abilities
Switch task- Executive functions
If S.L. is an independent theoretical construct, we
should expect it not to be nested within a given
faculty (very small correlations), with some
correlation with intelligence.
Switch task (executive functions)
Participants are presented with letters or digits, in
one of four different locations. Letters are always
shown in the first and second positions, and digits
in the third and fourth.
In each trial, one stimulus is presented (a letter or
a digit).
When seeing a digit – participants are asked to
decide whether the digit is odd or even.
When seeing a letter – whether the letter is
consonant or vowel.
Switch task (executive functions)
For example:
#A
‘right shift’ (vowel)
Switch task (executive functions)
For example:
‘left shift’ (consonant)
%R
Switch task (executive functions)
For example:
‘right shift’(even)
&6
Switch task (executive functions)
For example:
@1
‘left shift’ (odd)
Switch task (executive functions)
There are two kinds of trials:
1. ‘Stay’ Trials: When the stimulus is of the same
type as the previous stimulus (letter after a
letter or a digit after a digit).
2. ‘Switch’ Trials: When the stimulus is of a
different kind from the previous stimulus (letter
after a digit or a digit after a letter).
The dependant variable= mean RT difference
between ‘Switch’ trials and ‘Stay’ trials.
Syntactic Processing
Participants read a syntactically complex sentence,
followed by two short sentences, and asked to
choose which of the two sentences is semantically
congruent with the target sentence.
The Gardener who stood next to the teacher ran away.
The Gardener ran away.
The teacher ran away.
The score is a standardized score based on the subject’s RT,
provided by the Israeli National Institute of Testing and
Evaluation (NITE).
Correlations of VSL and tests of
general cognitive capacities
VWM
0.194*
VSL
(n=178,
p=0.01)
Raven’s
Advanced
Matrices
Switch
Task
Syntactic
Processing
0.151 0.238*
(n=103) (n=103,
p=0.01)
-0.163
(n=47)
-0.029
(n=47)
DIGIT
SPAN
RAN objects
VSL
0.147
(n=123)
RAN letters
RAN digits
0.023
0.103
(n=123) (n=123)
(all other p’s > 0.05)
Correlations of ASL (adjacent) and
tests of general cognitive capacities
VWM
ASL
ASL
DIGIT
SPAN
Raven’s
Advanced
Matrices
Switch
Task
Syntactic
Processing
0.051
-0.021 0.002 -0.018 0.045
(n=102) (n=102) (n=102) (n=47) (n=47)
RAN objects
RAN letters
RAN digits
-0.060
(n=48)
0.075
(n=48)
0.072
(n=48)
all p’s > 0.2
Correlations of AVN (non-adjacent)
and tests of general cognitive capacities
AVN
AVN
Raven’s
Advanced
Matrices
Switch
Task
Syntactic
Processing
-0.085 -0.116 -0.049 0.060
(n=102) (n=102) (n=102) (n=47)
0.012
(n=47)
VWM
DIGIT
SPAN
RAN objects
RAN letters
RAN figures
-0.152
(n=48)
0.033
(n=48)
-0.046
(n=48)
all p’s > 0.2
Correlations of ANA (non-linguistic)
and tests of general cognitive capacities
VWM
ANA
ANA
DIGIT
SPAN
Raven’s
Advanced
Matrices
Switch
Task
-0.084 0.039
0.118
0.096
(n=102) (n=103) (n=103) (n=47)
RAN objects
RAN letters
RAN figures
0.039
(n=48)
-0.065
(n=48)
0.080
(n=48)
Syntactic
Processing
0.136
(n=47)
all p’s > 0.2
Correlations of SRT and tests of general
cognitive capacities
SRT
SRT
VWM
DIGIT
SPAN
Raven’s
Advanced
Matrices
Switch
Task
Syntactic
Processing
-0.090
0.081
0.165
0.150
-0.111
RAN objects
RAN letters
RAN figures
-0.034
-0.166
-0.128
all other n’s = 48, all p’s > 0.2
Q2:
Is statistical learning a
unified or componential
Capacity?
What are the inter-correlations of scores
in the various S.L tasks?
Correlations between the different
Statistical Learning Tasks
VSL
VSL
ASLadjacent
ASLnon
adjacent
ASLNon
linguistic
***
ASLadjacent
ASL-
ASL-
non
adjacent
non
linguistic
0.084 -0.191 0.080
***
-0.056 0.009
***
-0.132
***
n=102,
all p’s > 0.05
Correlations between SL tasks and
SRT
VSL
SRT
ASL-
ASL-
ASL-
adj
non adj
Non
linguistic
0.112 -0.189 0.131 -0.001
n=48, all p’s > 0.2
Q3:
Is statistical learning a
reliable and stable
capacity?
Is there a test-retest reliability ?
Test-Retest Reliability - VSL
32
28
24
20
16
12
8
4
0
0
4
8
12
16
20
24
28
32
n=47, r=0.67
Test-Retest Reliability – ASL
36
32
28
24
20
16
12
8
4
0
0
4
8
12
16
20
24
28
32
36
n=48, r=0.6
Test-Retest Reliability – ASL Non-Adjacent
36
32
28
24
20
16
12
8
4
0
0
4
8
12
16
20
24
28
32
36
n=46, r=0.49
Test-Retest Reliability – ASL Non-linguistic
36
32
28
24
20
16
12
8
4
0
0
4
8
12
16
20
24
28
32
36
n=48, r=0.1
Test-Retest Reliability – SRT
100.00
80.00
60.00
40.00
20.00
-20.00
-10.00
0.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
n=47, r=0.31
Is there a learning effect from T1 to T2?
T1
average
T2
average
# subs
improved
# subs
not
improved
VSL
21.55
21.91
22
25
ASL adj
20.73
22.58
29
19
ASL
nonadj
20.76
19.81
19
27
SL
nonling
19.29
21.39
29
19
pSRT
17.44 ms
36.23 ms
39
8
Summary – S.L tasks
Task
Test/retest
reliability
Some
correlation
with
intelligence
VSL
V
V
ASL adjacent
V
X
ASL
nonadjacent
V
X
SL
nonlinguistic
X
X
SRT
X
X
Conclusions- S.L tasks
1. Not all tasks that monitor S.L as an individual
ability are equally good in terms of performance
distribution (variance) and test-retest reliability.
2. From all the tasks examined in our study, VSL
seems to provide the best fit. It has a normal
distribution of performance, it is reliable, it has a
small but significant correlation with intelligence,
and it does seem to predict success or failure in L2
literacy (e.g., Frost et al. 2013, Psych Science).
3. Tasks that do not allow for test-retest reliability
should be avoided when individual differences are
concerned (SRT, non-linguistic auditory sounds).
General conclusions
1. S.L is not a unified ability. Individuals may be good
in detecting correlations in one context and not as
good in another.
2. S.L is not a subset of intelligence or WM, although
some task correlate with intelligence.
3. Detection of correlations and regularities in a given
context seems to be a stable and reliable individual
ability.
4. Our research so far shows that individual
differences in detecting transitional probabilities of
adjacent shapes in the visual modality are predictor
of L2 literacy acquisition.
More questions to be answered:
S.L and language learning:
 Is there something specific about the correlation
of VSL with reading, or does VSL correlate with
other aspects of L2 learning?
 Do different aspects of S.L predict different
aspects of L2 learning? Is a certain aspect of
S.L. particularly important in a specific
language? (i.e., non-adjacent S.L for Hebrew?
 Is there an influence of the native linguistic
environment on the ability to pick-up statistics?
 Training in S.L. – will improve L2 learning?
More questions to be answered:
The mechanism of S.L and methodology
 How do our results (that points to a
componential ability) can co-exist with Arit’s
model of S.L?
 How can we improve the measurement of S.L?
 Towards a normalized measurement of
individual differences in S.L?
Thanks
The Laboratory for Verbal Information Processing
 Alona Narkiss
 Henry Brice
 Tali Ben-porat
 Dana Yankelevich
 Amit Elazar