Introduction

Humans are often not fully rational economic agents. We make choices that diverge from classical economic predictions and are strongly influenced by the context in which options are presented. Why do we choose to gamble when an option is framed as a potential loss, but play it safe when the same outcome is framed as a potential gain? Behavioral economics describes this with prospect theory Kahneman & Tversky, 1979, which emphasizes three core principles: reference points, loss aversion, and framing effects Tversky & Kahneman, 1989.

Prospect theory is powerful descriptively, but it does not by itself specify a neural mechanism. Context-dependent neural coding offers that missing mechanistic layer: the brain encodes value relative to surrounding context, such as available alternatives and internal state Louie & De Martino, 2014. This essay reviews work in neuroeconomics to show how context-dependent coding helps explain the same behavioral regularities captured by prospect theory.

Reference point

Context-dependent neural coding explains the reference point principle. Prospect theory argues that outcomes are evaluated relative to a baseline rather than in absolute terms Kahneman & Tversky, 1979. Likewise, valuation in the brain appears relative rather than fixed. Neural systems can encode a reward differently depending on the local choice context De Martino et al., 2009Elliott et al., 2008.

This helps explain unstable preferences. A $50 reward can feel highly attractive when paired with $10, but less attractive when paired with $100. Brain regions involved in contextual processing and valuation appear to support this dynamic recalibration.

Relative vs. absolute value coding

Elliott et al. tested this directly with fMRI by teaching participants to associate abstract cues with low, medium, and high monetary rewards Elliott et al., 2008. Critically, the medium cue represented the same absolute payoff in both conditions, but its relative standing changed depending on the paired alternative.

Activity in medial orbitofrontal cortex (OFC) was higher when the medium cue was the better option than when it was the worse option. Because stimulus identity, response demands, and objective reward magnitude were held constant, this pattern supports relative value coding over absolute value coding.

Neural coding of the reference point

Related work identifies specific neural substrates that may instantiate reference points. In macaques, anterior insular cortex (AIC) neurons encode current token wealth, effectively tracking an internal baseline for subsequent choices Yang et al., 2022. In humans, hippocampal activity increases when contextual reward information is introduced, and this activation relates to contextual influence on behavior Rigoli et al., 2016.

Together, these findings move beyond the claim that value is relative. They suggest that particular circuits contribute to representing and updating the baseline against which outcomes are evaluated.

Loss aversion

Context-dependent coding also helps explain loss aversion, the well-known asymmetry where losses loom larger than equivalent gains Kahneman & Tversky, 1979. This is behaviorally visible when people reject positive expected-value gambles because potential losses are weighted more heavily than potential gains.

Tom et al. examined this using fMRI while participants accepted or rejected mixed gambles Tom et al., 2007. Activity in ventral striatum and ventromedial prefrontal cortex (vmPFC) increased with prospective gains and decreased with prospective losses. The decline for losses was steeper than the increase for gains, a pattern they characterized as neural loss aversion. Individual differences in this neural asymmetry predicted individual differences in behavioral loss aversion.

Conceptual visualization: this curve illustrates steeper subjective weighting for losses than gains.

Comparable findings in valuation tasks, including ownership-based distortions between willingness to accept and willingness to pay, further support a neural basis for asymmetric valuation De Martino et al., 2009. In non-human primates, AIC populations also show disproportionate sensitivity to loss-related signals Yang et al., 2022. Taken together, these data align prospect theory's asymmetric value function with asymmetries in neural coding.

Framing effect

The framing effect is another context-driven deviation from classical rational choice: equivalent outcomes can lead to different decisions depending on whether they are described as gains or losses Tversky & Kahneman, 1989.

De Martino et al. used fMRI in a gain/loss framing paradigm with equivalent expected outcomes De Martino et al., 2006. Choices consistent with framing bias were associated with greater amygdala activity. By contrast, choices resisting the framing bias recruited regulatory regions, including anterior cingulate cortex (ACC), and lower susceptibility was linked to stronger orbitofrontal and medial prefrontal involvement.

These findings suggest that framing does not simply alter verbal description. It changes neural valuation dynamics, creating predictable shifts in value-based behavior.

Conclusion

Context-dependent neural coding provides a plausible neurobiological bridge between descriptive behavioral economics and brain-level implementation. Across reference dependence, loss aversion, and framing, the literature converges on the idea that valuation is not static or absolute; it is context-sensitive and neurally instantiated Louie & De Martino, 2014.

Prospect theory explains what people tend to do under risk. Neuroeconomics helps explain how those patterns emerge from the interaction of contextual signals, valuation systems, and control processes. Linking these levels improves our understanding of why economic decisions systematically depart from normative rational models.

References

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