Two main theories of learning have been evaluated in cognitive psychology and motor control theory to describe skill acquisition: rule- and instance-based learning. During rule-based learning, abstraction of relevant features of a stimulus is encoded, which generates a generalized, summary memory representation. Untrained stimuli sharing the same relevant features, or set of rules, are considered to be within the same class. Transfer effects are uniform across a class of behaviors as all stimuli rely on the same relevant features to direct transfer. Motor class membership is a critical variable for rule-based learning, and provides a mechanism to describe when transfer will discontinue for a given set of behaviors (i.e., poor transfer effects for outside- class behaviors).
In contrast, during instance-based learning all features (both relevant and irrelevant) are encoded into an instance representation. Thus, no particular feature of a stimulus is considered “more relevant” than another, and no underlying rules about features are generated. Transfer effects are dictated by the similarity of features between trained and untrained stimuli in a linear direction, i.e., decreased similarity between stimuli will result in decreased transfer effects. There are no assumed class effects with instance-based learning as no rules are abstracted during encoding. These theories, and their associated parameters, are contrasted in Table 2.
Table 2: Rule- and instance-based learning contrasts
Rule-based Learning Instance-based Learning Type of representation Summary, abstracted Exemplar
Information discarded Yes No
Type of Transfer Generalized Item-specific
Direction of Transfer Uniform pattern Varies with similarity Effect of Class on Transfer Yes; transfer only within class No effect
Memory process Encoding most important Retrieval most important
Rule-based learning, specifically motor program theory, is the dominant learning theory in limb and speech motor control models. Multiple motor program units have been postulated for speech production. Research evaluating smaller units, e.g., the phoneme, provides inconsistent evidence for motor program theory and the uniform transfer effects associated with rule-based learning (e.g., Austermann-Hula et al., 2008; Ballard et al., 1999; Knock et al., 2000). It is unclear from the literature if learning a larger motor program unit would result in rule-based transfer, or if rule-based learning theory is inappropriate in describing motor learning effects. As noted in Chapter 2, larger speech units (e.g., syllable) share characteristics with motor program theory and are rule-governed (Aichert & Ziegler, 2004; Cholin et al., 2006; Levelt et al., 1999; Maas & Mailend, 2012). In English, the frequency of occurrence for syllable stress position influences the response time in recognition and articulation of syllables and words. High- frequency syllables are responded to more quickly than low-frequency syllables, which has been postulated as a difference in memory representation for each frequency type (Aichert & Ziegler, 2004; Cholin et al., 2006; Cholin & Levelt, 2009; Laganaro, 2005, 2008; Staiger & Ziegler, 2008). Thus, differences in syllable stress frequency of occurrence may provide a viable class marker for stored motor programs of high- versus low-frequency stressed syllables.
Instance-based learning is rarely attributed to motor transfer effects despite evidence consistent with instance-based transfer. Evaluation of instance-based learning in limb studies is restricted to arm reaching and typing, and has yet to be investigated as a theoretical alternative to rule-based learning in speech motor control. Instance representation units in speech production may be similar to motor programs (e.g., phoneme, syllable, word). However, motor investigators within instance-based learning theory debate the theoretical size of motor representations (e.g., vector versus bigram). Small units of speech motor control, such as phonemes, may be an appropriate representation to investigate in initial examinations of instance-based learning for several reasons. These smaller units may be contrasted to larger proposed rule-based motor programs (e.g., syllable) in a direct comparison of rule- and instance-based learning in a speech motor control framework. Dissociation between syllable and phoneme levels of speech motor control suggests two different memory representations may be operating, and motor programs for syllable stress may dictate speech programming and execution of lower-level memory representations (i.e., phonemes). Stress patterns of trained words predict novel stress patterns in untrained words and nonwords that are controlled for shared phonemes and phonological neighborhoods (as reviewed in Guion et al., 2003). Syllable boundaries are maintained during disordered speech of individuals with AOS, with the clinical signs of consonant cluster reductions and substitutions occurring in non-syllable boundaries (i.e., onset or coda within a syllable; e.g., Aichert & Ziegler, 2004; Laganaro, 2005). Additionally, phonemes as an instance- based representation provide a unit of analysis which can be systematically manipulated based on similarity in a variety of word-like contexts. Adoption of Masson’s (1986) methods within a speech motor control framework (much like Crump and Logan (2010) adopted these methods for
typing) would permit investigation of the effect of phonetic similarity of words (or nonwords) across different trained phonemes.
In Chapter 4, experiments conducted by Chamberlin and Magill (1992b) and Crump and Logan (2010) were critically reviewed. These authors were pioneers in motor control theory for evaluating rule- versus instance-based learning predictions. However, their experimental manipulations were not conducive to assessing both types of memory representations, which may have biased their results. Additionally, the assessments used by these investigators to probe memory representation were invalid, as these measurements assessed the levels of response programming and/or execution of the information processing model of motor control. Thus, within motor control theory it continues to remain undetermined which memory representation directs transfer effects, and/or how these two representations may interact with one another.
The literature review provided in this dissertation indicates more research in the area of learning theory is needed to explain the inconsistent rule-based learning effects noted in speech motor therapy (e.g., AOS treatment), as well as describe why transfer effects appear to be driven by similarity. Historically, the explanation for these results has been to propose a different motor program and continue utilizing a rule-based framework. Although an instance-based theory may be more consistent with these results and more parsimonious, this avenue of theoretical exploration has not been attempted in speech motor control theory. Thus, the proposed evaluation in this dissertation, to contrast rule- and instance-based learning within a speech motor control framework, is innovative.
There are several parameters that could be contrasted between these two theories (see Table 2); however, transfer pattern and motor class are the most prominent and distinguishing parameters. Historically, the distinct transfer patterns noted with each theory have led
investigators to postulate the underlying memory representation leading to these transfer patterns (Chamberlin & Magill, 1992a; Shanks, 1995). Evaluating transfer patterns of untrained stimuli that vary on parameters of similarity (associated with instance-based learning) and motor class (associated with rule-based learning) would allow for evaluation of both theoretical transfer patterns. Additionally, selecting methods that specifically target the response selection stage of motor control models (versus the response programming and/or execution stages) would provide a purer estimate of the memory representations being selected for transfer. These manipulations are described in Chapter 7, and attempt to overcome the flaws present in the early examinations of these theories by Chamberlin and Magill (1992b) and Crump and Logan (2010). The main experiment described in the upcoming chapters will provide an initial theoretical evaluation of these two learning frameworks in speech motor control theory.