Cognitive Processes PSY 334

Slides:



Advertisements
Similar presentations
Novice and Expert Programmers Gild Project University of Victoria Jeff Michaud.
Advertisements

Learning Objectives Explain similarities and differences among algorithms, programs, and heuristic solutions List the five essential properties of an algorithm.
Chapter 10 Algorithmic Thinking. Copyright © 2013 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Learning Objectives List the five essential.
Best-First Search: Agendas
Identifying Content and Specifying Behaviors
Lecture Notes 1/21/04 Program Design & Intro to Algorithms.
Cognitive Processes PSY 334 Chapter 8 – Problem Solving May 21, 2003.
Models of Human Performance Dr. Chris Baber. 2 Objectives Introduce theory-based models for predicting human performance Introduce competence-based models.
Cognitive Processes PSY 334 Chapter 8 – Problem Solving.
Principles of High Quality Assessment
Problem Solving & Creativity Dr. Claudia J. Stanny EXP 4507 Memory & Cognition Spring 2009.
Learning and Teaching Theories Developmental trends Information – Processing Strategy Instruction Warm-up: –What are learning strategies and how are they.
Doing the Right Thing at the Right Time and Place: Stimulus Discrimination and Generalization Chapter 8.
An Information Theory based Modeling of DSMLs Zekai Demirezen 1, Barrett Bryant 1, Murat M. Tanik 2 1 Department of Computer and Information Sciences,
Improving a Mathematical Intelligent Tutoring System Experiments & Equation Solver Improvements July 27th, 2012 Jennifer Ferris-Glick & Hee Seung Lee.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
Problem-Solving and Reasoning
Chapter 1 Introduction Dr. Frank Lee. 1.1 Why Study Compiler? To write more efficient code in a high-level language To provide solid foundation in parsing.
 Knowledge Acquisition  Machine Learning. The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Rational/Theoretical Cognitive Task Analysis Ken Koedinger Key reading: Zhu, X., & Simon, H. A. (1987). Learning mathematics from examples and by doing.
Standards for Mathematical Practice
Problems, Problem Spaces and Search
WHS AP Psychology Unit 6: Cognition Essential Task 6-2: Identify problem-solving techniques (algorithms and heuristics) as well as factors that influence.
Writing Student-Centered Learning Objectives Please see Reference Document for references used in this presentation.
Bloom’s Taxonomy Benjamin S. Bloom February 21, September 13, 1999.
Chapter Five The Cognitive Approach II: Memory, Imagery, and Problem Solving.
CHAPTER FIVE The Cognitive Approach II: Memory, Imagery, and Problem Solving.
Cognitive Processes PSY 334
Generic Tasks by Ihab M. Amer Graduate Student Computer Science Dept. AUC, Cairo, Egypt.
Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research.
1 Analysing system-user cooperation in KADS H. P. de Greef and J. A. Breuker, Department of Social Science Informatics, University of Amsterdam Knowledge.
Modularity. Methods & Class as program unit A method is comprised of statement sequences and is often viewed as the smallest program unit to be considered.
INSTRUCTIONAL OBJECTIVES
Complex Cognitive Processes EDP 3004-Chapter 8. Metacognition –Awareness of your own cognitive machinery and how it works –Involves three kinds of knowledge:
RULES Patty Nordstrom Hien Nguyen. "Cognitive Skills are Realized by Production Rules"
Introduction to State Space Search
SOAR A cognitive architecture By: Majid Ali Khan.
Problem Solving PERTEMUAN Early research on problem- solving A cat placed in a box with a trapdoor was not observed to show behaviour approximating.
Chapter 1 The Phases of Software Development. Software Development Phases ● Specification of the task ● Design of a solution ● Implementation of solution.
ECE-7000: Nonlinear Dynamical Systems 3. Phase Space Methods 3.1 Determinism: Uniqueness in phase space We Assume that the system is linear stochastic.
1 Solving Problems with Methods Questions. 2 Problem solving is a process similar to working your way through a maze. But what are these “steps” and what.
Reading literacy. Definition of reading literacy: “Reading literacy is understanding, using and reflecting on written texts, in order to achieve one’s.
SYNTAX.
CHAPTER 7: The Effective Lesson © (2015, 2012, 2009) by Pearson Education, Inc. All Rights Reserved Educational Psychology: Theory and Practice Edition.
Knowledge Representation Techniques
Algorithms and Problem Solving
Using Cognitive Science To Inform Instructional Design
Chapter 5 – Requirements Engineering
Section 2-1 Inductive Reasoning.
Cognition and Language
IB Assessments CRITERION!!!.
Developing Goals and Objectives
Unit 6: Cognition WHS AP Psychology
Computer Programming.
Problem Solving A Major Category of Human Cognition
Problem Solving Techniques
Conducting a Goal Analysis
Informatics 121 Software Design I
Memory and Thought Chapter 3.
Problem Solving 8.2.
Programming Fundamentals (750113) Ch1. Problem Solving
Programming Fundamentals (750113) Ch1. Problem Solving
Unit 7: Cognition AP Psychology
Programming Fundamentals (750113) Ch1. Problem Solving
Unit 7: Cognition AP Psychology
Copyright © 2011 Pearson Education, Inc. All rights reserved.
Cognitive Processes PSY 334
Programming Fundamentals (750113) Ch1. Problem Solving
Conducting Knowledge Application Lessons By 4th Grade Reading
Social Practice of the language: Describe and share information
Presentation transcript:

Cognitive Processes PSY 334 Chapter 8 – Problem Solving

Procedural Knowledge Declarative knowledge – knowledge about facts and things Procedural knowledge – knowledge about how to perform various cognitive activities. To a cognitive psychologist all cognitive activities are fundamentally problem-solving in nature. Sultan and the bananas

Elements of Problem Solving Goal directedness – behavior is organized toward a goal. Subgoal decomposition – the original goal can be broken into subtasks or subgoals. Operator application – the solution to the overall problem is a sequence of known operators (actions to change the situation).

The Problem Space Problem space – the various states of the problem. State – a representation of the problem in some degree of solution. Initial state – the initial (starting) situation. Goal state – the desired ending situation. Intermediate states – states on the way to the goal.

Search Operator – an action that will transform the current problem state into another problem state. The problem space is a maze of states. Operators provide paths through the maze – ways of moving through states. Problem solving is a search for the appropriate path through the maze. Search trees – describe possible paths.

Acquisition of Operators How do we learn ways of transforming problem states (operators)? Discovery – trial and error, exploration. Instruction – depends on language. Observation and imitation – monkey see, monkey do. Examples are chances for observation: 13% solved with instruction, 28% with an example, 40% with both.

Analogy and Imitation Analogy – the solution for one problem is mapped into a solution for another. The elements from one situation correspond to the elements of the other. Tumor radiation example.

Problems Using Analogy Thinking is needing to use it correctly. Geometry example – student must recognize which parts can be mapped and which are unique to the situation. People do not notice when an analogy is possible – don’t recognize the similarities. Similarities frequently exist in the deep structure, not the superficial details. Proximity is a cue in textbooks.

Production Systems Production rules – rules for solving a problem. A production rule consists of: Goal Application tests An action Typically written as if-then statements. Condition – the “if” part, goal and tests. Action – the “then” part, actions to do.

Features of Production Rules Conditionality –a condition describes when a rule applies and specifies action. Modularity – overall problem-solving is broken down into one production rule per operator. Goal factoring – each production rule is relevant to a particular goal (or subgoal). Abstractness – rules apply to a defined class of situations.