Optimal coding and neuronal adaptation in economic decisions

Abstract

During economic decisions, offer value cells in orbitofrontal cortex (OFC) encode the values of offered goods. Furthermore, their tuning functions adapt to the range of values available in any given context. A fundamental and open question is whether range adaptation is behaviorally advantageous. Here we present a theory of optimal coding for economic decisions. We propose that the representation of offer values is optimal if it ensures maximal expected payoff. In this framework, we examine offer value cells in non-human primates. We show that their responses are quasi-linear even when optimal tuning functions are highly non-linear. Most importantly, we demonstrate that for linear tuning functions range adaptation maximizes the expected payoff. Thus value coding in OFC is functionally rigid (linear tuning) but parametrically plastic (range adaptation with optimal gain). Importantly, the benefit of range adaptation outweighs the cost of functional rigidity. While generally suboptimal, linear tuning may facilitate transitive choices.

Values Encoded in Orbitofrontal Cortex Are Causally Related to Economic Choices

“…In these conditions, two groups of cells in OFC encode the offer values of juices A and B (Padoa-Schioppa and Assad, 2006;Padoa-Schioppa, 2013). Importantly, their tuning curves are quasi-linear and the gain is inversely proportional to the value range (range adaptation) (Padoa-Schioppa, 2009;Rustichini et al, 2017). Moreover, cells in each group adapt to their own value range.…”

“…Here we formalize the prediction illustrated in Fig.3. As a premise, previous work found that the tuning curves of offer value cells in OFC are quasi-linear (Rustichini et al, 2017) and the proportion of neurons presenting positive versus negative encoding is roughly 3:1 (Padoa-Schioppa, 2013;Ballesta and Padoa-Schioppa, 2019). Importantly, cells in each group adapt to their own range, not to the maximum range (Conen and Padoa-Schioppa, 2019).…”

The effect of counterfactual information on outcome value coding in medial prefrontal and cingulate cortex: from an absolute to a relative neural code

“…These context effects allow efficient coding of broad ranges of sensory input and are well documented in perception (see, e.g., Carandini and Heeger, 2012;Louie and Glimcher, 2012). Recently, context--dependence and normalization were reported also in value--based decision, in both monkeys (Padoa--Schioppa, 2009;Bermudez and Schultz, 2010;Kobayashi et al, 2010;Rustichini et al, 2017;Conen and Padoa--Schioppa, 2019) and humans (Nieuwenhuis et al, 2005;Elliott et al, 2008;Cox and Kable, 2014;Burke et al, 2016; see Louie and De Martino, 2014 for a review), allowing optimal responses to vast ranges of values in PFC. Specifically, neurons seem to rescale their firing to adapt to the decision context (relative coding) so that their response to a specific value depends on the choice context (e.g., reward vs. punishment), rather than being invariant (absolute coding).…”

The bounded rationality of probability distortion

“…There is increasing evidence that dynamic remapping of representational range occurs along more abstract dimensions, such as value 25,26,27,28,29 , numerosity 30,31 , relative frequency 32 , and variance 33 .…”