Equal evidence perceptual tasks suggest a key role for interactive competition in decision-making.

Abstract

The dynamics of decision-making have been widely studied over the past several decades through the lens of an overarching theory called sequential sampling theory (SST). Within SST, choices are represented as accumulators, each of which races toward a decision boundary by drawing stochastic samples of evidence through time. Although progress has been made in understanding how decisions are made within the SST framework, considerable debate centers on whether the accumulators exhibit dependency during the evidence accumulation process; namely, whether accumulators are independent, fully dependent, or partially dependent. To evaluate which type of dependency is the most plausible representation of human decision-making, we applied a novel twist on two classic perceptual tasks; namely, in addition to the classic paradigm (i.e., the unequal-evidence conditions), we used stimuli that provided different magnitudes of equal-evidence (i.e., the equal-evidence conditions). In equal-evidence conditions, response times systematically decreased with increases in the magnitude of evidence, whereas in unequal-evidence conditions, response times systematically increased as the difference in evidence between the two alternatives decreased. We designed a spectrum of models that ranged from independent accumulation to fully dependent accumulation, while also examining the effects of within-trial and between-trial variability. We then fit the set of models to our two experiments and found that models instantiating the principles of partial dependency provided the best fit to the data. Our results further suggest that mechanisms inducing partial dependency, such as lateral inhibition, are beneficial for understanding complex decision-making dynamics, even when the task is relatively simple.

Accumulating evidence for myriad alternatives: Modeling the generation of free association.

“…Second, evidence accumulation models offer a variety of distinct mechanisms to account for competition between response alternatives, including rich-get-richer dynamics (or closely related lateral inhibition), and normalization of evidence (Teodorescu & Usher, 2013). Third, an increasing number of studies have highlighted the role of the absolute level of support (in addition to relative support) for selected and nonselected response alternatives in evidence accumulation (Kirkpatrick et al, 2021; Simen et al, 2016; Teodorescu et al, 2016; van Ravenzwaaij et al, 2020). Thus, evidence accumulation models are, in principle, well-equipped to explain the latent cognitive dynamics giving rise to empirically observed associations and the speed with which they are generated.…”

Magnitude-sensitivity: rethinking decision-making

“…when, compared with a baseline condition, the magnitude of the alternatives is increased by keeping either difference or ratio between alternatives constant [11]; similarly, in other studies that compared models extensively, race models performed poorly when fitted to data [58]. For these reasons [2,11,58], this account is not generally considered as a plausible candidate in investigations about magnitude-sensitivity.…”

"Reliable Organisms from Unreliable Components'" Revisited: The Linear Drift, Linear Infinitesimal Variance Model of Decision Making

“…Other accumulator model architectures, like the simple accumulator or recruitment model (Audley & Pike, 1965;La Berge, 1962), the Poisson counter model (Smith & Van Zandt, 2000;Townsend & Ashby, 1983), and the Vickers accumulator (Smith & Vickers, 1988;Vickers, 1970Vickers, , 1979, give poorer RT distribution predictions than models based on racing diffusion processes . Usher and McClelland (2001) proposed a model with mutual inhibition between the accumulators on grounds of neural plausibility and some researchers have reported that these kinds of models give a better fit to data (Ditterich, 2006;Kirkpatrick et al 2021). Others have found that models with independent accumulators give good accounts of data Smith & Ratcliff, 2009;Tillman et al 2020) including neural data.…”

“…Lateral inhibition in race-model architectures has recently been implicated in the absoluteintensity or absolute-value effect (Kirkpatrick et al, 2021;Ratcliff et al, 2018;Teodorerscu & Usher, 2013;Teodorescu et al, 2016). This is the finding that decisions about differ- still perform better than any of reduced versions of it for a majority of participants.…”