From single decisions to sequential choice patterns: Extending the dynamics of value-based decision-making.

Area of Science:

• Cognitive Neuroscience
• Decision Science
• Computational Psychology

Background:

• Value-based decisions are typically studied in isolation, neglecting the influence of prior choices.
• Real-world decision-making involves sequential choices influenced by past experiences across various timescales.
• Understanding the temporal dynamics of value-based decisions is crucial for accurate modeling.

Purpose of the Study:

• To investigate the temporal dynamics of value-based decision-making across sequential trials.
• To explore how previous choices impact current decisions and their underlying neural mechanisms.
• To determine if sequential decision patterns observed in perceptual tasks extend to value-based choices.

Main Methods:

• Utilized a neural-inspired attractor model, incorporating activation dynamics across decisions.
• Predicted sequential patterns: oscillatory switching, perseveration, and delay-dependent perseveration.
• Validated model predictions through two new empirical studies and reanalysis of existing delay discounting data.

Main Results:

• Confirmed oscillatory switching and perseveration as key sequential decision patterns.
• Demonstrated that the delay between decisions significantly influences perseveration.
• Identified reaction time (RT) effects contingent on specific trial sequences.
• Validated model predictions across diverse experimental paradigms, including delay discounting and a choice questionnaire.

Conclusions:

• Sequential choice patterns are integral to value-based decision-making dynamics.
• Dynamic models incorporating activation dynamics are essential for accurate analysis and modeling.
• Accounting for sequential effects is critical for understanding individual differences in decision-making.