Cognitive Dynamics on the Balance-Scale Task

Cognitive Dynamics on the Balance-Scale Task Corinne Zimmerman (czimmer@ilstu.edu) Steve Croker (s.croker@ilstu.edu) Department of Psychology, Illinois State University Campus Box 4620 Normal, IL 61790-4620 USA Abstract The Balance-Scale Task Our ability to detect patterns and observe regularities is a fundamental part of reasoning and learning. Various theoretical accounts conceptualize induction in different ways. In the current study, we used the balance-scale task and mouse-tracking techniques as a means to explore the cognitive dynamics that underlie inductive pattern recognition. Although we did not replicate an expected interaction between problem difficulty and task instruction because of a procedural constraint, we were still able to examine the implicit cognitive dynamics underlying explicit behavioral responses as a function of problem difficulty. In particular, we found some support that newer, non- algorithmic accounts of cognition on such tasks better characterize performance than rule-based accounts. Keywords: induction; balance-scale task; mouse tracking; cognitive dynamics Inductive reasoning is a fundamental component of the scientific process. A key cognitive process underlying scientific reasoning skills (e.g., prediction, hypothesis testing, conceptual change) is the ability to induce patterns in evidence. In order to discover laws and formulate hypotheses, scientists use induction to find regularities in observed phenomena. Sometimes we may search explicitly for rules that govern natural phenomena, but we also use implicit learning processes to discover patterns and rules. The processes that underlie induction have been conceptualized in different ways. According to information- processing accounts, knowledge structures can be described in terms of symbols, algorithms, and rules. More recent theories in psychology, such as the dynamic systems approach (Thelen & Smith, 1994), have conceptualized learning and change in terms of human behavior being situated and embodied. Dynamic systems theory has provided insight into accounting for change, but largely with respect to perceptual and motor processes. Thus far, it has been difficult for us to determine the best way to apply dynamic systems concepts to higher-level cognitive phenomena such as inductive reasoning. New research techniques and data-analytic methods allow us to apply these theoretical constructs and explanations to higher-level cognition. Although computerized tasks have been used to measure accuracy and reaction time, mouse tracking can be used to examine the temporal, embodied, and dynamic elements of cognition, and to track learning across micro timescales. Moreover, this technique provides a window into the implicit processes that underlie explicit responses. Piaget introduced the balance-scale task as a means of studying cognitive development (Inhelder & Piaget, 1958). Participants make predictions about a two-arm balance scale on which weights are placed on pegs at different distances from the fulcrum. Siegler (1976) demonstrated that changes in performance could be described with respect to different strategies or rules. Several theoretical accounts of change have been proposed. Proponents of classic information- processing theories (e.g., Klahr & Siegler, 1978) argue that we acquire a series of algorithms that we transition through as a function of experience. According to connectionist accounts (e.g., McClelland, 1989), changes in performance are a result of learning statistical relations between problems and correct answers. Zimmerman (1999) derived predictions for performance from an analysis of how neural networks solve these problems and an analysis of the problem space. In particular, these predictions focused on the idea that performance measures (accuracy and response time) should vary as a function of where a problem is located in the problem space (e.g., participants should react faster and more accurately to problems with a high torque difference). Dynamic systems theorists (e.g., van der Maas & Raijmakers, 2009) discuss transitions in terms of patterns of variability and stability; a previously stable behavioral pattern becomes highly variable and undergoes a sudden transition to a new pattern of behavior. Over the last few decades, many researchers have examined performance on the balance-scale task, typically with the goals of determining (a) what underlying competencies exist at different ages and (b) how performance is affected by factors such as the number of pegs and weights and whether feedback is given immediately following each trial. The balance-scale task has several features that make it ideal for studying cognitive change and learning. It is simple enough that young children understand the task demands, yet the underlying physics principle (torque) is complex enough that adults require much experience with the task before they can induce the underlying physical rules. The problem space has been analyzed in detail (Zimmerman, 1999), and we know much about the context effects that influence performance and facilitate rule discovery (Messer, Pine, & Butler, 2008). Zimmerman and Pretz (2012) examined the effects of instructing participants to make rapid predictions or to think deeply and try to discover the rule underlying the balance