Decision Processes Leading to Unhealthy Food Choices

Post by Andrew Vo

What's the science?

After making poor dietary choices, we often blame our actions on either a strong preference for tasty (but oftentimes unhealthy) food, or on poor self-control. Traditional computational models characterize such value-based decisions as a dynamic accumulation of evidence that biases us towards one option over another. These models, however, do not account for distinct contributions of separable attributes to a decision (e.g., how health and taste attributes are integrated with different weights and at different times in evidence accumulation). This week in Nature Human Behaviour, Sullivan and Huettel use an updated computational framework to better understand how distinct attributes influence decision processes that could lead to unhealthy food choices.

How did they do it?

The authors recruited a group of young adults who arrived hungry at the lab after a four-hour fast. They were then asked to rate 30 different snack foods based on tastiness, healthiness, and ‘wanting’ attributes. Before beginning the main task, participants received a behavioral primer that emphasized the importance of either healthy or tasty choices. During a main, binary choice task, they were presented with pairs of food items (that they had previously rated) and were asked to indicate which they would like to eat more. Of the 300 self-paced trials, half were designed to be “conflict trials” in which one option was tastier but less healthy than the other, whereas the other half were non-conflict trials in which both options were closely matched.

Participants’ food choices and response times (RTs) were fitted using a multi-attribute, time-dependent, drift diffusion model (mtDDM) (a statistical model). This model has the advantage of distinguishing the various contributions of different attributes to a decision. To do this, it estimates (1) drift slope, which captures the rate of evidence accumulation for each attribute, and (2) drift latency, which describes when each attribute begins to exert its influence during evidence accumulation.

What did they find?

The authors found faster RTs for conflict versus non-conflict trials, as participants made fast unhealthy choices over healthier ones. Those participants who were primed with health information were found to put less weight on taste information, which marginally increased their likelihood of making healthy choices.

The mtDDM estimated that taste drift slopes were larger (steeper) than health drift slopes and taste drift latencies were earlier than health drift latencies. These results suggest that bias towards tasty versus healthy food choices is due to a greater weighting and earlier entry of taste information into evidence accumulation. To test whether slope and latency independently influenced food choices, multiple linear regressions of drift slope and latency differences (i.e., taste minus health) were performed. Both drift slopes and latencies predicted individual differences in the likelihood of healthy food choices. Finally, examining the relationship between trial-by-trial RTs and healthy choices in conflict trials, the authors found that longer RTs were associated with healthier food choices. This suggests that longer RTs allow time for slower-processed healthy information to influence evidence accumulation.

What's the impact?

This study demonstrates how the influence of different attributes on decision-making processes might explain our food choices. The results provide insight into how we can augment our thinking to make better decisions for our long-term benefit, such as considering the healthiness alongside the tastiness of a food item or taking more time to seek out health information on a food choice. Understanding the timing of decision processes in the brain might also be key to creating effective interventions that help people make better choices — not just in terms of diet but also in financial decisions, for example. Much like how you should look before you leap, consider pausing before you place that next restaurant order.

Sullivan & Huettel. Healthful choices depend on the latency and rate of information accumulation. Nature Human Behaviour (2021). Access the original scientific publication here.