ACT-R is a hybrid cognitive architecture. It is comprised of a set of programmable information processing mechanisms that can be used to predict and explain human behavior including cognition and interaction with the environment. We start by reviewing its history, which shapes its current form, contrasts and relates it to other architectures, and helps readers to anticipate where it is going. Based on this history, we then describe it as a theory of cognition that is realized as a computer program. After this, we briefly discuss tools for working with ACT-R, and also note several major accomplishments that have been gained by working with ACT-R in both basic and applied science, including summarizing some of the insights about human behavior. We conclude by discussing its future, which we believe will include adding emotions and physiology, increasing usability, and the use of nongenerative models.There are numerous reviews of ACT-R, many noted here. Therefore, in this study in addition to describing it as a theory, we start by briefly reviewing its history, which explains its current form and helps readers to anticipate where ACT-R is going. Similar to most reviews of cognitive architectures, we describe the theory and its structure (see Box 1 for key concepts), and how these are realized as a running computer program; we also briefly discuss tools for getting started and working with ACT-R and major accomplishments in both the scientific and applied science areas. This includes summarizing some of the insights about human behavior that have been gained by working with ACT-R. We conclude by discussing its future, which we believe will include emotions and physiology, usability, and the use of nongenerative models. We include explicit lessons for other architectures, but there are many implicit lessons as well. | HISTORYThe history of ACT-R is worth reviewing briefly for several reasons. First, the predecessors and previous iterations of the theory continue to shape ACT-R's current form. Second, it shows ACT-R's evolution from early theories of cognition and the influence of contemporaneous cognitive architectures (see Box 2 for types). Third, we hope the history will help readers and future researchers anticipate where ACT-R is going. Finally, the progression shows how cognitive architectures can evolve. The progression from HAM to ACT-R 7, as well as research that influenced ACT-R, is summarized in Figure 1.Every broad simulation tool has innate strengths attributable to its base simulation's original purpose (i.e., the phenomena that were modeled). For example, the HumMod physiology simulation was initially developed from a heart simulation before being developed into a unified model of human physiology "from birth to death" (Hester et al., 2011). HumMod's heart simulation is its most developed module. ACT-R fits this paradigm as well: ACT-R began as a model of human memory before being developed into a unified theory of cognition. As such, ACT-R is strongest when modeling memory.Work based on the ACT-R archit...
In this study, we plan to answer the fundamental question of what factors affect the human utility function and decision-making strategy. Utility function is an internally assigned value to each state to reflect the satisfaction of moving to that state. Decision Time (i.e., Reaction Time) is the time required for a user to make a decision after observing the current state. The assessment of human decision-making and Decision time has been frequently discussed in the fields such as psychology, neuroscience, and ergonomics.One of the most commonly used experiments to analyze the decision-making process is the choice task, where a set of choices are presented to users, and they need to select one of these choices. For the purpose of this study, we consider only two choices and assign a probabilistic reward to each choice. The task is named “Bias Coin Flip Game”, a web-based coin flip game where one side of the coin is more likely to appear. In another word, the coin is biased. Users are not aware of this bias and are asked to win as much as they can in the course of 250 tries. Probability Learning studies have indicated that after a sufficient number of tries, people are capable of learning the bias. However, the number of tries needed to learn the bias, the time spent between each try (e.g., Decision Time), and the strategy (e.g., matching and maximizing) users would choose to follow are highly susceptible to the visual cues represented to users. We consider multiple factors such as (a) the hidden/unhidden Win rate, (b) showing four last recent coin results, and (c) the order of visual cues. We analyze the effect of these cues on decision-making strategy and decision-making time on different genders and age groups using Factorial ANOVA (i.e., a statistical experimental design to analyze the significance of each cue). Results indicate how each visual cue affects the decision-making strategy chosen by users to design an environment that optimizes the chance of the optimality of the decisions made by the user, avoids convergence to suboptimal strategies, and controls reflection on the utility function. Finally, we suggest the relationship between the complexity of the utility function and the decision time for each environment with different sets of visual cues.
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