The paper introduces the Linear Ballistic Accumulator (LBA) model, a simplified version of the Linear Ballistic Accumulator (LBA) model, which is proposed to explain choice response time (RT) in decision-making tasks. The LBA model is characterized by independent accumulators that race towards a common response threshold, with activity in these accumulators increasing linearly and deterministically. This simplicity allows for complete analytic solutions for choices between any number of alternatives, making the model easy to apply to both binary and multiple-choice situations. The authors demonstrate that the LBA model successfully accommodates empirical phenomena from binary and multiple-choice tasks that have proven difficult for other theoretical accounts. They compare the LBA model with other models, highlighting its advantages in terms of simplicity and the ability to account for various empirical phenomena, such as RT distribution shape, speed-accuracy tradeoffs, and the relative speed of correct vs. incorrect responses. The LBA model is further tested using data from five previously published experiments, including lexical decision experiments, a brightness discrimination experiment, and a multiple-choice task. The results show that the LBA model accurately predicts response times and probabilities, even in complex scenarios involving fast and slow errors, speed-accuracy tradeoffs, and multiple-choice tasks. The model's ability to provide simple analytic solutions for choices between any number of alternatives is highlighted as a significant advantage over other models of choice RT.The paper introduces the Linear Ballistic Accumulator (LBA) model, a simplified version of the Linear Ballistic Accumulator (LBA) model, which is proposed to explain choice response time (RT) in decision-making tasks. The LBA model is characterized by independent accumulators that race towards a common response threshold, with activity in these accumulators increasing linearly and deterministically. This simplicity allows for complete analytic solutions for choices between any number of alternatives, making the model easy to apply to both binary and multiple-choice situations. The authors demonstrate that the LBA model successfully accommodates empirical phenomena from binary and multiple-choice tasks that have proven difficult for other theoretical accounts. They compare the LBA model with other models, highlighting its advantages in terms of simplicity and the ability to account for various empirical phenomena, such as RT distribution shape, speed-accuracy tradeoffs, and the relative speed of correct vs. incorrect responses. The LBA model is further tested using data from five previously published experiments, including lexical decision experiments, a brightness discrimination experiment, and a multiple-choice task. The results show that the LBA model accurately predicts response times and probabilities, even in complex scenarios involving fast and slow errors, speed-accuracy tradeoffs, and multiple-choice tasks. The model's ability to provide simple analytic solutions for choices between any number of alternatives is highlighted as a significant advantage over other models of choice RT.