Research Article: Implicit learning of what comes when and where within a sequence: The time-course of acquiring serial position-item and item-item associations to represent serial order

Date Published: May 21, 2012

Publisher: University of Finance and Management in Warsaw

Author(s): Nicolas W. Schuck, Robert Gaschler, Peter A. Frensch.


Much research has been conducted aimed at the representations and mechanisms that
enable learning of sequential structures. A central debate concerns the question
whether item-item associations (i.e., in the sequence A-B-C-D,
B comes after A) or associations of item
and serial list position (i.e., B is the second item in the
list) are used to represent serial order. Previously, we showed that in a
variant of the implicit serial reaction time task, the sequence representation
contains associations between serial position and item information (Schuck, Gaschler, Keisler, & Frensch,
2011). Here, we applied models and research methods from working
memory research to implicit serial learning to replicate and extend our
findings. The experiment involved three sessions of sequence learning. Results
support the view that participants acquire knowledge about order structure
(item-item associations) and about ordinal structure (serial position-item
associations). Analyses suggest that only the simultaneous use of the two types
of knowledge acquisition can explain learning-related performance increases.
Additionally, our results indicate that serial list position information plays a
role very early in learning and that inter-item associations increasingly
control behavior in later stages.

Partial Text

The ability to flexibly store and retrieve sequential structures is fundamental to
human cognition and ubiquitous in human behavior, such as in language or skill
acquisition. The major theoretical challenge – the problem of serial order – in this
field is twofold: first, to explain how a largely parallel system like the brain can
store and produce sequentially ordered outputs (e.g., Houghton & Hartley, 1995). Second, the flexibility of
serial memory/actions one can observe in humans seems to rule out traditional memory
accounts that exclusively rely on associations between successive items (so called
chaining; see Lashley,
1951). Consequently, the question of how the order and timing of events
can be computed, stored, and retrieved has been investigated in a variety of
different research contexts, such as working memory (e.g., Botvinick & Watanabe, 2007; Burgess & Hitch, 1999, 2006;
Henson, 1998), motor learning (e.g.,
Salinas, 2009; Tanji, 2001), long-term memory (e.g., Howard & Kahana, 2002; Nairne, 1992), interval timing (e.g., Ivry & Spencer, 2004; Meck, Penney,
& Pouthas, 2008), numerical cognition (e.g., Nieder, 2005; Verguts &
Fias, 2006), and sequence learning in animals (e.g., Burns & Dunkman, 2000; Terrace, 2005). All this work is related to the issue of
whether representations of the position of an item within a list (e.g.,
B is the second item in a list) are necessary to explain
sequence representation, or if associations between successive items (e.g.,
B comes after A) are adequate as the sole
mechanism. In a nutshell, the debate has been focused on the question what is the
functional stimulus in serial learning and memory, the preceding action or the
serial position/time of the action (Young,
1962; Young, Hakes, & Hicks,

All analyses were conducted using R (R Development
Core Team, 2010). For all analyses conducted with RTs in the following
sections, erroneous responses and responses following errors were excluded. To
reduce the influence of outliers, analyses were conducted based on the median RT for
each participant in each of the factor cells (Luce,
1991) that constituted the analysis. Thus, unless otherwise noted,
analyses were based on the individual median RTs per block. The p-values
accompanying correlations are according estimations as implemented in the stats
package in R (R Development Core Team,

We propose – in line with research from other serial learning tasks – that in the
present task, implicit sequence knowledge may represent (a) transposition
probabilities between successive target screen locations, and (b) contingencies
between serial positions and target screen locations (e.g., Ebenholtz, 1963, 1966;
Young, 1962). We hypothesized that these
kinds of information are stored in (a) item-item associations and (b) associations
between serial positions and items, respectively. Unlike in many other experiments,
we based our analyses on the assumption that both types of associations are actively
and simultaneously supporting serial learning. To test our assumption, we
administered transfer blocks in regular intervals throughout a prolonged practice
phase of a serial reaction time task. In these transfer blocks, the targets appeared
in new sequences that were derived from the learned sequences. The analysis of RTs
in these sequences then allowed us to test separately if item-item and serial
position-item associations had been acquired.