Project Versions

All versions of the project were created in Psychopy. The code for all experiment versions is on github.

Note

Versions 1 and 2 were made with the Builder interface. Version 3 was written in plain Python using the Psychopy 3 toolbox.


The current iterations of the project include:

v1

Description

  • 1,2,3 multipliers

Goal

  • Disambuguate atribute weights from attribute evaluation
  • Test whether value and weighting affect subject attention

v2

Description

  • Additional multipliers (0.1, 0.5, 1, 2, 3, 10)

Goal

  • Test response to fractional weights
  • Calculate subjects’ weighting curve to wider range of weights

v3.0.1

Description

  • Both multipliers onscreen simultaneously (0.1, 0.33, 0.5, 1, 2, 3, 10)

Goal

  • Test whether subjects bias their first/total fixation to the higher multiplier

v3.0.2

Description

  • Accuracy incentive
  • Both multipliers onscreen simultaneously (0.1, 0.33, 0.5, 1, 2, 3, 10)

Goal

  • Test whether subjects try harder with an accuracy incentive, rather than a cumulative payoff

    • This avoids the most difficult trials having the lowest value/cost

v3.1.0

Description

  • Time pressure (low)
  • Accuracy incentive
  • Both multipliers onscreen simultaneously (0.1, 0.33, 0.5, 1, 2, 3, 10)

Goal

  • Test whether time pressure increases the likelihood of biasing first fixation toward the higher weighted stimulus

v3.1.1

Description

  • Time pressure (low/high/no)
  • Accuracy incentive
  • Both multipliers onscreen simultaneously (0.1, 0.5, 1, 2, 3, 10)

Goal

  • Test whether time pressure increases the likelihood of biasing first fixation toward the higher weighted stimulus under three conditions.

v3.2.0

Description

  • Full choice (multipliers and images)
  • Accuracy incentive
  • (0.1, 0.5, 1, 2, 3, 10)

Goal

  • Test whether a stimulus (base value) or multiplier (weight) bias exists.

v3.3.0

Description

  • Bias test: Attractiveness
  • Accuracy incentive
  • (0.1, 0.5, 1, 2, 3, 10)

Goal

  • Test whether subjects over-weight attractive vs. unattractive faces