Neural similarity between choice options predicts group-level context effects.

Area of Science:

• Neuroscience
• Cognitive Science
• Decision Science

Background:

• Choice context effects influence decisions based on available options.
• Existing models rely on two explicit numerical attributes for options.
• Decision-makers may use unstated, latent attributes influencing choices.

Purpose of the Study:

• To predict choice context effects using neural representations.
• To access and model latent attributes influencing decisions.
• To advance understanding of decision-making beyond explicit attributes.

Main Methods:

• Behavioral estimation of choice context effects for lotteries.
• fMRI data acquisition to capture neural representations of lotteries.
• Predicting context effects using similarity in neural representations.

Main Results:

• Neural representations predicted choice context effects effectively.
• Predictions improved compared to traditional attribute-based models.
• Neural encoding captured both explicit and latent decision attributes.

Conclusions:

• Neural representations offer a powerful tool to study decision-making.
• This approach overcomes limitations of models requiring explicit attributes.
• Unveiling latent attributes enhances our understanding of choice context effects.