The computational form of craving is a selective multiplication of economic value

Abstract

Craving is thought to be a specific desire state that biases choice toward the desired object, be it chocolate or drugs. A vast majority of people report having experienced craving of some kind. In its pathological form craving contributes to health outcomes in addiction and obesity. Yet despite its ubiquity and clinical relevance we still lack a basic neurocomputational understanding of craving. Here, using an instantaneous measure of subjective valuation and selective cue exposure, we identify a behavioral signature of a food craving-like state and advance a computational framework for understanding how this state might transform valuation to bias choice. We find desire induced by exposure to a specific high-calorie, high-fat/sugar snack good is expressed in subjects' momentary willingness to pay for this good. This effect is selective but not exclusive to the exposed good; rather, we find it generalizes to nonexposed goods in proportion to their subjective attribute similarity to the exposed ones. A second manipulation of reward size (number of snack units available for purchase) further suggested that a multiplicative gain mechanism supports the transformation of valuation during laboratory craving. These findings help explain how real-world food craving can result in behaviors inconsistent with preferences expressed in the absence of craving and open a path for the computational modeling of craving-like phenomena using a simple and repeatable experimental tool for assessing subjective states in economic terms.

Keywords: attribute similarity; craving; economic value; multiplicative gain.

Fig. 1.

Experimental procedures. (A) Timeline of the economic decision-making task, exposure, and ratings procedures. (B) During the 3-min selective exposure, subjects were sensorially exposed to one snack good (randomly assigned Snickers, Cheetos, or Coke) while recalling a memory of consuming that particular good. (C) Example bid, desire, and similarity rating trials.

Fig. 2.

Effect of selective exposure on subjective valuation. (A) Average bids, by group, for the exposed good at the two baseline blocks and at the two postexposure blocks. (B) Median rank, based on bid amount, of the exposed good (the three groups combined) relative to all other goods at the same time points (baseline and postexposure), showing preference reversals following exposure. Individual subject data are shown in gray. Values are means ± SE. *P < 0.05, **P < 0.01.

Fig. 3.

Postexposure changes in valuation are proportional to subjective attribute similarity to exposed good. (A) Example subject’s data showing bid increases postexposure depend on similarity of each good to the exposed (in this case Coke, shown at 100% similarity). (B) Monotonic ordering of population bids by similarity. (*) denotes significant bid increases postexposure for the single most similar nonexposed good within the >80% similar set of nonexposed goods. (C) Moving average (size = 3) of the data in B over block number postexposure showing the effect dissipates with time at a rate at which a full return to baseline is predicted within ≈48 blocks or ≈2 h. (D) The same example subject’s data as in A, over block number, for the exposed and the most (>80%) and least (<20%) similar nonexposed goods. The time courses of bids for the exposed and for the most similar goods are tightly coupled, but not for the exposed and the least similar. (E) Monotonic ordering of population correlation coefficients (R values) by similarity, showing the same underlying process explains both the similarity and temporal effects in valuation. Values are means ± SE. *P < 0.05, **P < 0.01.

Fig. 4.

Algorithmic process. (A) Illustrative examples of three possible transformations of valuation following exposure: addition, multiplication, and exponentiation. (B) Empirical data in study 2 showing a specific increase in a linear weight term (ω) but not curvature (α) of the utility function for the exposed good postexposure and a smaller increase in the same parameter for nonexposed but similar goods, consistent with multiplication. Values are means ± SE. *P < 0.05, **P < 0.01.