The Go/No-Go paradigm is being used in various clinical applications frequently. Besides, the sequential sampling models have achieved much attention for modeling the underlying processes of decision making. One of the most successful sequential sampling models that is used also for modeling the Go/No-Go paradigm is the drift diffusion model (DDM). The major assumption of the drift diffusion model is that the process of information accumulation is terminated by reaching the boundaries. But this assumption has been argued during the last years and some studies have confirmed that the accumulation process continues after making a decision (i.e. accumulator reaches the boundaries). The main constraint of the drift diffusion model for modeling the Go/No-Go paradigm is that it can not capture the ongoing information accumulation after making a decision. This is important in the Go/No-Go paradigm because when the participants decide to no-go they should wait until finishing the trial and it is a good time for continuing the information accumulation. In this paper, we are going to develop a variation of the drift diffusion model which is able to capture continuing the information accumulation after making a no-go decision. The developed model is based on substituting the lower boundary with a reflecting boundary. This paper aims to introduce an alternative model for Go/No-Go paradigm and after presenting a theoretical discussion on the model behavior, mean first passage time, and the first passage time distribution of the model, it is compared with the previous DDM model.

In many real-life situations, we make decisions between a defined set of options, which can be either discrete (as when deciding between going on driving and stopping the car) or continuous (as when stirring the wheel, the possible range of angles goes from $-30$ to $30$ degrees). However, most computational models for decision-making focus on decisions between a discrete set of options. While there are a few sequential sampling models that can explain behavioral patterns (i.e., choices and response times) of decisions in a continuous option space (i.e., the CDM and the SCDM), these models have a few limitations. For example, these models assume no leakage in the evidence accumulation process and no spatial inhibition (i.e., inhibition among different areas of the option space depending on their distance to each other). In this paper, we propose a novel sequential sampling model based on an existing computational model (i.e., the leaky competing accumulator model) for decisions in a continuous option space. Our proposed model includes leakage and spatial inhibition and is thus more biologically plausible.

The impact of visual attention on choice processes has been established over the last decades. Several studies are consistent with the view that visual attention increases the subjective value of the attended option. However, a few computational models have been proposed to investigate how attention and subjective values interact in multi-attribute choices. Moreover, these models disagree in terms of whether value is modulated by attention additively or multiplicatively. The additive theory states that the boost up subjective value depends only on gaze duration, and gaze on an option magnifies the subjective value at a constant rate. On the other hand, the multiplicative theory assumes that the magnitude of the attention-driven boost is value-dependent, and gazing at a high-value option yields a more significant boost in subjective value. Although there is a long debate on these two theories, recent studies have shown that both additive and multiplicative interactions between subjective value and gaze time may be essential for explaining empirical data and have suggested hybrid theories. For multi-attribute decisions, however, extant attentional models only consider the multiplicative interaction. This work introduces a new computational framework to account for attention in multi-attribute decisions. Our model assumes a hybrid attentional mechanism for the interaction between subjective values and gaze duration. We have tested the model on four datasets from various domains (e.g., clothing/brand, food/nutrition, food bundle, and money risk tasks). The results from the nested model comparison show that the proposed hybrid model works better than the other computational models.

Lexical Decision Task is one of the most widely used experimental paradigms for studying word recognition that allows researchers to make inferences about lexical processing and lexical representations of words and non-words. Evidence Accumulation Models (EAM) have been successfully used to model this task. Despite the accurate prediction of participants’ reaction time and accuracy, these models lack a mechanism for representing the lexical features of words and non-words. Incorporating lexical features directly into an EAM can open up new and better ways to study lexical processing and lexical representations. For this purpose, we developed two models by combining FastText and BERT models, with the race-diffusion model. In this framework, representations of words and non-words are generated by FastText or BERT models and transformed into the race-diffusion model’s drift rate. Results show that a combination of FastText and race diffusion model is a promising approach for modeling the lexical decision task.