Introduction
Repetition is one of the most interesting phenomena
because it captures the transition from the first strenuous effort at
solving a task to an automatized and much more effortless process (
Logan, 1990;
Fecteau and Munoz, 2003).
In verbal memory, rehearsal develops at about 7 years as indicated by
the onset of the phonological similarity effect at this age (
Jarrold and Tam, 2011,
p. 186), yet these authors hold that the onset of verbal rehearsal in
general may nevertheless be gradual rather than discrete. Indeed, in the
development of reading, repetition was shown to be of major importance
already at a young age (
Horst et al., 2011;
Horst, 2013).
The effect of repetition is also extensively exploited in supervised
neural networks where in each repetition an error feedback signal is
considered in order to optimize learning (
McLeod et al., 1998).
The adaptation of the neural structure often takes many sweeps. To take
time to memorize to perfection by rote learning was already measured in
1885 (
Ebbinghaus, 1964).
Ebbinghaus meticulously recorded the time it took him to learn
non-sense syllables perfectly by heart and found that on each
repetition, he needed less time to achieve the same performance level.
Furthermore, a neural network simulation using the original Ebbinghaus
stimuli as input showed that the network learned better and more
accurately without transformational (conceptual) hidden nodes, but
produced the same output as input with a direct
mapping approach.
Networks always needed 200 sweeps independently whether a graphic or a
phonological code was used, or homogeneous or mixed lists had to be
learned – it just queued the stimuli into a sequence for output (
Lange-Küttner, 2011; see also
Mitchell and Zipser, 2003).
This early Ebbinghaus experiment showed that we do not
necessarily need rehearse just whole words. The current study
investigates whether rehearsal in a verbal recall task may actually
involve word fragments. This hypothesis is backed up by recent work that
shows that word structure is relevant for reading (
Lange-Küttner, 2005) as well as for word memory (
Lange-Küttner and Krappmann, 2011). In neural networks and reading research, usually the word onset, vowel and coda (
Plaut et al., 1996) or the ‘grain size’ of units (
Ziegler and Goswami, 2005)
are distinguished as building blocks of a word. In memory research,
participants could visually recognize word fragments that they had seen
in a presentation – even if they were unable to complete the word
fragment into a whole word (
Challis and Sidhu, 1993;
Nyberg et al., 1994;
Cleary and Greene, 2000). Meaningful fragments (
Cleary, 2002) and more frequent fragments (
Cleary and Greene, 2001)
were easier to recognize. Already 5-year-old British children who are
beginning to read are able to recognize word fragments such as ‘bzn’ for
the word ‘basin’ and they can even distinguish it from another fragment
where instead of the phonetic cue ‘z’ for the word ‘basin’ a control
cue ‘f’ is used (
Rack et al., 1994).
In the current study, only whole words and pseudo-words varying in
familiarity and content were presented. Instead, we analyzed whether
participants generated word fragments when writing down their responses
in the recall phase of the word memory experiment. We used a new method
that measured the percentage of correctly remembered word structure.
From participants’ written recall of words, we scored not only the
correctly sequenced words, but also the amount of letters in a correct
sequence in fragments of a proper word. We disregarded missing or added
letters, and just computed the number of correctly recalled letters as
the percentage of the actual word length, because the word length effect
is one of the most robust effects in word memory (
Baddeley et al., 1975).
More specifically, we hypothesized that like young
infants who gradually learn the correct pronunciation of a word in their
spoken word production and simultaneously drop their approximations and
inventions (
Dromi, 1987),
the young adults in the current study would be able to gradually write
the correct orthography of a memorized word in their written word
production in a word recall task when trying several times. All
participants repeated the recall of the word lists four times, because
we know that rehearsal and repetition enhances word memory as such as
well as the length of a word that can be remembered (
Samuels et al., 1979).
We were interested whether verbal recall would improve more when the
words were immediately repeated in the next three blocks, or whether a
less forceful rehearsal with a randomly distributed encounter of each
word list would facilitate word recall more. The distinction of massed
versus distributed practice in verbal learning usually refers to the
length of the inter-stimulus interval (ISI).
Underwood (1961) claimed that a long ISI allows time for the successive extinction of
errors, while a short ISI would suppress errors rather than extinguish
them.
We did not vary the length of the ISI of trials thus
each block had the same length. However, we did vary the sequence of the
blocks in order to test massed versus distributed practice. One group
of participants experienced each list four times in immediate succession
in a kind of forced rehearsal. The other group of participants also
experienced each word list four times, but the repeated word lists were
presented in a mixed sequence randomized by the computer program for
more incidental learning. Our prediction was that immediate repetition
of a word list would support verbal recall more than a randomly
distributed repetition. We assumed that an immediate repetition would
also have a stronger effect because it resembles the spontaneous
rehearsal of children and adults when they try to keep words in the mind
for fast and safe retrieval.
Fast word learning (word mapping) in children is also dependent on semantic factors (
Horst and Samuelson, 2008;
Carey, 2010).
Because we tested mainly young people with different ethnic backgrounds
and from many countries who often spoke more languages than just
English, we also monitored the content of the words. We tested names of
British and international (INT) European towns (no capitals and
controlled for town size). We expected that the UK towns would be easier
to remember than the INT towns because of a geocentric memory bias (
Baddeley, 1999,
pp. 158–159). Furthermore, we tested low frequency (LF) English words
against INT non-words to control familiarity. INT non-words were
previously used for a cross-cultural comparison of word reading in young
adults (
Paulesu et al., 2000) and vocabulary learning in children (
Morra and Camba, 2009) in order to avoid that non-words would vary in familiarity like existing lexical items (
Treiman et al., 1990).
We created the INT non-words by translating the English LF words into
German, Danish, French, Italian, and Spanish. We then randomly took two
or three word fragments (depending on the length of the original word)
and combined them into a new word which contained legal letter sequences
from the foreign words.
In summary, we designed a word recall task that
controlled for repetition intensity, content and familiarity. The
theoretically relevant idea for this investigation into visual word
memory is to evaluate the memory fragments that the young people
recalled instead of only the absolutely correct whole words that were
remembered. In this way, we may be able to discover whether visual word
memory rehearsal also involves word fragments, and whether these
remembered fragments are a gradual approximation toward memorizing whole
words. Similar response evaluations that distinguished between
partially correct and completely correct responses were conducted in
research with spoken stimuli and spoken responses (
Storkel et al., 2006) which will allow comparison in the Discussion.
Materials and Methods
Participants
There were n = 80 participants in this study, n = 37 monolinguals (20 females) and n
= 43 bilinguals (26 females). All participants were students of the
London Metropolitan University, City Campus. The mean age was 27 years
(SD = 9 years, range 18–55 years). Monolinguals were native
English-speakers with British or US nationality. Nationalities of the
bilinguals varied widely, with 27 different nationalities.
Material
Word frequencies, range and distribution were taken from the British National Corpus (
Leech et al., 2001). All LF words had a frequency below 50. The methodology of testing with INT non-words words was adopted from
Paulesu et al. (2000). The generation of the INT word list from the LF words using translations into foreign languages is presented in Table
1.
The LF words were all nouns with relatively different translations in
German, Danish, French, Italian, or Spanish. We did not use words like
‘monarchy’ that would have been nearly the same in all the translations.
Word fragments used for the creation of the new INT words are set in
bold in Table
1.
Four different types of word
lists were used, LF familiar words versus INT non-words, and UK places
versus INT places, as per Table
2. The LF words and INT non-words (the white area in Table
2) were matched for amount of letters and number of spoken syllables, and so were the UK and INT places (the gray area in Table
2)
as far as possible because also the size of the towns in terms of
number of inhabitants was controlled. No names of capital towns were
used. Combined letters such as ‘st’ or ‘aa’ or ‘nn’ or ‘ei’ were counted
as one letter when spoken as one sound. Consonant clusters are used as
one sound in experimental studies (e.g.,
Page et al., 2006,
p. 726) and their letter count varied on average by one or two letters
per word. Although consonant clusters, such as ‘st,’ are not listed in
the IPA phonetic alphabet, the phonetic voiceless alveolar sibilant
consonant ∫can be joined by a tie bar if for instance merged with
another sound like in ‘st’ or ‘sch’ in another language (
International Phonetic Association, 2005).
We also controlled phonotactic values (PTV, the sum of all phoneme probabilities per word;
Vitevitch and Luce, 2004) which are specified besides each word and averaged per word list in Table
2.
Averaged values give information about the overall ease of
pronunciation of a word because difficult phoneme transitions can be
ameliorated by easier phoneme transition (
Coleman and Pierrehumbert, 1997).
These PTVs are more commonly used in studies where words need to be
articulated as part of the experimental design in order to control for
the ease to pronounce a word. Ease of word pronunciation according to
PTV makes overt word repetition easier and has an interactive
relationship with vocabulary size in children and adults (
Edwards et al., 2004;
Munson et al., 2005). It also facilitates repetition level but not repetition rate in neural networks (
Gupta and Tisdale, 2009).
However, the current study investigated visual word
memory, that is, participants saw the words and wrote down the words
without a word being said. Thus the PTV was not an experimental design
factor. We also did not translate the memory items into a Klattese
transcription, but entered them as correctly spelled words – as the
participants encountered them in the experiment – into a Phonotactic
Probability Calculator that operates on the basis of an English language
word data base (
Kucera and Francis, 1967). The resulting PTV was related to word length as shorter words had lower values which conforms with earlier research (
Bailey and Hahn, 2001).
Furthermore, PTVs were not related to familiarity as the international
INT words had relatively similar PTVs to the more familiar words. Also
this result is in agreement with
Bailey and Hahn (2001) who emphasized that PTVs can vary more drastically between native
English words than in comparison to INT words. In the current study,
similarities may have occurred because the INT non-words and place names
were all from West European areas.
Thus, in general, when comparing the word lists, the
PTVs were relatively homogeneous. The average PTV of the four memory
lists in Word Lists 1 was
M = 25.5 (range 22–28) and in Word Lists 2 it was
M = 28.7 (range 27–32). All memory lists in Word Lists 1 were tested
before those in Word lists 2. Accordingly, block sequence was separately
permutated for Word Lists 1 and Word Lists 2. The computer programming
software Experimental Run Time System (ERTS;
Beringer, 1994)
was used to present the word lists and instruct the participants. One
word was presented at a time in a randomized sequence on a DOS computer
with a 15 inch screen. Each word was presented in Times Large 12 font in
white on a black background for 1000 ms, with an ISI of 500 ms. The
presentation of the words occurred in blocks of eight words (see Table
2).
When programming the experiment, the four types of word
lists were blocked into two sets (see Word Lists 1 and Word Lists 2 in
Table
2).
Because each word list was repeated four times, each set had 16 uniform
word list presentations. In the rehearsal condition, each word list
type was immediately repeated. In contrast, in the incidental learning
condition, the sequence of the four times repeated blocks A, B, C, and D
was randomly and completely permutated by the ERTS within each set,
rather than at fixed intervals (
Page et al., 2013). For instance, memory word list A
1-4 could be repeated at any place in the random sequence of 16 blocks (e.g., A
1, B
1, D
1, C
1, A
2, A
3, B
2…) and the maximum possible space between repetitions of block type A
1 and A
2 was about 12 blocks if the first block was repeated only at the end of the set (e.g., A
1, C
1, D
1, B
1, D
2, C
2, B
2, C
3, D
3, B
3, B
4, C
4, A
2, D
4, A
3, A
4).
Procedure
Participants were tested individually in a quiet
computer laboratory. The experiment was vetted and approved by the
Departmental Ethics Committee. Before the start of the experiment,
participants were provided with a Consent form which they signed.
Afterward, they received a Debrief form for informative details about
the nature of the study. They were randomly assigned to one of two
experimental conditions – condition 1 (immediate block repetition,
forced rehearsal) or condition 2 (program generated permutated block
sequence, incidental learning condition). Participants were provided
with paper notepads to write down their responses. They turned over a
sheet after each word list.
Instructions were given in written form on the computer
display. Participants were informed that some words made sense, while
others would not, and that each word list would be repeated four times
during the course of the experiment. Their task was to recall as many
words as they could remember. At the end of each word list presentation
they were asked to write down the words on paper in any order in which
they came to mind (free recall). There was no delay after the
presentation of each word list and recall time was not constrained.
Participants pressed the space bar to initiate the next word list
(self-paced block transitions).
Scoring
Participants’ responses were scored twice. Firstly, we
scored correctly memorized and orthographically correctly written whole
words. Accuracy was computed per block in per cent correct. We also
scored remembered words that were recognizably part of the memory list
but consisted of word fragments with only some letters in the correct
sequence. We disregarded missing (omissions) or added (intrusions)
letters. For instance, one word in the UK places list was ‘Salisbury.’
In the response word ‘Sailsbry’ (which has half a word with a different
meaning denoting the sails of a boat), the letter ‘i’ is in the wrong
place and the letter ‘u’ is missing, but all other letters are in a
correct sequence. Thus, the participant scored 7 letters out of 9
correct, and received a score of 7/9 = 77.8% accuracy for this word. In
another scoring example, a participant wrote ‘Sainsbury,’ that is, also
this participant made a semantic mistake, but for the whole word. The
participant remembered a similarly written word that denoted a British
supermarket instead of a British town. In this word, the letter ‘l’ is
missing and the letter ‘n’ is a wrong letter, but all other letters are
in the correct sequence, 8/9 = 88.9% correct (% correct per word). These
examples show that the meaning of the associated word may only have
been a memory trigger as the semantic association could be quite remote
to the actual stimulus, while the important feature is the orthographic
similarity with the target word. The results from the accurate and the
more lenient scoring were averaged per word type list, respectively,
across memory list 1 and 2.
Secondly, because the lenient scoring yielded higher
accuracy scores, we computed a stricter score for correct whole words
which was then subtracted from the values that were obtained with the
lenient scoring. The resulting scores were the pure values for just the
‘nearly correct words’ (word fragments) which we then compared with the
whole word score. The comparison allowed to test whether the effect of
repetition (rehearsal) relates not only to whole words but also to word
fragments. If there is a gradual approximation during rehearsal toward
the correct whole word, we expected that word fragments should decrease
during rehearsal/repetition and would correlate with the whole word
score in the subsequent block.
Results
The first analysis compares the two scoring methods.
Recall scores were analyzed with analyses of variance with repeated
measures. When the Mauchley’s Test of Sphericity was significant, the
degrees of freedom were adjusted according to Huynh-Feldt. In a second
analysis of variance thereafter, a fragments-only score was analyzed.
We conducted a 2 (Words/Places) × 2 (Familiarity) × 4
(Repetition) × 2 (Scoring Method) × 2 (Training) analysis of variance
with repeated measures on the first four factors, and type of training
as a between-subject factor. Differences due to age were partialled out
using the variable ‘age in years’ as covariate. In an initial analysis,
we also included the variables sex and language of the participants as
between-subject factors. However, the inclusion of these individual
difference factors made the analysis of variance very complex. Like
Logie (2011,
p. 243) predicted, the main experimental results did not change when
the individual difference variables were omitted. In short, men showed a
memory advantage for INT places. Bilinguals profited somewhat more from
immediate repetition, while monolinguals benefited from incidental
learning especially when words were unfamiliar. Because these results of
individual differences did not substantially contribute to the
hypothesis, the statistical details of this initial analysis are not
reported.
The details of the statistical results are listed in Table
3 and are not quoted again in the text. The main effect of training type
(immediate vs. distributed repetition of blocks) was not significant as a
between-subject factor showing that memory performance in general did
not vary in the two training groups. The main effects of scoring,
familiarity and repetition were all highly significant,
ps< 0.001. As expected, participants showed better word memory when
also correct letter sequences in word fragments were scored (
M = 57.2%) rather than just whole words (
M = 49.4%). Participants remembered the familiar LF English words and UK places (
M = 67.8%) better than the unfamiliar words, that is the INT non-words and INT places (
M = 38.8%). Furthermore, mere repetition nearly doubled word memory
accuracy which supports the hypothesis that prescribed rehearsal is an
efficient facilitator (Block 1
M = 38.3%, Block 2
M = 53.0%, Block 3
M = 60.1%, Block 4
M = 61.8%).
A content by familiarity effect interacted with (1) scoring and (2) repetition and training. LF English words (M = 67.9%) were remembered just as well as the UK places (M = 67.7%), with a difference of just 0.02%. However, the INT places (M = 45.6%) were less difficult to remember than the INT non-words (M
= 32.0%). As would be expected, the more challenging INT words
benefited considerably from the additional letter sequence scoring
compared to the whole word scoring, that is an increase of above 10%
occurred (INT non-words M = 37.0%/M = 26.9%; INT places M = 51.1%/M = 40.2%). In contrast, the UK places benefited only 6.2% (M = 70.8%/M = 64.6%) and the LF English words just 3.8% (LF English words M = 69.8%/M = 66.0%). Thus, the more difficult a word was to remember the more it benefited from the additional letter sequence scoring.
Moreover, immediate repetition could be more efficient for word memory recall than more incidental encounters, see Figure
1.
Post hoc independent
t-tests
(two-tailed) comparing the two rehearsal conditions per block showed
that the immediate repetition advantage only gradually emerged for the
INT words, that is, for INT places [Block 1
t(78) = 1.93,
p = 0.058; Block 2
t(78) = 1.56,
p = 0.123; Block 3
t(78) = 1.43;
p = 0.157; Block 4
t(78) = 2.04,
p = 0.044] and especially for the INT non-words [Block 1
t(78) = 0.543,
p = 0.058; Block 2
t(78) = 1.44,
p = 0.153; Block 3
t(78) = 2.74;
p = 0.008; Block 4
t(78) = 3.03,
p = 0.003].
Interestingly, we also found an effect of the scoring method in interaction with repetition and content (familiar/INT).
Post hoc pairwise
t-tests
(two-tailed) showed that memory for geographic place names was better
than for words. This difference stayed significant throughout the
experiment,
ts (79) > -4.03,
ps< 0.001, see Figure
2. Correlations between memory for place names and words increased when only whole words were scored (Block 1
r = 0.51, Block 2
r = 0.68, Block 3
r = 0.79, Block 4
r = 0.79), and also when letter sequences were scored in addition (Block 1
r = 0.50, Block 2
r = 0.72, Block 3
r = 0.84, Block 4
r = 0.82),
ps< 0.001. These results suggest that the content of the words became
less important for memory performance during the experiment because the
shared variance between the two types of memory lists increased. Figure
2 shows that indeed there is a subtle narrowing of the gap between place
names and words during practice which is slightly more pronounced when
scoring letter sequences (difference between initial and final gap =
2.51%) than when scoring whole words (difference between initial and
final gap = 2.19%).
Finally, the interaction of
scoring by repetition with familiarity showed that participants
particularly benefited from the scoring method which appreciated letter
sequences when they recalled unfamiliar words as they were significantly
more likely to recall unfamiliar than familiar words as word fragments,
see Figure
3.
Word Fragments
The second analysis compared the whole word score with
the partial word fragment score as described before in the Methods
section. Again, recall scores were analyzed with analyses of variance
with repeated measures. We conducted again a 2 (Words/Places) × 2
(Familiarity) × 4 (Repetition) × 2 (Scores) × by 2 (Training) analysis
of variance with repeated measures on the first four factors, type of
training as between-subject factor and age in years as covariate. Also
in this second analysis, the statistical effects are listed in Table
3, on the right hand side. Most of the statistical effects are the same, however, there are some important differences.
The effect size of the scoring effect more than doubled.
This occurred because there were on average significantly fewer word
fragments (M = 7.7%) than totally correct words (M =
57.2%). The scoring effect interacted with familiarity and training.
This interaction was further explored with two MANCOVAs (controlled for
age) separately for whole words and fragments, respectively. The type of
repetition mattered for whole words, F(1, 80) = 10.25, p = 0.002, h2 = 0.12. When familiar words were encountered it made little difference whether they were immediately (M = 69.8%) or incidentally (M
= 70.8%) repeated. In contrast, when unfamiliar words were encountered,
these were better remembered when repeated in immediate succession (M = 47.5%) than when encountered incidentally (M = 40.5%), t(78) = 2.26, p
= 0.027 (two-tailed). The type of training did not matter for word
fragments, though: In both memory training conditions, unfamiliar words
were more likely to be recalled as a fragment (immediate M = 10.8%, incidental M = 10.1%) than familiar words (immediate M = 5.9%, incidental M = 4.2%).
Importantly for the rehearsal hypothesis, the scoring
effect interacted with repetition, with a relatively large effect size
of η2 = 0.44, and this effect did not interact with the
timing of the blocks. The amount of orthographically correct recalled
words increased with repetition by 24.4% (Block 1 M = 41.6%, Block 2 M = 56.9%, Block 3 M = 64.1%, Block 4 M = 66.0%), while the word approximations increased by 1.8% (Block 1 M = 6.6%, Block 2 M = 7.9%, Block 3 M = 8.0%, Block 4 M = 8.4%). There was no decrease in word fragments.
Still, there was the possibility that the word fragments
did not increase as much because they were feeding into the increase of
correctly spelled words. To investigate this question, we computed the
four correlations between the four repeated blocks of the whole words
and the word fragments, respectively, and the three correlations of word
fragments with the subsequent block of whole words. We adapted the
level of significance to 0.05/11 correlations =
p< 0.004. The correlations in Table
4 show the same correlational pattern for the total sample as for all
four sub-samples. The repeated recall of correctly spelled whole words
correlated highly and significantly with each other, and likewise, word
fragments where only some letters were in the correct sequence
correlated significantly with each other from one block to the next,
although at a somewhat lower level. However, the recalled word fragments
showed not a single significant correlation with correct whole word
recall.
Discussion
Working memory as well as psycholinguistic research usually assumes that rehearsal is based on the phonological loop (
Gathercole and Baddeley, 1993,
1997;
Edwards et al., 2004;
Gupta and Tisdale, 2009). In particular, the processing of non-words gives important cues to language learning (
Gathercole, 2006a,
b).
We do not doubt these findings, but we do doubt that the phonological
loop and (sub-vocal) articulation are the only relevant systems of word
memory.
Page et al. (2006,
p. 732), for instance, write that when access to the loop would be
blocked by concurrent articulation, participants would need to fall back
‘on a largely unrehearsable visual store.’ Importantly for the current
study,
Page and Norris (2009) assume that the repetition and rehearsal of a word actually builds a
long-lasting long-term memory (LTM) representation, but of a
phonological word-form in the mental lexicon. However,
Darling and Havelka (2010) and
Darling et al. (2012) suggested instead that visual and verbal information of words are bound
together in the multi-sensory episodic memory system which is
integrated into the working memory system (
Logie, 2011;
Baddeley, 2012).
Likewise, in many word recognition models in reading research,
grapheme–phoneme correspondences are assumed to be made when reading
aloud (
Coltheart, 2012).
In the current study, participants were writing down
responses in a free word recall task, and hence they had to resort to
visual orthographic patterns that they saw before. They saw them several
times which gave them the opportunity to improve their memory
performance. However, participants did not only reproduce the actual
words from the memory list, but also wrote down partially correct words.
These sublexical word structures were found to occur also in spoken
responses (
Storkel et al., 2006). They are also common in children, for instance,
Treiman (1993) showed that first graders’ correct spellings increased within 1 year
from 888 to 1,989 correct spellings (124%), but so did the wrong
spellings from 1,135 to 1,605 wrong spellings (41.4%). Thus, both
accurately spelled words and words with wrong spellings increased,
albeit at different rates. Also in the current experiment with young
adults, however, written word fragments increased during repetition and
this showed no trade-off with correct words. The word fragments were
learned insofar as scores were correlated with each other during
repeated blocks, but not with correct words. This kind of
error learning during repetition also occurred in a serial recall task using just letters (
Couture et al., 2008) and in a word memory task (
Storkel et al., 2006).
In short, the current study makes a case that word fragment learning
showed the two sides of rehearsal and repetition: not just accurate
responses, also the probability of giving a wrong response increases
with the number of prior occurrences of that response.
Visual Orthographic Patterns of Letters
Friederici and Lachmann (2002) came to the conclusion that there are no brain areas which are
originally reserved for reading words. In development, brain areas with
other primary functions such as syntactic processing when reading
sentences, face recognition in the case of visual complex pattern
recognition when identifying words, or the lexicon for spoken words when
matching phonological forms, are recruited for reading print. The most
direct way to encode in a visual word memory task where words are read
from the screen and written down during recall would be
visual mapping (
Lange-Küttner, 2014) or
visual bootstrapping (
Darling et al., 2012).
However, modalities can or should interact in word memory.
Page et al. (2006) investigated the effects of repetition in the two modalities. Adults’
learning effects during repetition were locked into one modality without
any transfer in the case of letters and pictures. However, when words
were used, transfer occurred in the visual-then-auditory condition, but
not in the auditory-then-visual condition: A sound was associated with a
visual word seen before, but a visual word was not associated with a
spoken word heard before. Hence, we may be more likely to enliven the
‘graphic imagery’ of a written word with a sound than to think about how
a word is written after hearing it. This seems to indicate that we do
not have much visual imagery for written words.
Also in children, there was a clear culturally and
educationally shaped preference of children to recruit one modality only
for reading, either visual or auditory word memory (
Lange-Küttner and Krappmann, 2011). This selectivity in memory has been emphasized since quite some time (
Cowan, 1995).
In fact, when neural networks were run, double-modality word input and
double encoding was most beneficial for immediate word reproduction, but
only one working memory system was necessary to integrate a letter
sequence for word learning to occur (
Lange-Küttner and Krappmann, 2011).
Boys seem to have a preference for the visual modality which includes
perception of the fine visual detail of the orthographic letter patterns
(
Mohamed et al., 2011;
Huestegge et al., 2012).
These orthographic patterns are assumed to be stored in the brain in
the ‘visual word form system’ and can be evoked by writing (
Dehaene et al., 2010).
The letters in the orthographic pattern allow a much more precise
notation of sounds than is apparent in the sonograms of spoken language
where not only individual words but also individual sounds present a
segmentation problem (
Whitney, 1998, p. 142;
Lange-Küttner et al., 2013).
The digital transformation of naturally spoken speech into written
words still represents a major challenge for typing software. Likewise,
in school children the best predictor for writing inner speech into a
fluent text – besides writing speed – is word spelling accuracy (
Connelly et al., 2012).
Thus, one would suppose that also visual orthographic patterns of
letters are important for verbal memory and can be rehearsed.
We found indeed that in the written responses of our
participants, mere repetition nearly doubled accuracy which clearly
supports the hypothesis that prescribed rehearsal is an efficient
facilitator for word memory. For familiar words it made little
difference whether they were immediately or incidentally repeated, while
the unfamiliar INT words were better remembered when repeated in
immediate succession. The unfamiliar INT words were also more likely to
be recalled as a fragment than familiar words. This suggests that
unfamiliar words with foreign spellings benefit from immediate rehearsal
that builds up a visual orthographic template in LTM within a
relatively short time. Writing an unfamiliar word correctly, however, is
a fragile process which was not helped by immediate repetition. In the
following section we discuss why this may be the case.
Error Learning in Word Memory during Repetitions
We paid particular attention to the orthography that the
participants produced when recalling the word lists and writing down
their responses. When words were not spelled correctly yet still
identifiable as memory of the correct word, we scored the letters in the
right sequence as per cent of the actual word length. We wanted to know
whether these letter sequence word fragments would develop into proper
whole words if rehearsed several times. We predicted that if this would
be the case, word fragments should decrease during the repetition, while
the whole word score should increase. This hypothesis was partly
confirmed. One the one hand, it was true that rehearsal in the repeated
memory blocks produced a higher whole word memory score, on the other
hand, word fragments did not decrease, but increased too. Thus, the
expected trade-off between word fragments and whole words did not occur.
Instead, also the word fragments increased with repetition, albeit by a
smaller amount, but then there were also fewer word fragments than
whole words in participants’ response sheets. Word fragments were more
often produced in response to unfamiliar words, e.g., in response to the
INT non-words with legal letter combinations from other languages and
INT geographical places also following non-English language spelling
rules.
This confirmed results from a developmental study with
8- to 10-year-old children showing that non-words created from the
native language were easier to learn than non-words created from a
non-native language (
Morra and Camba, 2009).
The increase of word fragments suggests that during the experiment,
participants kept trying to cobble together letters into word patterns
that resembled the visual input word to some degree, not unlike the
5-year-old reading beginners adept in distinguishing visual word
fragments (
Rack et al., 1994).
Orthographic patterns were also scored in a serial recall task of letters with adults (
Couture et al., 2008).
Also in this study, correct recall of a letter in the right place
showed the same learning curve during repetition as erroneously recalled
letters, that is error learning occurred. Interestingly, in the study
of
Couture et al. (2008) the error learning during repetition occurred only when data of real
people were analyzed, but not when simulated data were used which
yielded an increase in correct answers while wrong responses stayed at
floor level.
In another study also partially correct responses were analyzed, but stimuli and responses were spoken (
Storkel et al., 2006).
The auditory format enabled the authors to control the stimuli for
phoneme transition difficulty (ease of pronunciation) and lexical
neighborhoods (number of similar words), two factors which impact on
non-word learning quite independently of each other (
Bailey and Hahn, 2001).
Storkel et al. (2006) showed that scores of both completely correct words and partially
correct sublexical word units increased during repetition. This
repetition effect did neither interact with lexical neighborhood density
nor with phonotactic probability of the words. Correct words increased
at a steady rate throughout seven repetitions, while partially correct
words leveled off after four exposures. However, no statistical
comparison was made which would have shown whether this difference would
have amounted to a significant interaction that denoted a trade-off
between partially correct words and complete words. Thus, in this study
it remains unclear whether adults could transform a spoken word
approximation into a proper word during repetition. To our knowledge,
only two studies so far showed that errors were actually decreasing
during repetition. One study used 10-item digit sequences from 0 to 9 in
an immediate serial recall task (
Cumming et al., 2003).
Importantly, errors were omission mistakes where participants would
initially fill in blanks, but during repetition became able to fill the
gaps. The same effect of repetition was found when letter sequences were
used (
Couture and Tremblay, 2006,
Experiment 3). However, this was not the case in 5–6 years old children
who improved with repetition, but not by supplementing missing
information in serial positions (
Mosse and Jarrold, 2010).
This may have been the case because at this age, children are not yet
fluent readers, and when they spell words with letters, commission
errors are more frequent than omission errors or reversals in the letter
sequence (
Treiman, 1993).
Nevertheless, the 5–6 years old children’s learning during repetition
correlated significantly with learning non-words, but not with regular
word learning. Hence, one conclusion could be that the input repetition
effect seems to transform novel information into familiar information
that can potentially be incorporated into a systematic database (
Plunkett and Marchman, 1990).
In the current study, it is very likely that the
correctly written down whole words were rehearsed via inner speech which
speaks to a straightforward involvement of the lexicon and semantic
LTM. Also in the
Storkel et al. (2006) study, memory for complete spoken words was determined by lexical neighborhood density only.
However, in the current study it is less likely that
also the partly correct written down word fragments were processed via
lexical access because they were immune to content and presentation
distribution effects. The amount of word fragments occurred also
independently of individual differences with regards to sex and
language. We must assume that when word fragments were written down as a
response that this visual orthographic pattern was remembered from the
presentation. A fragmented visual registering of the word input may be
responsible for partial recall because inserting a delay before a recall
test which could have been used for enhanced recovery did not make any
learning difference (
Oberauer and Meyer, 2009). The repeated learning would then serve as a kind of sensory visual learning (
Mortensen and Nachtigall, 2000;
Blum and Yonelinas, 2001) until an accurate word form has been registered that can be associated with some meaning. Also in the
Storkel et al. (2006) study with spoken word stimuli, the partially correct words were not
lexically retrieved, but instead phoneme transitions of the words were
important. Hence, one could conclude that learning of novel unfamiliar
words can begin on a very raw sensory level, for spoken words with
acoustic sounds and for written words with graphemes.
This result of different processes for complete vs.
partial word memory during rehearsal and repetition is further
underpinned by the finding that the increase in complete words and the
increase in word fragments occurred independently of each other, as we
could not find significant correlations between them. Word fragments in
the repetition were highly and significantly correlated with each other
in the total sample, with r values between 0.69 and 0.75. This
was somewhat lower than for whole words which correlated very highly
between 0.83 and 0.87 in the repetitions. This correlational pattern
could be replicated with a split-file method, with r values between 0.59 and 0.83 for word fragments and r
values for whole words between 0.85 and 0.95 in the repetitions. We
tested hypothesis-guided planned correlations and predicted that the
word fragment score in one block would correlate with the whole word
score in the next Block. However, these and also almost all of the other
correlations between word fragment scores and subsequent whole words
scores were not significant.
Moreover, we would like to suggest that it is likely that
also the increase of word fragments consisted of two processes. The
first process would be the rehearsal of the word fragment, and this
explains why there were significant correlations that could increase
during the repetition. The second process would be that increasingly
some more new word fragments were produced, and this relatively free
generative process explains why the correlations were on average lower
than for whole words. In the context of an immediate serial recall task,
Couture et al. (2008) found that repeated learning of visual letter sequences yielded 2,376
response mouse clicks. Of these clicks 938 responses were errors, with
468 repeated errors and 470 new errors. 159 repeated errors were from
the previous block, but 309 errors were from an even earlier block in
the experiment. This indicates that wrong letter sequences were well
remembered in visual LTM beyond the immediate recall context. When
increasing error learning during repetition is not analyzed this could
be mistaken for an absence of correct response learning, while in fact
both correct and wrong responses increase simultaneously (
Lafond et al., 2010). Also
McClelland (2001) warned that Hebbian learning may actually strengthen inappropriate
activations if for instance an over-inclusive prototype was generated
during learning.
Sparse Written Word Representations
Would partially correct words be similar to Mojibake? A
Mojibake of unintelligible characters emerges when different writing
systems clash, such as Japanese Kanji JIS and the Western Alphabetic
code ASCII (
Wlodarczyk, 2005). It is even suggested to make PDF word documents safer by using Mojibake (
Bakhtiyari et al., 2014).
PDF documents have an upper layer with an image of the text and a lower
layer with the letters that make up the words. It is suggested that a
way to increase PDF security would be to eliminate the letter sequences
and instead of well-sequenced letters only Mojibake would be offered in
the lower layer which would render copying of the PDF document
impossible.
This suggests that there may be also two layers of word
memory in participants, and not just in PDF documents. The current
experiment showed that there may be a lower sensory layer consisting of
free sparse word fragments which can be image-like pictures or
acoustic-like sounds and an upper layer that consists of
language-specific, orthographically and semantically constrained words.
This is just the opposite of what was suggested by Chomsky for spoken
language (
Chomsky, 1959,
2002;
Chomsky and DiNozzi, 1972).
He suggested that we are creative rather than conditioned insofar as
there is a lower layer of deep meaning anchored in action schemata,
while the human mind finds myriads of ways to express the meaning in
syntactic structures on a surface level. However, the current study
shows that when top–down word representations from a mental lexicon
cannot trigger an unfamiliar word from the LTM store because of complete
novelty, or a small constrained lexicon, incomplete sparse sublexical
bottom-up sensory impressions of word input take over (see also
Nuerk et al., 2000) which are reinforced over repetitions even if partly wrong.
A similar explanation was given by
Frick (1988) who wrote an immensely instructive early review about learning with
repetition, in particular the Hebb effect. The Hebb effect shows that
dispersed repeated sequences of letters, digits or words are better
learned than novel sequences in an immediate recall task, even if
participants do not notice the repetition (see also
McKelvie, 1987). Frick suggested a
recorder model with a fixed amount of recording tape. Thus, in general, reproduction
of words would show high fidelity of the original word. However, when
too many items are presented, only a small amount of representational
medium could be devoted to each item resulting in a low fidelity
representation. He described that while participants represent a set of
words, they do not represent psychophysical parameters such as duration,
or mimic the pitch, accent, rhythm or loudness (
Frick, 1988, p. 223, but see
Lange-Küttner et al., 2013).
Instead, an unparsed, uncategorized, more or less degraded input would
need to be recovered for recall. According to Frick, the recovery for
recall would represent a second level of processing which can be
facilitated with grouping or chunking (see also
Cumming et al., 2003) into categories or perceptual boundaries of Gestalt-like stimuli and stimulus sequences.
We would suggest that in the case of written words, this
process of recovery is not creative but on the contrary, it is
conventional insofar as it is governed via the lexicon that prescribes
an exact replication of the graphic orthography. In terms of working
memory, the inner scribe and the visual cache components of the
visual-spatial sketchpad of the working memory model may be likely
candidates for the visual rehearsal of words fragments.
Logie (2011,
p. 214) describes visual rehearsal as follows: ‘The Inner Scribe
component (…) can allow visual codes to be held for longer by mentally
rehearsing the codes held in the Visual Cache.’ Thus, we would suggest
that rehearsal of written word fragments is most likely to take place in
the inner scribe and the visual cache, firstly because participants
held some sparse details of recently perceived unfamiliar words (in the
visual cache), and secondly, during the repetition these were processed
further (in the inner scribe). However, in order to avoid learning wrong
words, an active mapping process would need to take place where the
visual slave systems are controlled by the central executive whether the
visual orthographic code matches LTM representations in the episodic
memory system that stores accumulated conventional orthographic patterns
encountered during previous experiences.
Storkel and Rogers (2000) showed that in spoken language, children were drawing an advantage from
more easily pronounceable words in word recognition only from age 10.
This late onset suggests that in word memory children develop
language-specific acoustic and probably also graphonomic sensitivities
relatively late after being taught to read. It also suggests that
increased sensitivities may need an increased categorical filter or
quality control. For instance, children seem to be biased toward
positive feedback whether it is justified or not (
Crone et al., 2004;
Eppinger et al., 2009;
Lange-Küttner et al., 2012)
which may help to persevere in a learning task, but not to discriminate
when words do not ‘look right.’ Moreover, the current study showed that
this is still the case in young adults if they encounter unfamiliar
words with no ready-made word template available for word recall.
Future Research Questions
In development, the onset of written language changes
word memory because the new visual modality is added to language. For
instance, in beginning readers, their small lexicon of written words
makes them rely heavily on familiar items in their visual word memory,
while the saturated lexicon of spoken words accumulated over several
years allows them to better memorize novel words (
Lange-Küttner and Martin, 1999;
Lange-Küttner, 2005;
Lange-Küttner and Krappmann, 2011).
Also children with reading difficulties produce significantly more
misspellings that are close visual matches to the target word rather
than phonological mismatches (
Lennox and Siegel, 1996).
This is why the current study put more weight on orthographic patterns
in visual word memory than on phonemic sound transitions in spoken word
memory. Visual word rehearsal may be counter-intuitive, but for written
language it is quite a crucial research question that needs further
testing. For instance, while it is a reasonable assumption that word
fragments develop into whole words, the current study did not find any
statistical evidence for a trade-off between word fragments and whole
words.
The finding of a persistent proportion of word fragments
in free recall is rather worrying. It has indeed been claimed recently
that error learning during repetition may be responsible for
developmental dyslexia (
Szmalec et al., 2011).
While learning with repetition was completely absent in dyslexic
participants when they had to remember the places of dots, it was only
attenuated in visual and auditory learning of letter sequences.
Likewise, also children with Down syndrome showed learning with
repetition comparable to normally developing children which explained
their good vocabulary despite a verbal short-term memory deficit (
Mosse and Jarrold, 2010).
Although the current study could not show that word
fragments would develop into a whole words during repeated rehearsals,
there is a hint in the non-significant correlations, which developed
from a negative into a positive correlation (monolingual males and
females), or from a positive into a negative correlation (bilingual
males and females) during the experiment, see Table
4.
While this appeared to be a smooth trend, none of these correlations
ever reached significance. We also tried to increase the correlations by
distinguishing between word fragments in response to familiar vs.
unfamiliar words, but again without obtaining significant correlations
with whole word responses of the same kind.
The comparison with previous research showed some
indicators that rehearsal of whole words and word fragments is based on
two different cognitive processes. Future research could use an
item-based methodology where the fate of an individual word fragment is
followed up. For instance,
Carey (2010) assumes that extended mapping with context information produces more
constrained meaning in words that were acquired via fast word mapping.
Hence, extended mapping could facilitate the transition of a response
from the lower free sensory layer to the upper semantically and
orthographically constrained layer. This transformation from a word
fragment to a proper word recall could be tested using the category size
effect (
Hunt and Seta, 1984).
This effect demonstrates that words from small categories are better
recalled following orientating relational processing, and words from
large categories are better recalled following individual item
processing. One could envisage an experiment where an increasingly
longer word list in the repetitions gradually provides more context
which could support the refinement of a word fragment into a correct
whole word, or an experiment where a word list gradually becomes more
homogeneous during repetition. For example, if the Word List with EU
towns would gradually change into a Word List with French towns only,
providing a more systematic database, would the first initially
introduced French town that was recalled as a word fragment be spelled
correctly once all town names are presented in the same language? In
this item-based experiment, unbeknown to the participants, only the
rehearsal of the first word fragment would be important, while the
remaining words could be left unscored.
We conclude that the current study provided compelling
evidence that written word fragments are likely to be produced when
unfamiliar words are encountered, and that these word fragments are
rehearsed and increase during repetition. We suggest that written word
fragments seem to be free and highly idiosyncratic which currently makes
it difficult to demonstrate how a written word fragment can be
rehearsed until a whole word emerges. We suggest that extended mapping
may simultaneously constrain the semantic content and the orthography of
a written word fragment so that it ‘looks right.’
However, it is also imaginable that word fragments never
develop into proper words but persist in memory. In the development of
young children’s first spoken word production, invented words were found
to be abruptly dropped in favor of conventional words only (
Dromi, 1987).
Anecdotal evidence from children shows that strict rules can control
orthographic output and inhibit the rehearsal activity at the lower
level rather than evolve it. We introduced this study with the neural
network simulation of the Ebbinghaus study (
Lange-Küttner, 2011) because
Ebbinghaus (1964) learned the nonsense syllables always to perfection and the gains that
he described were only in terms of time. However, a focus on perfect
accuracy may inevitably simultaneously inhibit the learning potential
with regards to memory for unfamiliar words of any kind. Hence, to
investigate error learning and the interactivity between fragile letter
sequences and robust word representations is an important future
research goal.
Conflict of Interest Statement
The authors declare that the research was conducted in
the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
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