Implicit Learning : History & Applications

The history of research on implicit learning has been driven primarily by studies using specialized laboratory tasks that are designed to isolate our ability to extract statistical structure from experience, outside of awareness of what is learned. This empirical approach has been fruitful and necessary to establish implicit learning phenomena. It has allowed for systematic characterization of the mechanisms and neural systems that are the basis of this type of memory. However, the question of how pervasive implicit learning is in our everyday human experience is not directly addressed by this laboratory-based approach. The fact that implicit learning does not leave a conscious memory trace runs the risk of it being overlooked as an important component of complex cognition. Here, applications of implicit learning in three established research domains are briefly reviewed as examples of phenomena that appear to be driven by knowledge outside of consciousness. This review will show how this type of memory plays important roles in domains as diverse as language learning, skill acquisition and decision making processes. The principles extracted from implicit learning research can provide important theoretical contributions to these other domains and points towards the importance of examining relatively unexplored questions about the nature and processes involved in interactions between memory systems in complex cognitive processing. Introduction & History The term “implicit learning” was first published 50 years old in a report titled, “Implicit Learning of Artificial Grammars” (A.S. Reber, 1967). This paper described studies with a novel paradigm aimed to create a laboratory analogue of language learning. The new approach was based on using mathematical formalisms for stimulus creation that were similar to ones being developed to help understand human language function. Surprisingly, participants exhibited an unusual behavioral pattern in their learning process. They appeared to be learning to be sensitive to the statistical structure of the underlying formalism, but seemingly without any awareness that there were any underlying rules. This report established the possibility of a dissociation between learning that could only exhibited through performance and more traditional learning and memory that was available to conscious awareness. Over the next several decades a wide variety of additional studies and many more novel paradigms were constructed to drive research into understanding the phenomenon of implicit learning (A.S. Reber, 1989; P.J. Reber, 2013). The historical context in which the original report was published provides some insight into why this finding had such a widespread, enduring impact and how the idea of implicit learning came to be foundational to the modern characterization of Memory Systems Theory (Squire, 1992) and the cognitive neuroscience of memory. The story of the basis of the original studies (A.S. Reber, personal communication) starts with a chance meeting between the author, Arthur S. Reber, and George A. Miller in the early 1960s. Miller had fairly recently published the seminal paper on “the magic number 7” and working memory (Miller, 1956) which is often cited as one of the core reports demarking the shift in the field of Psychology away from Behaviorism and to Cognitive Psychology, known as the Cognitive Revolution. Other notable publications also considered in the same vein include Broadbent (1958), Newell, Shaw & Simon (1958)​ ​and a review written by Chomsky (1959) highly critical of a book by B.F. Skinner (1957) titled “Verbal Behavior.” The Cognitive Revolution was effectively a movement against and away from the Behaviorist school that had attempted to put Psychology on robust scientific footing through the use of simple, well-characterized task with quantifiable measures that allowed for robust, reliable experimental paradigms. In practice, this meant using tasks from the tradition of physiologists (e.g., Pavlov’s conditioning research) that could also be studied in animal models. However, the extrapolation from animal cognition to human cognition has always posed some difficult questions, in particular when considering complex human cognition and especially the process of language, which is effectively unique to humans. Skinner’s suggestion (1957) that language could be explained from reinforcement and conditioning studies was forcefully rejected by Chomsky (1959), implying that the study of human cognition needed a different approach. The new approach favored by Chomsky led to his seminal work developing the field of computational linguistics. Early explorations of this work appeared in the Handbook of Mathematical Psychology (1963; ed. R.D. Luce) which includes three chapters authored or co-authored by Chomsky outlining how language production and comprehension might be modeled with formal grammars. Two of these chapters were co-authored with Miller, which provides some context for how Reber, as a graduate student at nearby Brown University, came into contact these formalisms through Miller (at Harvard) via their occasional interactions. While Chomsky’s research program can be seen as characterizing mathematical formalisms that would account for human language production and comprehension, Miller and Reber were considering a separate but related problem. If these grammars were how humans accomplished language, how does a human acquire them? The formalisms seemingly required to account for language use appeared to be exceedingly complex and possibly entirely unlearnable, especially considering the cognitive abilities of newborns. One approach was to assume they were not learned, necessitating the existence of a pre-wired “universal grammar” embedded in the human brain (e.g., genetically endowed). Another approach was to try to capture this learning process in the laboratory using simplified ‘artificial grammars,’ which then led to the seminal finding (A.S. Reber, 1967) and observation of a novel type of human learning that might solve this ‘unlearnability’ problem for language. Researchers familiar with this history are aware that the idea of implicit learning did not immediately revolutionize the study of memory or language. In fact, for much of the next several decades, there followed a great deal of debate centered on the difficult problem of establishing the ‘implicit’ part of this kind of learning. With a definition of implicit learning founded on ‘not available to consciousness,’ establishing even the existence of this phenomenon depends critically on proving a universal null, no awareness, which is an essentially intractable problem (Merikle, 1994). While experimental techniques and measurement approaches eventually began to provide guidelines for tackling this issue(Dienes & Berry, 1997), important support for the concept also emerged from ideas being developed separately and in parallel from research in neuropsychology and neuroscience. Cognitive Neuropsychology and Systems Neuroscience At around the same time as the several famous publications in cognitive psychology that launched the Cognitive Revolution were published, a landmark paper in human cognitive neuropsychology was also reported. Scoville & Milner (1957) described the famous case of the patient H.M., who exhibited severe and selective impairment to his ability to acquire new conscious memories after bilateral medial temporal lobe (MTL) removal to treat otherwise intractable epilepsy. While there had been a few prior reports of selective cognitive loss following localized brain regions (Broca, Wernicke) the theoretical model of the time was dominated by Lashley’s (1929) theory of equipotentiality that hypothesized that any region of the brain could support high-level cognitive function. The case of patient H.M. established that memory was dependent on a specific neural region and did not arise from mass action of neural changes across the entire brain. Research over the next 35 years characterized the structure and function of the memory circuitry within the MTL (hippocampus and adjacent cortical areas) and established that this system was critical for the acquisition and consolidation of memories for facts and event (Squire, 1992). Patients with damage similar to H.M. are unable to acquire new explicit memories, but are able to retrieve remote episodic memories of events that occurred prior to the damage to the MTL. More recent memories are partially affected by a temporal gradient of retrograde amnesia (see Lechner et al., 1999, for a review and history), leading to the development of a theory of memory consolidation dependent on a gradual process of memory strengthening and reorganization that depends on the MTL after initial learning. However, detailed neuropsychological assessment of H.M.’s memory capabilities subsequently indicated that not all learning processes in his brain were entirely disrupted. Corkin (1968) and Milner, Corkin, Teuber (1968) documented improvements in performance in procedural tasks (mirror tracing), maze learning, and picture identification from fragments. Shortly after, Weiskrantz & Warrington (1970) described a broader phenomenon of intact memory from fragmentary information in amnesic patients (priming) that would come to be known as “implicit memory” and very widely studied (Schacter, 1987). Together these findings indicated that another type of memory existed that did not operate in the same manner as memory for new facts and events that depended on the MTL memory system. These findings were foundational to the development of a ‘memory systems framework’ that aimed to connect these observations about human memory to research going on in parallel on the neuroscience of memory. The field of neuroscience also progressed remarkably over the course of the 20th century (c.f. Gross 1999 for a highly readble overview) with a notable moment in this progression being the fou