Multialternative decision field theory: a dynamic connectionist model of decision making

Area of Science:

• Cognitive Psychology
• Decision Science
• Computational Neuroscience

Background:

• Decision Field Theory (DFT) provides a dynamic framework for understanding choice behavior.
• Existing models struggle to comprehensively explain multialternative choice phenomena.
• Empirical findings like similarity, attraction, and compromise effects present challenges for current theories.

Purpose of the Study:

• To extend Decision Field Theory (DFT) to multialternative preferential choice situations.
• To demonstrate that established models are special cases of the proposed Multialternative Decision Field Theory (MDFT).
• To explain key empirical effects in multialternative choice using a unified theoretical framework.

Main Methods:

• Interpreting DFT as a connectionist network.
• Developing the Multialternative Decision Field Theory (MDFT).
• Comparing MDFT with weighted additive utility, Thurstone, and Elimination by Aspects (EBA) models.

Main Results:

• MDFT successfully integrates weighted additive utility and Thurstone models as special cases.
• MDFT emulates the search process of the Elimination by Aspects (EBA) model.
• MDFT explains the similarity, attraction, and compromise effects and their interactions.

Conclusions:

• MDFT offers a unified and dynamic framework for understanding multialternative preferential choice.
• The theory provides novel insights into the interplay of choice effects.
• MDFT generates testable predictions about the influence of time pressure on choice behavior.