Non-declarative memory and learning

A definition of non-declarative learning is not straightforward, as different avenues of research define and use terminology in different ways. Firstly, the multiple memory systems account refers to the procedural memory system, which is the focus of this thesis, as only one type of implicit memory system. It is the system which regulates the acquisition, consolidation and automization of both motor and cognitive skills and habits (Squire, 2004) and that is required for performance of skilled motor actions, such as bike-riding and the perceptual-cognitive skills that make the fluent use of language possible (Ullman & Pierpont, 2005). Priming (as well as conditioning and non-associative implicit memory) is considered as a separate implicit memory system according to the multiple memory systems view. However, it is generally agreed that the terms procedural learning and implicit learning are largely synonymous (Shanks, 2005; Berry & Dienes, 1993) or at least overlapping (Seger, 1994). A task is learned implicitly if procedural knowledge develops without, or at least before, any declarative knowledge and it is the procedural system which is necessary in order to perform implicit tasks (Berry & Dienes, 1991). In what follows implicit learning and procedural learning will be used interchangeably, as will explicit and declarative learning.

Secondly, a distinction can be made between research into implicit memory and into implicit learning that echoes the distinction between the procedural and priming pathways of the multiple memory systems taxonomy. Berry and Dienes (1991) refer to how little cross-referencing there is between these two research traditions, leading to a frequent supposition that they refer to very different things. Implicit memory has traditionally been investigated using priming tasks, such as word stem completion, while implicit learning research uses paradigms that will be the focus of this thesis, such as the serial reaction time and Hebb serial order learning tasks, artificial grammar learning, probabilistic categorization and contextual cueing (see Chapter 3 for a detailed explanation of these paradigms). The two research traditions are separated by

43 their experimental approach. However it can be argued that both fields are investigating similar distinctions and that the same cognitive processes underlie performance on the paradigms in both traditions (Reber, 2008).

Frensch & Runger (2003) refer to there being at least a dozen different definitions of implicit learning, most of which benefit from being given in relation to explicit learning. At the most surface level the distinction is made between implicit and explicit learning as learning without or with awareness respectively. Reber, Walkenfield and Hernstadt (1991) also focus on a dissociation from awareness as being the crucial factor in distinguishing between the two, such that implicit knowledge is acquired without awareness of both the learning process and the information learned. However, defining implicit learning only in terms of what it lacks does not give us a full understanding of how it differs from explicit learning (Reber, 2013). Other characteristic features of implicit learning distinguish it from explicit learning. The knowledge acquired in implicit learning is difficult to access; frequently combines with a subjective sense of intuition; is associated with incidental learning conditions; is robust to decay and interference; is rigid and is subject to considerable specificity of transfer, so it can typically only be applied within the specific circumstances in which it was learned (Berry & Dienes, 1993; Reber, 1993). In the case of procedural implicit learning it is also slow to develop, as it gradually extracts the common elements from strings of separate events (Reber, 2013). All these features are seen as in opposition to the characteristics of explicit learning, which uses deliberate strategies; is accessible to consciousness; is flexible and, once learned, can be applied in various ways; and can be expressed on demand.

2.1.3.1 The neural substrates of implicit learning

The procedural memory system is made up of a network of several interconnected brain structures – the cortico-striatal-pallidal-thalamo-cortical circuitry system (Seger & Miller, 2010; Squire, 2004). The basal ganglia are arguably the hub of this network and are a distributed set of sub-cortical structures that include the globus pallidus and the striatum (itself divided into the caudate nucleus and putamen), as well as the more distant, but connected subthalamic nucleus and sustantia nigra. Within the system the

44 caudate and putamen are the main input nuclei from the rest of the cortex (Grahn et al., 2009). The system is split into dorsal and ventral streams. The former is connected primarily via the caudate (Grahn et al., 2009) and is particularly implicated in learning and memory (Packard & Knowlton, 2002). The latter loop, connected to sensory and motor areas via the posterior putamen, appears to be more specialized for motor learning (Grahn et al., 2009). Neurobehavioural experiments in animals and humans have shown this area to mediate the learning of incrementally acquired stimulus- response associations, probabilistic rule learning (Knowlton, Mangels, & Squire, 1996; Packard, Hirsch, & White, 1989) and sequence learning (Doyon et al., 1997) and working memory (Wise, Murray, & Gerfen, 1996) among other processes.

The cortico-striatal circuitry of the procedural memory system has been divided into four striatal loops with different cognitive specifications during learning, associated with different cortical connections (Seger, 2006). The executive loop links the anterior caudate and the prefrontal cortex; the visual loop links the posterior caudate and visual cortex; the motor loop links the putamen and motor cortex; and the motivational loop links the ventral striatum and ventromedial frontal cortex. These loops may be differentially involved depending on the nature of learning required. Seger (2006) suggested that the role of the striatum is to react to the learning context to modulate subsequent cortical processing and in support of this, striatal activation during learning has been demonstrated prior to cortical activity. However, alternative interaction processes have also been put forward (Packard & Knowlton, 2002).

There are other important structures involved in the procedural learning system including the frontal cortex, the pre-motor cortex (which includes the supplementary motor cortex) and Broca’s area. In addition, research has also pointed to the involvement of the cerebellum in both motor and non-motor implicit learning. Sequence processing is at the heart of cerebellar function (Leggio et al., 2008) and it is suggested that the cerebellum is involved in the prediction of sequences based on the comparison of incoming sequences of stimuli across multiple cognitive domains. As such, it must work in tandem with working memory in order to maintain this information for comparison.

45 To recap, the multiple memory systems view divides memory into separate systems. The conscious, declarative memory system is associated with the medio- temporal lobe, incuding the hippocampus, and is responsible for the encoding and storage of semantic facts and memory for events. The procedural memory system operates without consciousness and is involved in the learning of motor and cognitive skills and habits. The neural substrate of procedural memory is the cortico-striatal system, which includes the basal ganglia and cerebellum.