Problem solving

Welcome to Episode 5: Problem Solving. In this episode, we'll explore one of the most complex and essential cognitive functions: how we navigate challenges and find solutions. Building on our understanding of *Memory*, *Attention*, *Perception*, and *Learning*, we'll define what constitutes a 'problem' from a psychological perspective. We will differentiate between well-defined and ill-defined problems and examine the mental toolkits we use, including methodical algorithms and clever heuristics. You'll also learn about common cognitive barriers that can trap our thinking, such as mental sets and functional fixedness. By the end, you'll have a clearer framework for understanding the structured yet flexible process your mind undertakes whenever it faces an obstacle.

Check your understanding

These are the same multiple-choice questions you will see in the Quiz section after you listen to the episode. Use them here to preview or review the answers.

Which of these are considered the three fundamental components defining a problem in cognitive psychology?

  1. The initial state
  2. The goal state
  3. The operators or actions
  4. A heuristic approach
  5. Functional fixedness
  6. The solution itself

A mental shortcut or 'rule of thumb' used to solve problems more quickly, though not always correctly, is known as what?

  1. An algorithm
  2. A mental set
  3. A heuristic
  4. Functional fixedness
  5. A schema

Which of the following are common barriers that can hinder effective problem-solving?

  1. Mental set
  2. Means-ends analysis
  3. Functional fixedness
  4. Analogical reasoning
  5. Confirmation bias

You need to figure out how to get to a friend's new house. You look up their address on a map and then trace the best route from their house back to your own. This strategy is an example of:

  1. Working backwards
  2. An algorithm
  3. Functional fixedness
  4. Means-ends analysis

An ill-defined problem is characterized by which of the following features?

  1. It can only be solved with a specific algorithm
  2. Uncertainty about the necessary operators or actions
  3. A clear and unambiguous goal state
  4. Vagueness in the initial state or goal
  5. A single, guaranteed correct answer

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