Running head : INSIGHT AND STRATEGY IN MULTICUE LEARNING Insight and Strategy in Multiple Cue Learning

In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous studies claim that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments we re-examined these conclusions by introducing novel measures of task knowledge and self-insight, and using ‘rolling regression’ methods to analyze individual learning. Participants successfully learned a four-cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment. Insight and strategy 3 Insight and Strategy in Multiple Cue Learning A fundamental goal of cognition is to predict the future on the basis of past experience. In an uncertain world this involves learning about the probabilistic relations that hold between the available information and an outcome of interest, and integrating this into a singular judgment. Thus a stock-broker draws on various market indicators to predict the future price of a share, and a race-course pundit uses factors such as form and track condition to pick the likely winner of a race. The underlying structure of this kind of prediction is captured in the multiple cue learning framework (Brunswik, 1943; Hammond, 1955), which focuses on how people learn from repeated exposure to probabilistic information. This paradigm, also known as probabilistic category learning, has been applied in numerous areas of psychology, including human judgment (Brehmer, 1979; Doherty & Kurz, 1996; Klayman, 1988; for a review see Goldstein, 2004), learning and memory (Gluck & Bower, 1988; Knowlton, Squire & Gluck, 1994; Shanks, 1990), neuroscience (Ashby & Ell, 2001; Poldrack et al., 2001) and social cognition (Gavanski & Hoffman, 1986; Rappoport & Summers, 1973). A prototypical experimental task is the weather prediction task (Knowlton et al., 1994). In this task people learn to predict a binary outcome (rainy or fine weather) on the basis of four binary cues (four distinct tarot cards, see Figure 1). Each card is associated with the outcome with a different probability, and these combine to determine the probability of the outcome for any particular pattern of cards. The trials in a task are made up of a representative sampling of possible card combinations. On each trial participants see a specific pattern of cards, predict the weather, and receive feedback as to the correct outcome. This enables them to gradually learn the cue-outcome relations, and thus improve the accuracy of their predictions. Insight and strategy 4 There has been extensive research on the conditions under which people are able to master such tasks (Brehmer, 1980; Doherty & Balzer, 1988; Klayman, 1988). The main findings are that people perform well when the cues are few in number and linearly related to the outcome, when the content of the problem is meaningful, and when they receive sufficient trials and appropriate feedback. An important question that has received less attention concerns the relation between people’s performance and their knowledge of what they are doing. The Apparent Dissociation of Learning and Insight An intriguing finding emerging from recent studies is that even when people perform well in such tasks, they seem to lack insight into how they achieve this (Evans, Clibbens, Cattani, Harris, & Dennis, 2003; Gluck, Shohamy & Myers, 2002; Harries, Evans & Dennis, 2000; Wigton, 1996; York, Doherty & Kamouri, 1987). This is illustrated in Gluck et al.’s (2002) study with the weather prediction task (hereafter WP task). They found that although participants attained high levels of predictive accuracy they demonstrated little explicit knowledge about what they were doing. In particular, in questionnaires administered at the end of the task they gave inaccurate estimates of the cue-outcome probabilities, and there was little correspondence between self-reports about how they were learning the task and their actual task performance. This apparent dissociation between learning and insight has been taken as evidence for two separate learning systems (Ashby et al., 2003; Knowlton et al., 1996; Reber & Squire, 1999; Squire, 1994): (i) an implicit (or procedural) system that operates in the absence of awareness or conscious control, and is inaccessible to self-report; (ii) an explicit (or declarative) system that requires awareness and involves analytic processing. Tasks like the WP task, which require the gradual learning and integration of probabilistic information, are Insight and strategy 5 generally considered to involve (i). The lack of self-insight on this task is thus explained by the operation of an implicit system to which participants lack access. It is also argued that these two systems are subserved by distinct brain regions that can be differentially impaired (Ashby et al., 2003; Knowlton et al., 1996; Poldrack et al., 2001). Thus the WP task has been used to reveal a distinctive pattern of dissociations amongst patient populations. For example, Parkinson’s disease patients with damage to the basal ganglia show impaired learning on the task, despite maintaining good explicit memory about task features (Knowlton, Mangels & Squire, 1996). In contrast, amnesic patients with damage to the medial temporal lobes appear to show normal learning but poor declarative memory of the task (Knowlton et al., 1996; Reber, Knowlton & Squire, 1996). If correct, these conclusions have wide repercussions for everyday reasoning, and for the understanding and treatment of patients with neurological damage. However, there are several reasons to be cautious about this dual-process framework. In this paper we will focus on two fundamental issues: the measurement of insight and the analysis of individual learning. The Measurement of Insight It is important to distinguish between a learner’s insight into the structure of a task (task knowledge) and their insight into their own judgmental processes (self-insight). In the case of the WP task, this translates into the difference between a learner’s knowledge of the objective cue-outcome associations, and their knowledge of how they are using the cues to predict the outcome. And there is no guarantee that the two coincide. Someone might have an incorrect model of the task structure, but an accurate model of their own judgment process. Politicians seem particularly prone to this. Previous research tends to run the two together. Thus it is not always clear whether claims about the dissociation between insight and learning refer to a dissociation between Insight and strategy 6 self-insight and learning, task knowledge and learning, or both. Further, this conflation can infect the explicit tests given to participants. Questions that are designed to tap someone’s insight into their own judgment processes may instead be answered in terms of their knowledge about the task. Such confusion needs to be avoided if firm conclusions are to be drawn about the relation between learning and insight. Insensitivity of Explicit Tests There are several other problems with the explicit tests commonly used to measure task knowledge and self-insight. First, these measures tend to be retrospective, asked after participants have completed numerous trials, and this can distort the validity of their assessments. The reliance on memory, possibly averaged across many trials, can make it difficult to recall a unique judgment strategy. This is especially problematic if people’s strategies have varied during the course of the task, making it hard if not impossible to summarize in one global response. In general it is better to get multiple subjective assessments as close as possible to the actual moments of judgment (cf. Ericsson & Simon, 1980; Harries & Harvey, 2000; Lovibond & Shanks, 2002). Second, explicit tests often require verbalization, but this can also underestimate task knowledge because participants may know what they are doing but be unable to put this into words. This is particularly likely in probabilistic tasks, where natural language may not be well adapted to the nuances of probabilistic inference. A third problem, prevalent in the neuropsychological literature, is that explicit tests are often too vague (Lovibond & Shanks, 2002). Rather than focus on specific task-relevant features they include general questions that are tangential to solving the task (e.g., questions about location of cards on screen). Once again this reduces the sensitivity of the test to measure people’s relevant knowledge or insight. This can lead to an over-estimation of Insight and strategy 7 insight (e.g., someone may be able recall features of the task irrelevant to good task performance) or to under-estimation (e.g., the questions fail to ask about critical information). The studies in this paper seek to improve the sensitivity of explicit tests on all these counts. Separate explicit tests will be used for task knowledge and self-insight, and both will involve specific questions of direct relevance to the task. In the case of task knowledge these will concern probability ratings about cue-outcome relations, in the case of self-insight subjective ratings about cue usage. Such ratings-based tests will avoid any problem of verbalization. To tackle the problem of retrospective assessments we will take multiple judgments during the course of the task (either blocked or trial-by-trial). Analyses of Judgment and Learning Claims about the dissociation between insight and learning also depend on an appropriate analysis of learning performance. In tasks with