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Outcome Evaluation and Procedural Knowledge in Implicit Learning

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Outcome Evaluation and Procedural Knowledge in Implicit Learning Outcome Evaluation and Procedural Knowledge in Implicit Learning Danilo Fum ([email protected]) Department of Psychology, University of Trieste Via S.Anastasio 12, I-34134, Trieste, Italy Andrea Stocco ([email protected]) Department of Psychology, University of Trieste Via S.Anastasio 12, I-34134, Trieste, Italy Abstract form. Starting from the 90s, however, it has been suggested that many results could be explained by Although implicit learning has been considered in recent supposing that participants acquire concrete, fragmentary, years as a declarative memory phenomenon, we show that a declarative knowledge of the task represented through procedural model can better elucidate some intriguing and instances. Instance-based knowledge has indeed been unexpected data deriving from experiments carried out with Sugar Factory (Berry & Broadbent, 1984), one of the most shown to underlie or significantly affect performance on popular paradigms in this area, and can account for other artificial grammar learning (Perruchet & Pacteau, 1990), phenomena reported in the literature that are at odds with sequence prediction (Perruchet, 1994) and dynamic current explanations. The core of the model resides in its system control tasks (Dienes & Fahey, 1995; Lebiere, adaptive mechanism of action selection that is related to Wallach & Taatgen, 1998, Taatgen & Wallach, 2002). outcome evaluation. We derived two critical predictions In this paper we will focus on a widely adopted from the model concerning: (a) the role of situational dynamic system control task known as Sugar Factory factors, and (b) the effect of a change in the criterion (Berry & Broadbent, 1984). In this task, henceforth SF, adopted by participants to evaluate the outcome of their participants are asked to imagine themselves in chief of a actions. We tested both predictions with an experiment whose results corroborated the model. We conclude the sugar-producing factory. Their goal is to reach and paper by emphasizing the role played by procedural maintain the production of sugar (P) at a specified target knowledge and evaluation mechanisms in explaining some level by modifying the amount of workers (W) allocated implicit learning phenomena. to the production. Unbeknown to participants, P and W are related by the equation: 2 Introduction Pt = Wt - Pt -1 + e One of the current hot topics in cognitive science is where Pt is the quantity of sugar produced at trial t, just represented by implicit learning, that is the ability to after the allocation of Wt workers, Pt–1 represent the acquire new knowledge even when apparently unaware of amount of sugar produced in the previous trial, and e is a it, or unable to express it. Research on implicit learning, random variable that can assume with equal probability has focussed mostly on three phenomena. First, people one of the values in the set {–1, 0, +1}. The value for both that in a learning phase have experienced a set of W e P ranges from 1 to 12; resulting values of P less than grammatical strings generated from artificial grammar 1 are simply set to 1, and values exceeding 12 are set to automata are surprisingly good in the test phase at 12. For a more realistic interpretation, values of W are discriminating new grammatical strings from multiplied by 100 (hundreds of workers), and values of P ungrammatical ones (Reber, 1967). Second, participants by 1,000 (tons of sugar). seem able to abstract the structure of temporal event In controlling SF for two subsequent blocks of trials, series, as shown, for instance, by an above-chance ability participants generally exhibit an improvement in their to predict the successive events (Kushner, Cleeremans & performance from the first to the second phase Reber, 1991). Third, participants improve their capacity to demonstrating that some learning occurred, even if they control a dynamic system, albeit being unable to report were actually unaware of the relation between W and P. significant information on how the system worked (Berry Performance in SF has been generally measured by the & Broadbent, 1984). total amount of hits, i.e., trials in which participants In all these domains implicit learning was first reached a production that was at most one unit above or explained by supposing the existence of an unconscious below the target level. This loose criterion was and autonomous learning system capable of extracting introduced to take into account the role of the stochastic knowledge in general, abstract, and transferable rule-like noise e, but is important to notice that participants were 426 not informed about the adopted criterion, and they were 1998; Taatgen & Wallach, 2002), it relies almost kept to believe that the only correct responses were those exclusively on the subsymbolic procedural learning capable of obtaining the exact target production level. mechanism provided by that architecture. Two main computational models have been put forward ACT-R is based on the assumption that the knowledge to explain the results from the Sugar Factory task: one utilized by the cognitive system can be distinguished developed by Dienes & Fahey (1995), and the other by between declarative and procedural. The former encodes Wallach and coworkers (Lebiere, Wallach & Taatgen, knowledge related to remembered facts, perceived events 1998; Taatgen & Wallach, 2002). The former is loosely or sensorial input, and is represented through chunks, i.e., based on Logan’s theory of automatization (Logan, 1988), frame-like structures constituted by labeled slots with the latter is grounded on the ACT-R cognitive architecture associated filler values. Procedural knowledge represents (Anderson & Lebiere, 1998) and takes advantage of many processes and skills used by the cognitive systems to of its features. Both models rely on the retrieval from pursue its goals. It is represented through production memory of previously stored instances of the interaction rules, or productions, whose conditions specify the with SF, although they differ in the way this information patterns of declarative elements that are to be active for is represented, and in the retrieval mechanism adopted. the production to apply. The action part of a production In the paper we illustrate a new procedural model that specifies some modifications to be brought to declarative can better elucidate some intriguing and unexpected data chunks, or some actions to be performed in the obtained in previous experiments with SF (Fum & Stocco, environment. Differently from chunks, productions are 2003), and that can account for other phenomena reported supposed not to be conscious or reportable, and thus are in the literature that are at odds with current explanations. implicit in nature. All the strategies encoded in our model We present a new experiment testing two critical have been implemented as ACT-R productions that are let predictions of the model concerning (a) the role of compete for execution. situational factors, and (b) the effect of a change in the The process of selecting which production to execute criterion adopted by participants to evaluate the outcome among those that have their conditions satisfied is based of their actions in which we obtained results that in ACT-R on the concept of expected utility. The utility corroborated the model. We conclude the paper by Ui of a production i is a noisy, continuously varying emphasizing the role played by procedural knowledge and quantity defined by the formula Ui = PiG – Ci, where Pi evaluation mechanisms in explaining implicit learning represents the probability that production i will reach the phenomena. goal it is meant to achieve, G is the value of the goal itself, and Ci is the estimated cost of applying i. The A Procedural Model for Sugar Factory higher the expected utility of a production, the higher the The model we developed for the SF task is grounded on probability that the production will be chosen for the assumption that participants interacting with SF can execution, even if a randomly distributed noise prevents exploit a set of very simple strategies in choosing the the mechanism from being completely deterministic. workforce value for the SF task. By combing the SF Subsymbolic procedural learning is accomplished in literature, and by looking at the interaction traces of the ACT-R through the adjustment and refinement of the participants, we could identify some of these strategies: production parameters. The estimated value Pi of a Choose-Random: Choose randomly a value between 1 production i, in particular, is given by the ratio between and 12. the number of successes obtained in the past by applying Repeat-Choice: Repeat the value of W chosen in the the production (i.e., the number of times the production previous interaction episode. has succeeded in reaching its goal) and the total number Stay-on-Hit: Whenever the previous W choice resulted of times (i.e., the sum of the successes and failures) the in a success, repeat it. This strategy can be considered as production has been executed. After each production a more selective variant of the previous one. firing, the probability Pi is updated to reflect the statistical Pivot-Around-Target: Choose for W the value of the structure of environment, according to a Bayesian target, plus/minus one. updating framework. Because probability P is initially Jump-Up/Down: If the resulting P is lower than the not defined (and after the very first production firing its target, increase the value of W; if it is higher, diminishes estimate will be 1 or 0 according to whether it was a it. There exist several possible variants of this strategy. success of a failure), it is common practice to start the The one employed in the model was the Jump-on-Middle, learning process by providing each production with values i.e., choose as the new W a quantity that lies midway that represent prior estimates of the production’s between the previous value and the upper/lower limit of successes and failures.
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