Acquisition of Intellectual and Perceptual-Motor Skills

Rosenbaum, David A.; Carlson, Richard A.; Gilmore, Rick O.

doi:10.1146/annurev.psych.52.1.453

Cited by 274 publications

(167 citation statements)

References 110 publications

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“…Nevertheless, performance in the final phase is fast, effortless, and hardly susceptible to voluntary control just like the automatic processing described by Schneider and Shiffrin (1977). Although Fitts' framework was designed to describe perceptual-INFLEXIBILITY OF EXPERTS 6 motor skill acquisition, the situation with cognitive skill is assumed to be similar (see, for example, Rosenbaum, Carlson, & Gilmore, 2001). In Anderson's ACT-R framework (1983;Anderson & Lebiere, 1998) cognitive skill acquisition starts with a declarative phase, where the learner acquires general information and knowledge about the task, and finishes with a procedural phase in which the knowledge from the declarative phase has been turned into productions, compiled (combination of a series of simpler productions collapsed into a more complex production), and strengthened (productions that produce better results are preferred over the productions that do not).…”

Section: Skill Acquisitionmentioning

confidence: 99%

Inflexibility of experts—Reality or myth? Quantifying the Einstellung effect in chess masters

Bilalić

McLeod

Gobet

2008

Cognitive Psychology

171

132

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Section: Skill Acquisitionmentioning

confidence: 99%

Inflexibility of experts—Reality or myth? Quantifying the Einstellung effect in chess masters

Bilalić

McLeod

Gobet

2008

Cognitive Psychology

171

132

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“…Typically, stimulus magnitudes are coded graphically and without any explicit numeric coding, for example, by a horizontal arrow of varying lengths (Lewandowsky et al, 2002). Response magnitudes can be variously provided by response keys that are labeled with discrete numbers (Kruschke, 2001a), written or verbal responses (Birnbaum, 1976;Mellers, 1986), typing of numeric values (Reed & Evans, 1987), the elapsed time between two key presses (Koh & Meyer, 1991), and graphical means (e.g., participants might have to adjust a vertical slide rule with the mouse, with corrective feedback being presented on the same slide rule; Lewandowsky et al, 2002).The learning of function concepts shares a certain similarity with the learning of sensory and motor functions (Rosenbaum, Carlson, & Gilmore, 2001). In prism adaptation experiments, for example, participants might be presented with a visual target and asked to touch it or throw an object at it, thus learning to compensate for the distortion by prism goggles (Martin, Keating, Goodkin, & Bastian, 1996;Welch, Bridgeman, Anand, & Browman, 1993).…”

mentioning

confidence: 99%

Population of Linear Experts: Knowledge Partitioning and Function Learning.

Kalish¹,

Lewandowsky²,

Kruschke³

2004

Psychological Review

113

196

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Knowledge partitioning is a theoretical construct holding that knowledge is not always integrated and homogeneous but may be separated into independent parcels containing mutually contradictory information. Knowledge partitioning has been observed in research on expertise, categorization, and function learning. This article presents a theory of function learning (the population of linear experts model-POLE) that assumes people partition their knowledge whenever they are presented with a complex task. The authors show that POLE is a general model of function learning that accommodates both benchmark results and recent data on knowledge partitioning. POLE also makes the counterintuitive prediction that a person's distribution of responses to repeated test stimuli should be multimodal. The authors report 3 experiments that support this prediction.The learning of concepts by induction from examples is fundamental to cognition and ". . .basic to all of our intellectual activities" (Estes, 1994, p. 4). Many concepts are categorical: for example, when a paleontologist learns to classify dinosaurs as birdhipped or lizard-hipped, when an infant learns to label furry four-legged animals as cats or dogs, or when a physician learns to categorize a nevus as benign or potentially cancerous. In these cases, responses are limited to a nominal scale, often consisting of binary response options such as "Category A" or "Category B."However, people often also learn function concepts, in which a continuous stimulus variable is associated with a continuous response variable. For example, one may learn how long to water the lawn as a function of the day's temperature, how driving speed affects stopping distance, what his or her blood alcohol level will be depending on the number of cocktails consumed, and so on. Function concepts thus subsume category concepts as the small subset of cases in which the response scale is nominal rather than continuous. Remarkably, cognitive psychology to date has devoted far more empirical and theoretical attention to categorization than to function concepts as a whole.The purpose of this article is twofold. First, we seek to raise the profile of function concepts by presenting a computational theory of function learning that is based on the idea that people simplify a complex learning task by partitioning it into multiple independent modules. The theory, known as POLE-for population of linear experts-is shown to handle most existing data on function learning. Three new experiments explore some of POLE's counterintuitive predictions and provide additional support for the theory. We show that when people are confronted with uncertainty about which of several competing functions applies to a test stimulus, responding alternates between different learned functions rather than relying on a blend of existing knowledge, thus giving rise to multimodal response distributions.The second purpose of this article is to evaluate an overarching framework for learning and knowledge acquisition, known as knowledge part...

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“…"Although it's a hassle, I will cross over my feet and follow the 'fixed' path since I want to avoid the big gap"). This kind of framework has certainly been applied to reaching ESC tasks (Rosenbaum, Carlson & Gilmore, 2001;Smyth & Mason, 1997;Hughes, 1996) and might equally apply to a walking situation.…”

Section: Discussion Experiments 1 Andmentioning

confidence: 99%

The Development of Locomotor Planning for End-State Comfort

2010

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractWalking through real world environments involves complex choices between alternative routes, and the ability to make such choices must develop through childhood. We examine performance in one such situation. We use a novel paradigm analogous to manual 'end-state comfort' (ESC) planning tasks, where an uncomfortable manouevre at the start of a movement is traded off for comfort at its end. We find that adults show locomotor ESC planning, adjusting feet at the start of a route in order to gain comfort at its end (cross a manageable gap between two stepping stones). 3 -6 year olds also make this trade-off, but to a lesser degree than adults. The results suggest that end-state comfort is an important determiner of locomotor behaviour, and show an early ability to engage in locomotor end-state comfort planning.The development of locomotor planning for end-state comfort 3

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Acquisition of Intellectual and Perceptual-Motor Skills

Cited by 274 publications

References 110 publications

Inflexibility of experts—Reality or myth? Quantifying the Einstellung effect in chess masters

Inflexibility of experts—Reality or myth? Quantifying the Einstellung effect in chess masters

Population of Linear Experts: Knowledge Partitioning and Function Learning.

The Development of Locomotor Planning for End-State Comfort

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