Lecture 4 - Problem Solving Flashcards

1
Q

What is a problem?

A

When there is an obstacle between our present state and our goal state, and it isn’t obvious how to get around the obstacle.

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2
Q

Different types of problems

A
  • well defined/ill defined

- knowledge rich/knowledge lean

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3
Q

Well defined problems

A
  • Have a correct answer
  • set rules
  • require certain procedures to be applied to arrive at the goal state.
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4
Q

Ill-defined problems

A

-don’t necessarily have a single correct answer
-generally multiple pathways to reaching the solution
Eg breaking up with your bf/gf is an ill-defined problem

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5
Q

Knowledge lean problems

A

-don’t require any specialised knowledge on the part of the problem solver.

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6
Q

Knowledge rich problems

A

-require specialised knowledge on the part of the solver (eg medical diagnoses)

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7
Q

What type of research has the majority of research on problem solving focused on?

A

Well defined, knowledge lean problems

These are generally easier to replicate in the lab.

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8
Q

Gestalt view on problem solving

A

-problem solving depends upon how a problem is REPRESENTED mentally, and how the problem could be RE-STRUCTURED to reach a solution.

Eg: the triangle inside the circle example, what is the circles radius?

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9
Q

Gestalt view - an obstacle to problem solving

A

-fixation: the problem solver focuses on an aspect of the problem that prevents them from moving towards the solution,

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10
Q

An example of fixation as an obstacle to problem solving

A

Functional fixedness - where the problem solver unwittingly restricts the use of an object to its more familiar/usual use.

Eg the candle problem
Present state: box of thumb tacks, matches, a candle.
Goal state: fix the candle to the wall so that it doesnt drip was when lit.
-In order to solve the problem, you need to see the box as a potential shelf for the candle, not as a container. (You have to RESTRUCTURE the problem space).

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11
Q

What is insight?

A

A rapid realisation of a problem’s solution… An “a-ha!!” Moment.

  • Gestalt psychologists took this as evidence of rapid restructuring.
  • However later research has questioned whether this is a real phenomena or not.
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12
Q

The results of studies looking at the insight effect

A
  • results (Metcalfe & Wiebe) indicated that participants experienced insight v non sight problems in different ways.
  • used warmth ratings to see how close the participant felt they were to completing the problem.
  • in non-insight problems (algebra), participants indicated a steady increase in their warmth ratings.
  • in insight problems, participants did indeed experience an “A-Ha!” moment leading to a rapid representation change and the solution.
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13
Q

Basic theory of computational models

A

If a computational model behaves in a similar way to empirical human performance, then it can be considered to be a useful model of the processes underlying human behaviour on that task.

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14
Q

General Problem Solver - Early computational approach

A
  • was shown to be able to provide solutions to a number of well defined,multi step problems (eg towers of Hanoi)
  • at the heart of this approach is the idea of specifying the “Problem Space” for the task.
  • problem space contains all of the potential problem States or legitimate steps that can be taken in order to move from the initial state to the goal state.
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15
Q

The Travelling Salesperson Problem (TSP) - problem space

A
  • enormous problem space (a 20 point problem has around 6x10^16 solutions)
  • no known algorithm that can solve TSP’s in a linear time frame.
  • however, human solves are able to produce solutions that are close to optimal in a linear time frame.
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16
Q

Why are humans so good at solving TSP’s so quickly?

A
  • we solve in a different manner to computer algorithms.
  • Computer algorithms tend to operate in a ‘brute force’ manner, considering potential solutions that humans would never consider as viable.
  • humans appear to search a much smaller problem space.
  • we apply certain heuristics that allow us to limit our search of ‘plausible’ potential solutions.
17
Q

field of dots

A
  • can join them up in which ever way they choose.

- Participants tend to produce minimal structures.

18
Q

Dry et al. - testing Kohler’s assumption

A

(Presented participants with star constellations - participants weren’t aware of this, mirror-transformed them. Asked them to join up the dots)
-results indicated that there was an enormous degree of agreement across the participants in regards to what the salient structure was.

19
Q

The ability of computational models to mimic human performance

A
  • varies across different models and tasks.
  • this is because solving a given problem requires multiple cognitive processes: attention, working memory, semantic info, visual heuristics etc.