Accumulation is late and brief in preferential choice

Abstract

Preferential choices are often explained using models within the evidence accumulation framework: value drives the drift rate at which evidence is accumulated until a threshold is reached and an option is chosen. Although rarely stated explicitly, almost all such models assume that decision makers have knowledge at the onset of the choice of all available attributes and options. In reality however, choice information is viewed piece-by-piece, and is often not completely acquired until late in the choice, if at all. Across four eye-tracking experiments, we show that whether the information was acquired early or late is irrelevant in predicting choice: all that matters is whether or not it was acquired at all. Models with potential alternative assumptions were posited and tested, such as 1) accumulation of instantaneously available information or 2) running estimates as information is acquired. These provided poor fits to the data. We are forced to conclude that participants either are clairvoyant, accumulating using information before they have looked at it, or delay accumulating evidence until very late in the choice, so late that the majority of choice time is not time in which evidence is accumulated. Thus, although the evidence accumulation framework may still be useful in measurement models, it cannot account for the details of the processes involved in decision making.

How Consumers Resolve Conflict over Branded Products: Evidence from Mouse Cursor Trajectories

“…In contrast, dynamic models note how value changes over time as information is sampled and individuals make a choice when enough evidence has accumulated (e.g., Busemeyer and Diederich 2002; Busemeyer and Townsend 1993; Ratcliff 1978; Ratcliff et al 2016). Within the class of dynamic models, most models have assumed that information aggregation begins with the first piece of information sampled, but others have argued that information aggregation does not begin until all pieces of information are sampled (Edmunds et al 2020; Russo and LeClerc 1994; Shi, Wedel, and Pieters 2013). Our findings are consistent with the former class of models suggesting that evidence accumulation is impacted by some attributes before it is impacted by all attributes, as we find differential timing in when mouse trajectories are influenced by attributes.…”

Polarization and extremism emerge from rational choice

“…Each of the studies we analyzed came from an open data set hosted on the Open Science Framework (OSF). The dots data are provided at osf.io/ba5c7; the currency, food, poster, and lottery / DFD data are provided at osf.io/mvk95 (provided in association with Edmunds et al, 2020a); the decisions from experience / DFE data are provided at osf.io/ngc45; and the flash gambling task data (Study 1) are provided at osf.io/g7a49. These studies were chosen so that we knew the data were (1) coming from an incentivized decision making task, (2) tracked the information that participants considered during the decision process, and (3) had all of the necessary information to compute the expected mean and variance of evidence of a random sample for the stimuli on each trial.…”

Toward an Atlas of Canonical Cognitive Mechanisms

“…For instance, do decision‐makers actually integrate difference in evidence? Do they even integrate evidence at all (Cisek, Puskas, & El‐Murr, 2009; Edmunds, Bose, Camerer, Mullett, & Stewart, 2020)? Are there limits on the temporal window of information integration (Usher & McClelland, 2001)?…”