Predicting the future: From implicit learning to consolidation

Abstract

Sequence learning can be differentiated according to phases (rapid and slower), modalities (perceptual and motor), and whether or not it is conscious (implicit and explicit). Implicit sequence learning occurs when information is acquired from an environment of complex stimuli without conscious access either to what was learned or to the fact that learning occurred. In everyday life, this learning mechanism is crucial for adapting to the environment and for predicting events unconsciously. Implicit sequence learning underlies not only motor, but also cognitive and social skills; it is therefore an important aspect of life from infancy to old age. Moreover, this kind of learning does not occur only during practice, in the so-called online periods, but also between practice periods, during the so-called offline periods. The process that occurs during the offline periods is referred to as consolidation, which denotes the stabilization of a memory trace after the initial acquisition; this can result in increased resistance to interference or even improvement in performance following an offline period. Understanding the multiple aspects and influencing factors of consolidation can help us to reveal the nature of memory and changes in brain plasticity. Our review focuses on how consolidation varies with factors such as awareness, the length of offline periods, the type of information to be learned, and the age of participants. We highlight that consolidation is not a single process; instead, there are multiple mechanisms in the offline period, which are differently influenced by these factors.

Introduction

Prediction is one of the most fundamental functions of the brain. During every moment of our waking life, the brain is trying to anticipate future sensations. In order to recognize time-based patterns and predict subsequent events, storing and recalling of sequences are required (Hawkins et al., 2009). Without these skills, it would be impossible to carry out evolutionary adaptive behaviors. Most predictions are based on the implicit learning that occurs when information is acquired from an environment of complex stimuli, without conscious access either to what was learned or to the fact that learning occurred (Cleeremans et al., 1998, Reber, 1993). Despite the growing interest in implicit learning in the past decades, there has been relatively little research on offline processing of implicitly learned information (i.e., consolidation). Here, we review recent work on implicit sequence learning and its consolidation, with an emphasis on the last 10 years. More selectively, we address four of the most important factors that influence the consolidation of this fundamental learning mechanism. These factors must be taken into consideration before planning and performing brain imaging, psychophysiological, and behavioral studies on sequence learning and its consolidation.

Implicit sequence learning underlies not only motor, but also cognitive and social skills (Kaufman et al., 2010, Lieberman, 2000, Nemeth et al., 2011, Romano Bergstrom et al., in press, Ullman, 2004); it is therefore an important aspect of life from infancy to old age. Implicit sequence learning is essential for learning languages, as well as learning to operate appliances, computer applications or musical instruments (Howard et al., 2004, Romano et al., 2010). Social skills appear in compound behaviors (including series of perceptions, emotions as well as motor actions) realized in proper sequences and under appropriate circumstances. These skills – for example, dialogs, decision making in social context, communication of emotions, predicting others' behavior based on previous verbal and nonverbal social communication, and adjusting our own behavior based on these predictions – are needed for normal social functioning in various sorts of situations: in the workplace, in the family, in the neighborhood, during recreation, shopping, or in the context of medical and mental care (Heerey and Velani, 2010, Lieberman, 2000, Nemeth and Janacsek, 2011). Furthermore, these skills are crucial for effective participation in educational, training, and rehabilitation programs, for instance in relearning how to walk, reach for objects, and speak after brain injury (Howard et al., 2004, Nemeth et al., 2010a).

Most models and empirical studies of skill learning highlight the role of the basal ganglia and the cerebellum (Dennis and Cabeza, 2011, Doyon et al., 2009a, Hikosaka et al., 1999, Hikosaka et al., 2002, Keele et al., 2003, Kincses et al., 2008, Rieckmann et al., 2010, Sefcsik et al., 2009); in contrast, the role of the hippocampus remains inconclusive (Albouy et al., 2008, Schendan et al., 2003). A major approach to this research is through brain imaging and neuropsychological studies; in addition to these, experiments investigating the effects of pharmacological agents provide an opportunity for the better understanding of the biological background of implicit learning (for review see Uddén et al., 2010). For example, a study by Frank et al. (2006) showed that the benzodiazepine midazolam, which inactivates the hippocampus, causes explicit memory deficits in healthy participants, but enhances implicit learning. In contrast, a more recent study found impaired implicit learning after the exogenous administration of the stress hormone cortisol (Römer et al., 2011). The engagement of specific brain structures in these phenomena needs to be clarified.

In experimental settings, implicit learning is defined as the acquisition of co-occurrence/dependencies between stimuli or trials, and is expressed only through performance (Frensch, 1998, Howard et al., 2004, Rieckmann and Bäckman, 2009). In the past decades, several tasks have been developed to tap into implicit learning. These tasks can be organized into two main groups based on whether the covariation or the temporal sequence of stimuli has predictive information. For example, in artificial grammar learning, participants are exposed to strings of letters. They are not informed that the strings follow a set of rules; yet, it has been found that they can apply these rules at a later stage of practice (Dienes et al., 1991, Reber, 1989). In the weather prediction task, individuals have to decide whether a specific combination of cards predicts rainy or sunny weather. They are unaware that each combination of cards is probabilistically related to a particular weather outcome. During the task, participants learn gradually which of two outcomes will occur, although they have no conscious knowledge of the rule (Gluck et al., 2002, Kemény and Lukács, 2009, Kincses et al., 2004, Poldrack and Rodriguez, 2004). Similarly, in the contextual cueing task, the global configuration of a display cues the location of a search target (Chun and Jiang, 1998, Howard et al., 2006).

In these tasks, the covariation of certain stimuli (e.g., in a letter string/a set of cards) has predictive information, in contrast to sequence learning tasks, where participants have to predict the onset of a stimulus based on the preceding stimuli (Rieckmann and Bäckman, 2009). Evidence suggests that the latter type of task has partly different underlying mechanisms and activates partly different brain structures (Greene et al., 2007, Jimenez and Vázquez, 2011, Poldrack et al., 2005); therefore, it is important to differentiate between these two types of tasks. In recent years, a growing body of data has emerged regarding the consolidation of implicit sequence learning, while covariation learning has received less attention. In our review, we focus on the perceptual–motor learning of sequences. First, we describe the sequence learning tasks in more details. We follow this with a discussion of the consolidation processes and its potentially influencing factors. Finally, we consider developing questions and future directions on this research field.