A drift diffusion model account of the semantic congruity effect in a classification paradigm

Abstract

The semantic congruity effect refers to the facilitation of judgements (i) when the direction of the comparison of two items coincides with the relative position of the items along the dimension comparison or (ii) when the relative size of a standard and a target stimulus coincides. For example, people are faster in judging 'which is bigger?' for two large items, than judging 'which is smaller?' for two large items (selection paradigm). Also, people are faster in judging a target stimulus as smaller when compared to a small standard, than when compared to a large standard, and vice versa (classification paradigm). We use the Drift Diffusion Model (DDM) to explain the time course of a semantic congruity effect in a classification paradigm. Formal modelling of semantic congruity allows the time course of the decision process to be described, using an established model of decision making. Moreover, although there have been attempts to explain the semantic congruity effect within evidence accumulation models, two possible accounts for the congruity effect have been proposed but their specific predictions have not been compared directly, using a model that could quantitatively account for both; a shift in the starting point of evidence accumulation or a change in the rate at which evidence is accumulated. With our computational investigation we provide evidence for the latter, while controlling for other possible explanations such as a variation in non-decision time or boundary separation, that have not been taken into account in the explanation of this phenomenon. When subjects are required to judge two stimuli that differ on a single contrastive polar continuum (e.g., 'big' vs.'small'), subjects are faster to judge which of the two stimuli is higher on that continuum, when the stimuli are high on that particular dimension, and they are faster to judge which of the two stimuli is lower on that continuum, when the stimuli are low on that particular dimension. Furthermore, when subjects are required to judge whether a target stimulus is bigger or smaller than a standard stimulus, subjects are faster when the relative size of the standard and of the target coincides (see Dehaene, 1989). Dehaene (1989) defined the first paradigm (i.e., chose the bigger/smaller of two stimuli) as a selection paradigm, and the second paradigm (i.e., is the target bigger/smaller than a standard) as a classification paradigm. The result that characterises these two paradigms is referred to as the semantic congruity effect. The semantic congruity effect has been replicated in perceptual and symbolic judgements across different domains, including surface area (Moyer & Bayer,

Semantic memory: A review of methods, models, and current challenges

“…However, process models operating on these underlying semantic representations have not received the same kind of attention and have developed somewhat independently from the representation modeling movement. For example, although process models like the drift-diffusion model (Ratcliff & McKoon, 2008), the optimal foraging model (Hills, 2006), and the temporal context model (Howard & Kahana, 2002) have been applied to some semantic tasks like verbal fluency (Hills, Jones, & Todd, 2012), free association (Howard, Shankar, & Jagadisan, 2011), and semantic judgments (e.g., Pirrone, Marshall, & Stafford, 2017), their application to different tasks remains limited and most research has instead focused on representational issues. Ultimately, combining process-based accounts with representational accounts is going to be critical in addressing some of the current challenges in the field, an issue that is emphasized in the final section of this review.…”

Individual differences in numerical comparison is independent of numerical precision

“…Other models of decision-making such as drift diffusion models are fairly successful for weighing evidence in two-alternative decision-making (Park & Starns, 2015;Pirrone, Marshall, & Stafford, 2017;Purcell et al, 2010).…”

Individual differences in children’s numerical comparison is independent of numerical precision

“…Other models of decision--making such as drift diffusion models are fairly successful for weighing evidence in two--alternative decision--making (Purcell et al, 2010;Park & Starns, 2015;Pirrone, Marshall, & Stafford, 2017). The current approach does not contradict a drift diffusion model; there are some similarities in implementation.…”