“Purposeful behavior typically exists from (implicitly and explicitly) learned
series of events.” This is a typical justification for research on serial
learning. Indeed, it seems to be a sound and legitimate endeavourto try and probe
the cognitive and neural foundations of serial learning - because life would be
much more effortful without this ability. For example, think about parking your car,
playing Bach’s Goldberg Variations on the piano, or typing a letter on your
computer: These activities all require serial learning. This special issue entitled
Implicit Serial Learning is entirely devoted to the topic, its
major empirical questions, its numerous methodological challenges, and its link to
reality.Serial behavior as a research topic has a long history in experimental psychology. It
was once understood as the product of simple reflex chains, in which perceptual
feedback that derived from the previous movement triggers the next - and so on and
on. In a now classic paper, Lashley (1951)
brought (the acquisition of) serial behavior much more into the realm of cognitive
psychology, proposing a more plan-based account (see Rosenbaum, Cohen, Jax, Weiss, & Van der Wel, 2007, for a detailed
description). Since then several serial learning tasks, concepts, and theories have
been introduced and progressively have led to a long list of unanswered or partly
answered research questions about how serial behavior is represented in the mind and
brain. Notably, over the decades, a large portion of these questions have zoomed in
especially on implicit learning: learning that is incidental and the product of
which typically resides outside the realm of consciousness. (For more elaborate
definitions and characteristics of implicit learning and corresponding discussions,
please refer to Frensch & Rünger,
2003, and to Shanks, 2005.) Most
of the questions on the representational basis of implicit serial learning can be
roughly categorized into three major topics, and this special issue addresses these
topics:1. What is implicit serial learning, and how can we distinguish implicit
learning from its explicit counterpart?The concept of implicit serial learning is as intriguing as it is
problematic. Various issues are unsolved, both theoretically and methodologically.
In answering the question on the nature of implicit learning, most approaches
determine its features on the basis of often acclaimed distinctions between implicit
and explicit learning, such as the former’s relative automaticity and
independence of attentional resources. Mong, McCabe, and Clegg (2012), for example, took up the challenge of
identifying distinct processes within the process-impurity (with respect to the
automatic/controlled distinction) that is typical for (most) tasks. Mong et al.
proposed a novel implementation of the process dissociation procedure in serial
learning tasks and concluded that both automatic and more controlled learning
processes can be identified in incidental serial learning tasks. Additionally,
Wierzcho, Gaillard, Asanowicz, and Cleeremans (2012) tried to distill implicit learning effects by employing a highly
demanding - and novel type of - dual task setting that impairs explicit learning;
and indeed, serial learning is still observed when attentional resources are
strongly occupied by a secondary task. The strong focus on implicit learning,
however, should neither take away interest from its explicit counterpart, nor from
our looking for novel differences between implicit and explicit learning. Dale,
Duran, and Morehead (2012) showed that
predictive behavior emerges very early in serial production, but that its
development across training strongly co-varies with explicit recall of the
underlying regularity - and therefore is mainly characteristic of explicit learning.
Schwager, Rünger, Gaschler, and Frensch (2012) contrasted two theoretical accounts for the development of
explicit knowledge in an incidental learning task: gradually increasing
representation strength and the observation of unexpected events that trigger an
intentional search. Their results supported the unexpected-event hypothesis.2. What type of information is implicit serial learning based
on?There is growing consensus that serial learning can rely on both perceptual and
response-based regularities. Which is dominant probably depends on the specific
learning conditions. This means that we have to understand these conditions. Kirsch
and Hoffmann (2012) contribute to this
understanding: They explored the effect of manipulating spatial stimulus
configuration and observed that such seemingly unimportant task features can
modulate the balance between perceptual and response-based learning. Abrahamse, Van
der Lubbe, Verwey, Szumska, and Jaśkowski (2012) tried to exploit the accepted notion that implicit learning can be
perceptual in nature, and, inspired by the typical perceptual richness of everyday
life, explored the effect of the availability of multiple congruent sources of
stimulation on implicit learning. This sensory redundancy did not benefit
learning.3. What type of general learning process underlies implicit serial
learning?Several general learning mechanisms can be proposed to be at the heart of implicit
serial learning. To name a few, consider (a) the formation of associations between
successive items, (b) the formation of associations between an item and its position
within the full “list” of items (i.e., ordinal structure learning),
(c) the formation of a representation of specific series of successive items (i.e.,
fragmentation or chunking), and (d) the formation of an abstract rule. To complicate
matters, implicit serial learning may comprise multiple mechanisms, possibly
depending on the specific learning conditions. Both the studies of Franco and
Destrebecqz (2012) and of Schuck, Gaschler,
and Frensch (2012) showed the latter to be
indeed the case. Together they found support for the existence of options (a), (b)
and (c), depending on task characteristics (e.g., the saliency of fragments) and the
actual moment in training (e.g., early vs. late).Against the background of these complex questions, ensuring effective and valuable
progression of theory on serial learning needs continuous and careful contemplation
on the research in this domain and its key challenges. First, at a regular basis,
researchers in the field need to summarize and review findings on a specific topic
and from different laboratories in order to maintain an overview and to spur
theoretical progress. The current special issue offers two such high quality
reviews: Gheysen and Fias (2012) discuss and
evaluate how to best dissociate and characterize different serial learning systems
in the brain, whereas Schwarb and Schumacher (2012) outline how they believe that response selection determines the
locus of serial learning in general - thereby integrating a set of seemingly
conflictive findings in the literature. Both these reviews offer a broad approach to
serial learning, which brings about a refreshing link to other concepts and
theories. Second, the field needs to guard its link to reality. The serial learning
paradigm can nicely be defended from an applied point of view, but questions that
are being pursued now seem to arise mostly from the experimental paradigm - and are
no longer inspired by the real world. This concern at first may seem odd - in fact,
there are real brains out there in real laboratories with real computers and
keyboards, and these brains are really learning new skills during our experiments.
What else could we wish for? The answer is - of course - generalizability of
findings to (more) natural situations and everyday behavior. In this sense, papers
such as that of Norman and Price (2012)
cannot be applauded enough. They make the intriguing claim that detecting and
utilizing regularity in social interaction - “out there” - may be more
implicit than one would estimate from lab experiments, possibly due to the overall
greater complexity and richness of stimulus inputs and action repertoires in the
real world. Finally, it is necessary to remain aware of the methodological
challenges, and not to bury these out of desperation. In the current special issue,
the interested reader can find a multitude of (novel) methodologies that aim to
tackle the most complicated issues in (implicit) sequence learning. How can we
disentangle implicit and explicit influences on performance (Mong et al., 2012)? Should we mind differences in baseline
reaction times when we compare different training and/or test conditions (Abrahamse et al., 2012)? What task should we use
as a secondary task in order to best manipulate attentional resources (Wierzcho et al., 2012)?This special issue, I believe, will be a valuable contribution to the field of serial
learning, both through its theoretical and methodological advancements. From my
position as guest editor, I would like to thank all the authors and reviewers for
their wonderful contributions to this special issue. However, without wanting to
divert attention from these, there were some particularly sad events during the
realization of this special issue that need to be mentioned here - in a sense they
overshadow this special issue.First, Prof. Dr. Piotr Jaśkowski, founder and first chief-editor of Advances
in Cognitive Psychology, passed away last year. Prof. Dr. Jaśkowski
(1957-2011) was affiliated with the University of Finance and Management in Warsaw
(Poland) from 2003 onwards, and during his career conducted research on a wide
variety of topics such as visual attention, temporal order judgment, consciousness,
hemispheric asymmetry, and many other (a summary of some of his many contributions
can be found in Gut & Dalla Bella, 2011).
Personally, I experienced Piotr as a supervisor and a friend during my stay as a
researcher at the University of Finance and Management in 2008, and it was then that
the idea for a special issue on implicit serial learning first arose. Piotr
co-authored our contribution to this issue (Abrahamse
et al., 2012).Second, I would like to take a moment to remember Dr. David McCabe, who passed away
in the same year. Dave (1969-2011) was affiliated with Colorado State University
(Fort Collins, US) from 2006 onwards, and was a co-author for this special issue
(Mong et al., 2012). He was best known
for his research on human memory, including topics such as working memory, memory
and aging, and many more (a summary of some of his many contributions can be found
in Castel, Rhodes, Geraci, Parks, & Logan,
2011). Both Piotr and Dave will be sorely missed by the researchers in
their fields, and this special issue is dedicated to them.