Deciding not to decide: computational and neural evidence for hidden behavior in sequential choice

Area of Science:

• Cognitive Neuroscience
• Computational Psychiatry
• Decision Sciences

Background:

• Sequential sampling models (SSMs) predict decisions based on evidence accumulation but struggle with response time variability.
• Standard SSMs do not account for decision-makers temporarily delaying their choice.

Purpose of the Study:

• To develop and validate an extended SSM that incorporates decision postponement.
• To investigate the neural correlates of decision delays using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI).

Main Methods:

• Developed novel SSMs incorporating a decision postponement mechanism.
• Collected EEG and fMRI data from participants making sequential choices in a simulated stock market task.
• Analyzed EEG data using time-frequency analysis to examine beta-band oscillations.

Main Results:

• Standard SSMs inadequately described response time distributions.
• The extended SSM with decision postponement accurately fit the empirical data.
• EEG data showed alternating beta-band power, indicating response preparation and inhibition, supporting the decision-not-to-decide hypothesis.

Conclusions:

• Decision postponement is a critical factor in human decision-making not captured by standard SSMs.
• The extended model provides a more accurate account of decision dynamics and response times.
• Neural data supports the existence of temporary termination of evidence accumulation during decision-making.