Discussion of Jost et al. (2019): Input complexity affects long-term retention of statistically learned regularities in an artificial language learning task

Input complexity affects long-term
retention of statistically learned
regularities in an artificial
language learning task
Ethan Jost, Katherine Brill-Schuetz, Kara Morgan-Short
and Morten H. Christiansen
2019
Frontiers in Human Neuroscience

Statistical learning
• Latent distributional properties in a disorganised environment

• Very early development in humans (Kirkham et al., 2002)
• Shown by other animals (Hauser et al., 2001)
• Uses in statistics and cognitive sciences (Louwerse & Connell, 2011)

• Relatively implicit and unconscious (cf. textbook instruction)

• Relatively implicit and unconscious (cf. textbook instruction)
• SL might redress the poverty of the stimulus and indeed convey an
advantage to children during language development (Newport, 1990)

Consolidation and retention
• Not so many studies
• Some studies not using linguistic stimuli

• Longitudinal testing
• Weeks (Jost et al., 2019)

• Months (Morgan-Short et al., 2012)
• Limitation: great degree of practice obscuring effect of exposure

• Months (Kara Morgan-Short et al., 2012)
• One year (Kobor et al., 2017; Romano et al., 2010; Smalle et al., 2017)
• Limitations of Kobor et al. (2017) and Romano et al. (2010): no language, no meaning,
simple statistical structure in stimuli, no generalisation of regularities to new items

• Months (Morgan-Short et al., 2012)
• One year (Kobor et al., 2017; Romano et al., 2010; Smalle et al., 2017)
• Limitations of Kobor et al. (2017) and Romano et al. (2010): no language, no meaning,
simple statistical structure in stimuli, no generalisation of regularities to new items
• 17 years (Mitchell, 2006)

• Sleep (reviewed by Jost et al., 2019)

• Spaced repetition (reviewed by Ullman & Lovelett, 2018)

• Retrieval practice (reviewed by Ullman & Lovelett, 2018)

• …

• Incremental stimulus complexity (Poletiek et al., 2018)

• Primary direction of each of the three clusters (10 seconds):
https://www.jspsych.org/6.3/demos/jspsych-rdk-demo3.html

Discussion of Jost et al. (2019): Input complexity affects long-term retention of statistically learned regularities in an artificial language learning task

• M = 6.93
• Remember the order or the frequency of the numbers?

• M = 6.93
• Remember the order or the frequency of the numbers?
w 3 5 6 4 9
7 5 8 5 6
3 8 7 4 2 n

• Incremental stimulus complexity (Poletiek et al., 2018)
• Simplication of the task
• Exogenous (e.g., by researchers)
• Endogenous (by oneself)
• Chunking

Chunking
‘A mechanism by which distributional regularities are used to form
discrete representations of an input’ (Isbilen et al., 2022)

Chunking
Smalle et al. (2018)
• Hebb-repetition task: ‘From a total of 36 different drawings, nine
drawings were associated with each set of three CV-pools. Of these nine
drawings, three drawings were associated with the pool used to
generate the filler sequences; another three drawings were associated
with the first Hebb sequence and the remaining three drawings were
associated with the second Hebb sequence.’
• Findings: children exhibited better retention than adults after 1 year.
• Limitations: no test of input complexity, no generalisation to novel items

Chunking
Jost et al. (2019): ‘Rapidly recoding and compressing information
by chunking may allow learners to more efficiently process input,
and to do so at higher levels of abstraction. In fact, stronger
learners may show a decreased reliance on surface-level
fragment information when tested due to the fact that they have
already used that information to internalize the higher-order
regularities, and no longer rely on them as a crutch.’

Jost et al. (2019)
• Retention: 2-week delay
• Hypothesis: overall retention
• Input complexity
• Hypothesis: greater complexity  impaired learning
• Chunking
• Hypothesis: impaired learners  less reliance on grammatical regularities 
more reliance on fragment information, or chunk strength
• Smaller effect of chunk strength in Session 2

Jost et al. (2019)
• Implicit instruction: training input contained grammatical regularities
but participants’ attention was not explicitly drawn to the regularities.
• Simple training group (N = 23): 80% simple, 20% complex input
• Complex training group (N = 24): 80% complex, 20% simple input

Observations on the statistics
• Participants and items. No random slopes  Violation of
independence of observations (Brauer & Curtin, 2018)
• Somewhat lax workflow of tests: number could have been smaller
• Correlations
• Calculation of p values was somewhat anti-conservative
(see Luke, 2017): namely, model comparison instead of Satterthwaite
or Kenward-Roger approximation for degrees of freedom

Conclusions
• Complexity of stimuli impaired retention
• Complex training group relied more on surface-level fragment
information (chunk strength) than simple training group

Discussion of Jost et al. (2019): Input complexity affects long-term retention of statistically learned regularities in an artificial language learning task

Discussion of Jost et al. (2019): Input complexity affects long-term retention of statistically learned regularities in an artificial language learning task

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