PSY2204 Study Guide - Skill Acquisition & Expertise PDF

PSY2204_Study Guide - Week 7_Skill Acquisition and Expertise Key Topics Covered 1. Stages of Skill Acquisition (Fitts & Posner): Cognitive Stage: Initial learning phase where the focus is on understanding what to do. Heavily reliant on declarative knowledge (knowing what to do but...

PSY2204_Study Guide - Week 7_Skill Acquisition and Expertise Key Topics Covered 1. Stages of Skill Acquisition (Fitts & Posner): Cognitive Stage: Initial learning phase where the focus is on understanding what to do. Heavily reliant on declarative knowledge (knowing what to do but not yet how to do it). Associative Stage: Refining performance through practice. Mistakes are identified and corrected, and knowledge becomes more reliable. Autonomous Stage: The skill becomes automatic, requiring less conscious thought. This is the hallmark of skilled performance. 2. Power Law of Learning: - Performance improves dramatically at first with practice, then slows down as expertise develops. Describes a diminishing return effect. - Example formula: −b T = aP where _T_ is task time, _a_ is initial time, _P_ is practice, and _b_ is the rate of learning. 3. Asymptotic Learning and Retention: As you practice more, performance approaches a limit (asymptote). Skills, however, can decay over time if not practiced (“Use it or lose it”). Retention remains high with skills like driving, with minimal loss over time. 4. ACT Theory (John R. Anderson): Divides knowledge into Declarative (facts) and Procedural (knowing how). Skills develop as declarative knowledge becomes procedural through practice. Proceduralisation: The transition of declarative knowledge to automated processes. Strengthening: Practice strengthens procedural knowledge, making it faster and more reliable. 5. Instance Theory (Logan): Learning involves storing instances (past solutions). As more instances are stored, retrieval becomes faster. Over time, task performance transitions from algorithmic processing to instance retrieval (i.e., relying on past experiences). 6. Expert Memory and Chunking:  Experts don’t have better overall memories but excel in their domains due to chunking— organizing information in meaningful units. For example, expert chess players chunk pieces based on their functions, not just visual layout, enabling them to recall mid-game configurations better than novices. 7. Expertise and Processing: Experts process information differently than novices, focusing on deep, underlying principles rather than surface features. For example, expert physicists think in terms of principles, while novices focus on individual equations. 8. Implicit Learning: Some skills develop implicitly, meaning individuals learn how to perform tasks without being able to explain the rules. Implicit learning often occurs through repeated exposure or practice. 9. Expert-Novice Differences: Novices operate on surface-level information, while experts develop broader, interconnected units of knowledge. Experts are faster and more accurate due to these chunked, principle-based strategies. 10. Challenges of Expertise: Expertise can sometimes lead to bias or hinder performance in novel situations because experts rely heavily on familiar patterns. Key Theories and Models Fitts & Posner's Three-Stage Model: Cognitive Stage: Heavy cognitive load; learning the "what" and "how." Associative Stage: Refining performance, fewer errors, more fluidity. Autonomous Stage: Automatic performance, less attention required. Power Law of Learning: Performance improves with practice but at a diminishing rate. Initial gains are significant, but they taper off over time. ACT Theory (John R. Anderson): Declarative knowledge transforms into procedural knowledge through practice and strengthening. Instance Theory (G.D. Logan): Performance improves with increased retrieval of past instances. The more instances you retrieve, the faster and more automatic performance becomes. Important Experiments & Case Studies Kolers (1976): Demonstrated the Power Law of Learning using inverted text reading. As participants practiced, their reading speed increased dramatically, but after a year, skills decayed and took time to recover. de Groot & Chase & Simon (1973): Chess experiments showed how chunking helps experts remember configurations by processing meaningful patterns rather than surface details. Conclusion Skill acquisition is a gradual process that moves from understanding tasks in a cognitive, deliberate manner to performing them automatically. Through practice and the application of models such as ACT and Instance Theory, learners move toward expertise. Expertise is marked by the efficient use of memory, often through chunking and the retrieval of instances, but it is also subject to limitations such as domain specificity and potential bias in unfamiliar tasks. Implicit learning plays a critical role in acquiring skills without conscious awareness of rules, and expertise is achieved not just through knowledge but through extensive practice and refinement. Table: Key Concepts in Skill Acquisition and Expertise Concept Description Example/Study Cognitive Initial phase of skill acquisition where Learning to drive by recalling Stage the learner is focused on understanding steps for changing gears. what to do. Heavy reliance on declarative knowledge. Associative Intermediate phase where performance Coordinating clutch and Stage becomes more fluid, errors are corrected, accelerator in driving. and knowledge is strengthened. Autonomous Final phase where the skill becomes Experienced drivers who can Stage automatic, requiring little to no converse while driving. conscious thought. Power Law of Describes the pattern of improvement Kolers’ inverted text reading Learning with practice: rapid gains initially, study. Concept Description Example/Study followed by diminishing returns as expertise develops. ACT Theory Explains skill acquisition as a transition Learning math by from declarative knowledge (knowing proceduralising basic equations. what) to procedural knowledge (knowing how), through practice. Instance Describes skill acquisition as the Solving alphabet arithmetic Theory accumulation and retrieval of episodic tasks faster with practice. memories (instances), which lead to faster performance with practice. Chunking Experts group information into Chess players remembering mid- meaningful units, allowing them to game board configurations. process and recall it more efficiently than novices. Retention Skills, once learned, can be retained for Driving or cycling after years long periods with minimal decay, though without practice. a warm-up may be needed after a long break. Expert Memory Experts’ superior recall in their domain of Chess masters’ recall of chess expertise, often facilitated by chunking positions. and domain-specific knowledge. Implicit Learning without explicit knowledge or Subjects controlling a system Learning awareness of the rules governing the without understanding its task, often observed through practice. underlying equation (Berry & Broadbent).