Dialogue on economic choice, learning theory, and neuronal representations

Area of Science:

• Neuroscience
• Cognitive Science
• Decision Science

Background:

• Two research fields, associative learning and economic decision-making, often study similar brain regions like the orbitofrontal cortex but remain distinct.
• Existing literature lacks a clear connection between the frameworks of learning theory and neuroeconomics.

Purpose of the Study:

• To clarify the relationship between associative learning and neuroeconomics.
• To identify conceptual correspondences between the two fields, specifically regarding 'outcome' and 'good' concepts.
• To compare the definition and computation of 'value' in both frameworks.

Main Methods:

• Conceptual analysis and synthesis of existing literature from associative learning and neuroeconomics.
• Comparative examination of key concepts such as 'outcome', 'good', and 'value'.
• Identification of shared principles and unresolved differences between the two theoretical frameworks.

Main Results:

• A potential correspondence exists between the concept of 'outcome' in learning theory and 'good' in neuroeconomics.
• The concept of 'value' in both fields shares similarities, particularly in its subjective nature.
• Values are proposed to be devaluation-sensitive and computed dynamically ('on the fly'), rather than being pre-computed or 'cached'.

Conclusions:

• Despite differences, a common understanding of value computation in associative learning and neuroeconomics is emerging.
• Subjectivity and sensitivity to devaluation are key characteristics of value representation in both domains.
• Future research should further explore these shared computational principles for a unified understanding of decision-making and learning.