Multiple Systems : Part 2 1 Multiple Systems of Perceptual Category Learning : Neuropsychological Tests

There is widespread agreement that multiple qualitatively different category learning systems mediate the learning of different category structures. Two systems that have received support are (1) a frontal-based explicit system that uses logical reasoning, depends on working memory and executive attention, and is mediated primarily by the anterior cingulated, the prefrontal cortex, and the head of the caudate, and (2) a basal ganglia-mediated implicit system that uses procedural learning and requires a dopamine reward signal. This chapter reviews a large body of work conducted in our laboratories that examines the details of the two proposed systems using neurological patients as experimental participants. Collectively the studies suggest little involvement of the medial temporal lobes in category learning with large categories. They also suggest that, in striatal-damaged patients, the need to ignore irrelevant information is predictive of a rule-based category learning deficit, whereas the complexity of the rule is predictive of an information-integration category learning deficit. Introduction Category learning involves laying down a memory trace that can be used to improve the efficiency (i.e., accuracy and speed) of responding. It is now widely accepted that mammals have multiple memory systems (Schacter, 1987; Squire, 1992), and this fact alone makes it reasonable to postulate that multiple category learning systems might also exist. This chapter reviews a body of work that suggests that perceptual category learning is characterized by multiple systems each of which involves a set of diverse neuro-cognitive processes. This chapter builds upon the work outlined in the previous chapter by Ashby and Valentin who reviewed a number of studies that tested a priori predictions from a recently proposed neuro-biologically plausible multiple systems theory called the COmpetition between Verbal and Implicit 1 This research was supported in part by National Institute of Health Grant R01 MH59196 to WTM, National Institute of Neurological Disorders and Stroke Grant R01 41372 to JVF, and a James McDonnell Foundation Grant. Correspondence concerning this article should be addressed to W. Todd Maddox, University of Texas, Department of Psychology, Institute for Neuroscience, 1 University Station A8000, University of Texas, Austin, Texas, 78712 (e-mail: maddox@psy.utexas.edu). Multiple Systems: Part 2 2 Systems theory (COVIS; Ashby, Alfonso-Reese, Turken & Waldron, 1998; Ashby & Waldron, 1999; for a review see Maddox & Ashby, 2004). In each study, some experimental manipulation was introduced that was predicted a priori to affect processing in one system, but not the other. All of these studies used healthy young adults as participants. These studies provide a nice foundation, but the next step is to examine the systems in greater detail. One way to achieve this goal is to examine category learning in neurological patients with damage to specific brain areas. The aim of this chapter is to review a body of work conducted in our laboratories that examined category learning in various patient populations. This chapter is organized as follows. In the first (next) section we briefly introduce COVIS and the proposed underlying neurobiology. A more detailed description is offered in the previous chapter and in Maddox and Ashby (2004). The second section reviews briefly the qualitative dissociations introduced in the previous chapter. The third section reviews a body of work that examines category learning in various patient populations. The third section is sub-divided into sections devoted to rule-based and information-integration category in patients with amnesia, Parkinson's disease (PD), or Huntington's disease (HD). The final section offers a brief summary and conclusions. It is important to note that this is not a substantive review of the field. Two excellent reviews are provided by Keri (2003) and Poldrack and Packard (2003). Rather this chapter reviews and integrates a large body of patient work conducted in our and other’s laboratories that takes a systematic empirical approach, supplemented by the application of a series of quantitative models, to the study of perceptual category learning. COmpetition between Verbal and Implicit Systems (COVIS) A growing body of research suggests that the learning of different types of category structures is mediated by different systems with distinct but partially overlapping neuro-biological substrates (Ashby & Ell, 2001, 2002; Erickson & Kruschke, 1998; Pickering, 1997; Maddox & Ashby, 2004; Reber & Squire, 1994; Smith, Patalano, & Jonides, 1998; however see Nosofsky & Johansen, 2000). One of the most successful multiple systems models of category learning, and the only one that specifies the underlying neurobiology, is COVIS. COVIS postulates two systems that compete throughout learning – an explicit, hypothesis-testing system that uses logical reasoning and depends on working memory and executive attention, and a procedural learningbased system that relies more on incremental and feedback-learning processes. One intriguing aspect of the procedural learning-based system is its association with those processes involved in motor performance (e.g., Hazeltine & Ivry, 2002; Willingham, 1998), which leads to the important prediction that categories learned via a procedural learning-based system should have a close link to the motor response. Much of the evidence for multiple category learning systems comes from two different types of categorization tasks. Rule-based category learning tasks are those in which the category structures can be learned via some explicit reasoning process that treats each stimulus dimension separately. Frequently, the rule that maximizes accuracy (i.e., the optimal rule) is easy to describe verbally (Ashby et al, 1998). For example, in Figure 1a, the stimuli (with one presented on each trial) are composed of a single line that varies in length and orientation across trials. Each symbol in Figure 1 denotes the length and orientation of one stimulus. Also shown in Figure 1 are the decision bounds that maximize categorization accuracy. In the rule-based task, the Multiple Systems: Part 2 3 optimal bound requires observers to attend to length and ignore orientation. The vertical bound in Figure 1a corresponds to the rule: “Respond A if the line is short and B if it is long”. Information-integration category learning tasks are those in which accuracy is maximized only if information from two or more stimulus components (or dimensions) is integrated at some pre-decisional stage (Ashby & Gott, 1988). Perceptual integration could take many forms – from treating the stimulus as a Gestalt to computing a weighted linear combination of the dimensional values. In many cases, the optimal rule in information-integration tasks is difficult or impossible to describe verbally (Ashby et al., 1998). The information-integration task in Figure 1b was generated by rotating the rule-based categories by 45°. Notice that the information-integration rule is linear. Figure 1c depicts a case in which the information-integration rule is non-linear. Category structures like these were used in the studies reviewed in the previous chapter and in some of the studies reviewed below. INSERT FIGURE 1 ABOUT HERE COVIS assumes that learning in rule-based tasks is dominated by an explicit system that uses working memory and executive attention to generate and test hypotheses and is mediated primarily by the anterior cingulate, the prefrontal cortex, and the head of the caudate nucleus (see Figure 2 from the previous chapter). Learning in information-integration tasks is dominated by an implicit procedural-learning-based system, which is mediated largely within the tail of the caudate nucleus (see Figure 3 from the previous chapter; Ashby, et al, 1998; Ashby & Ell, 2001; Willingham, 1998). (See the previous chapter for details.) Testing A Priori Predictions from COVIS In this section we briefly review a number of studies that provided empirical tests of a priori predictions derived from the proposed neurobiological underpinnings of COVIS. Because the hypothesis-testing system is under conscious control and has full access to working memory and executive attention, the placement and timing of the feedback signal should not be critical for rule-based category learning because this information can be held consciously in working memory. In contrast, a procedurallearning system that is mediated within the tail of the caudate nucleus would not be accessible to conscious awareness and is far removed from working memory. As a result, it would depend more heavily on the placement and timing of the feedback. As a test of these predictions, rule-based and information-integration category learning was compared across an observational training condition (in which observers were informed before stimulus presentation of what category the ensuing stimulus was from) and a traditional feedback training condition (in which the category label followed the response) (Ashby, Maddox, & Bohil, 2002), and across an immediate feedback condition (in which corrective feedback was provided immediately following the response) and a delayed feedback condition (in which corrective feedback was delayed by 2.5-, 5or 10-seconds following the response) (Maddox, Ashby, & Bohil, 2003). In 2 A conjunction rule (e.g., respond A if the stimulus is small on dimension x and small on dimension y) is a rulebased task rather than an information-integration task because separate decisions are first made about each dimension (e.g., small or large) and then the outcome of these decisions is combined (integration is post-decisional, not pre-decisional). 3 Many argue for the existence of an implicit form of working memory that may not be available to conscious awareness. When we use the term “working memory” we refer to a conscious, verbalizable proc