Double responding: A new constraint for models of speeded decision making

Abstract

Evidence accumulation models (EAMs) have become the dominant models of speeded decision making, which are able to decompose choices and response times into cognitive parameters that drive the decision process. Several models within the EAM framework contain fundamentally di↵erent ideas of how the decision making process operates, though previous assessments have found that these models display a high level of mimicry, which has hindered the ability of researchers to contrast these di↵erent theoretical viewpoints.Our study introduces a neglected phenomenon that we term "double responding", which can help to further constrain these models. We show that double responding produces several interesting benchmarks, and that the predictions of di↵erent EAMs can be distinguished in standard experiment paradigms when they are constrained to account for the choice response time distributions and double responding behaviour in unison. Our findings suggest that lateral inhibition (e.g., the leaky-competing accumulator) provides models with a universal ability to make accurate predictions for these data. Furthermore, only models containing feed-forward inhibition (e.g., the di↵usion model) performed poorly under both of our proposed extensions of the standard EAM framework to double responding, suggesting a general inability of feed-forward inhibition to accurately predict these data.We believe that our study provides an important step forward in further constraining models of speeded decision making, though additional research on double responding is required before broad conclusions are made about which models provide the best explanation of the underlying decision-making process.

The Leaky Integrating Threshold and its impact on evidence accumulation models of choice response time (RT).

“…For example, consider the case of change-of-mind decisions or double responses (Evans, Dutilh, Wagenmakers, & van der Maas, 2020; Resulaj et al, 2009). Because the LIT is in effect a separate motor accumulator, resetting it after a motor action has been triggered does not reset the primary evidence accumulator feeding into it.…”

“…The traditional modeling approach for secondary responses in simple threshold systems, also consists of continuing the primary evidence accumulation process after the first decision has been made and monitoring the alternative boundary (Evans et al, 2020), sometimes accompanied by an additional change to the threshold value for this boundary (Resulaj et al, 2009). Although this approach can account for mostly corrective second responses and fewer double responses when decision accuracy is emphasized (two important qualitative features that are observed in this context), it still cannot account for the experimentally observed right skew in the double response times (the time between the first and second response; Evans et al, 2020). The architecture of the LIT, however, allows the model to properly account for all three observed features.…”

Uncovering the Cognitive Mechanisms Underlying the Gaze Cueing Effect

“…We chose this Bayesian approach because Bayesian methods can provide a better ability to traverse complex likelihood spaces and avoid local maxima through moving both up and down the likelihood surface (as found in Evans, Dutilh, et al, 2020).…”

Modified leaky competing accumulator model of decision making with multiple alternatives: the Lie-algebraic approach

“…Among the current models of decision making, the Leaky Competing Accumulator (LCA) model 1 – 6 has become fairly popular recently because it has been shown to account for a variety of behavioural datasets (mostly) related to two alternatives. In accordance with the model, evidence accumulation continues until an accumulator reaches a certain threshold level of activation, and a decision is made.…”

“…Hence, model-fitting procedures are extremely slow, and a thorough investigation of model-fitting procedures, recoverability and identifiability of the LCA model has not been performed for multi-alternative cases 3 . Moreover, recent research has found that the LCA model suffers from an instability problem in parameter recovery studies so that inferences made directly on the estimated parameter values are unreliable and of little meaning when applied to real data 3 – 6 .…”