Understanding the dynamics of serial dietary decisions through the lens of sequential sampling modeling

Often in our lives, we need to take a series of sequential actions rather than making a single, isolated decision. A typical everyday example is to decide which and how many items we want to put in our shopping basket. Multiple previous studies have investigated the temporal dynamics of these sequential decisions using a virtual shopping paradigm (Wolf et al., 2018, 2019; Xu et al., in press). Specifically, these studies have examined the probability and speed of dietary decisions with various constraints but did not further study the underlying cognitive mechanisms. Here, we show how a process model based on the sequential sampling modeling framework can elucidate those mechanisms and provide a comprehensive account of the reported choice and response time (RT) patterns. In the virtual shopping paradigm, participants decided for sequentially encountered items whether or not to add them into their shopping basket (“pick” or “leave”). The number of food items that could be added to the basket was limited and manipulated in one experiment. In some conditions, participants were given the opportunity to defer the food item by placing it on a waiting list. Importantly, “pick” decisions tended to be longer than “leave” decisions, and this difference was most pronounced when fewer food items could be selected. Decisions to wait were slower than both “pick” and “leave” decisions. On the inter-individual level, the RT difference between “pick” and “leave” decisions was higher for participants who rejected more options. We account for these choice and RT patterns using a new variant of the Feed-Forward Inhibition model (FFI) (Shadlen & Newsome, 2001) with three separate accumulators for the decisions "leave", "pick", and "wait". The “leave” and “pick” accumulators, but not the “wait” accumulator, inhibit each other. Due to the limited basket space, we assume that participants have a default preference for leaving the food item, which we implement by shifting the starting point of the accumulation process in favor of “leave” vs. “pick” decisions as a function of the basket size. Our simulations show that this model can account for the various choice and RT patterns in the virtual shopping paradigm, including the RT difference in “leave” vs. “pick” decisions, the excessively long “wait” decisions, and the dependency of these differences on the basket size. In addition, when inspecting our simulations, we see that the drift rate of “pick”-choices is considerably higher than average, mimicking the fact that only high-value food items are chosen for the basket. In contrast, the drift rate of “wait”-choices is only slightly above average, suggesting that food items which are of a somewhat higher value are put on the waiting list. Essentially, by simulating the data, we were able to reproduce the main effects of the above-mentioned studies and provided mechanistic explanations for sequential shopping decisions. Until the start of the conference, we also plan to replicate the paradigm and fit our model to the data. Shadlen, M. N., & Newsome, W. T. (2001). Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. Journal of neurophysiology, 86(4), 1916-1936. Wolf, A., Ounjai, K., Takahashi, M., Kobayashi, S., Matsuda, T., & Lauwereyns, J. (2018). Evaluative processing of food images: a conditional role for viewing in preference formation. Frontiers in Psychology, 9, 936. Wolf, A., Ounjai, K., Takahashi, M., Kobayashi, S., Matsuda, T., & Lauwereyns, J. (2019). Evaluative processing of food images: longer viewing for indecisive preference formation. Frontiers in Psychology, 10, 608. Xu, J., Jin Y., Lauwereyns, J. (in press). The Framing of Choice Nudges Prolonged Processing in the Evaluation of Food Images. Frontiers in Psychology