Adaptive Control Of Thought-Rational Model Research Articles

Driver behavior and mental workload for takeover safety in automated driving: ACT-R prediction modeling approach

Objective Conditional automated driving (SAE level 3) requires the driver to take over the vehicle if the automated system fails. The mental workload that can occur in these takeover situations is an important human factor that can directly affect driver behavior and safety, so it is important to predict it. Therefore, this study introduces a method to predict mental workload during takeover situations in automated driving, using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. The mental workload prediction model proposed in this study is a computational model that can become the basis for emerging crash avoidance technologies in future autonomous driving situations. Methods The methodology incorporates the ACT-R cognitive architecture, known for its robustness in modeling cognitive processes and predicting performance. The proposed takeover cognitive model includes the symbolic structure for repeatedly checking the driving situation and performing decision-making for takeover as well as Non-Driving-Related Tasks (NDRT). We employed the ACT-R cognitive model to predict mental workload during takeover in automated driving scenarios. The model’s predictions are validated against physiological data and performance data from the validation test. Results The model demonstrated high accuracy, with an r-square value of 0.97, indicating a strong correlation between the predicted and actual mental workload. It successfully captured the nuances of multitasking in driving scenarios, showcasing the model’s adaptability in representing diverse cognitive demands during takeover. Conclusions The study confirms the efficacy of the ACT-R model in predicting mental workload for takeover scenarios in automated driving. It underscores the model’s potential in improving driver-assistance systems, enhancing vehicle safety, and ensuring the efficient integration of human-machine roles. The research contributes significantly to the field of cognitive modeling, providing robust predictions and insights into human behavior in automated driving tasks.

Developing an Improved ACT-R Model for Pilot Situation Awareness Measurement

According to a previously built situation-awareness (SA) model based on attention allocation, with the ACT-R (Adaptive Control of Thought, Rational) theory for analyzing pilot cognitive processes for situation elements, an SA mathematical model was improved to predict pilot SA during exposure to different display interfaces and missions. An indicator-display monitoring task was performed under different experimental conditions for SA model verification, while the SA global assessment technique (SAGAT), performance measures, 10-dimensional SA rating technique (10-D SART), and eye movement measures were adopted to comprehensively assess the operator’s SA. The experimental results revealed that theoretical prediction values calculated using the improved SA model were strongly correlated with the operation performance, and thus confirming the model validation. The SAGAT was shown to be a more effective method than SART in this research, and the overall SAGAT accuracy rate, as well as the accuracy response time, are effective indices for SA measurement. Eye-movement indices, such as the fixation/saccade ratio, which corresponds to the mode of information perception and extraction, was examined to be sensitive to operator’s SA changes. The Advances of the improved SA model have been achieved in predicting and indicating SA by using human behaviors, including operation performance, SAGAT response behaviors, and visual behaviors. Thereby, it provides a new auxiliary tool for quantitative characterization of pilot’s SA during cockpit interface design optimization and ergonomic evaluation.

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation.

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.

Neural Correlates of Workload Transition in Multitasking: An ACT-R Model of Hysteresis Effect.

This study investigated the effect of task demand transitions at multiple levels of analysis including behavioral performance, subjective rating, and brain effective connectivity, while comparing human data to Adaptive Control of Thought-Rational (ACT-R) simulated data. Three stages of task demand were designed and performed sequentially (Low-High-Low) during AF-MATB tasks, and the differences in neural connectivity during workload transition were identified. The NASA Task Load Index (NASA-TLX) and the Instantaneous Self-Assessment (ISA) were used to measure the subjective mental workload that accompanies the hysteresis effect in the task demand transitions. The results found significant hysteresis effects on performance and various brain network measures such as outflow of the prefrontal cortex and connectivity magnitude. These findings would assist in clarifying the direction and strength of the Granger Causality under demand transitions. As a result, these findings involving the neural mechanisms of hysteresis effects in multitasking environments may be utilized in applications of neuroergonomics research. The ability to compare data derived from human participants to data gathered by the ACT-R model allows researchers to better account for hysteresis effects in neuro-cognitive models in the future.

EEG-based neural correlates of ACT-R model for multitasking

EEG-based neural correlates of ACT-R model for multitasking

Exploration in free word association networks: models and experiment

Free association is a task that requires a subject to express the first word to come to their mind when presented with a certain cue. It is a task which can be used to expose the basic mechanisms by which humans connect memories. In this work, we have made use of a publicly available database of free associations to model the exploration of the averaged network of associations using a statistical and the adaptive control of thought-rational (ACT-R) model. We performed, in addition, an online experiment asking participants to navigate the averaged network using their individual preferences for word associations. We have investigated the statistics of word repetitions in this guided association task. We find that the considered models mimic some of the statistical properties, viz the probability of word repetitions, the distance between repetitions and the distribution of association chain lengths, of the experiment, with the ACT-R model showing a particularly good fit to the experimental data for the more intricate properties as, for instance, the ratio of repetitions per length of association chains.

Modeling the effect of driving experience on lane keeping performance using ACT-R cognitive architecture

ACT-R (adaptive control of thought-rational) is a computational cognitive architecture that consists of multiple modules for perception and attention (e.g., a visual module), response making (e.g., a motor module), and mental information processing (e.g., a goal module, declarative memory module, and procedural memory module). The output of an ACT-R model is a time course that shows the coordination of the activation of these modules in solving a cognitive task. The research reported in this paper used a driver performance model developed in ACT-R to examine the effect of driving experience on driversperformance of vehicle lateral position control. First, we compared the lane keeping performance between novice and experienced drivers in a simulated driving experiment with different road, speed, and driving conditions. Behavioral data collected from both groups of drivers were then modeled in the ACT-R driving model to analyze the cognitive mechanisms of the behaviors. The modeling results were similar to the human results. There were two main findings. (1) Novice drivers had poor lateral control skills evidenced by significantly greater lateral deviation compared with experienced drivers. The lack of control was more evident on curved roads than on straight roads. On curved roads, the novice driversdriving paths significantly shifted to the outside of the curve, whereas there was no such shift for experienced drivers. (2) The cognitive-architecture-based modeling in ACT-R revealed the mental processes including parallel processing between modules and serial processing between sub-tasks. One of the factors responsible for the difference in curved-road driving paths between the two groups of drivers may be the location of visual attention focus. Our model showed that the behavioral difference in curved-road driving paths can be modeled by assigning a visual attention focus point to the novice-driver model that is closer than that assigned to the experienced-driver model. The method and results of this study can be used to provide cognitive-psychological bases for improving the effectiveness and efficiency of driver training courses and developing intelligent driving assistance systems. This research also provides an application example and a successful experience of computational cognitive modeling using cognitive architectures such as ACT-R.

Predictive Memory and the Surprising Gap

Predictive Memory and the Surprising Gap

Modeling fan effects on the time course of associative recognition

We investigated the time course of associative recognition using the response signal procedure, whereby a stimulus is presented and followed after a variable lag by a signal indicating that an immediate response is required. More specifically, we examined the effects of associative fan (the number of associations that an item has with other items in memory) on speed–accuracy tradeoff functions obtained in a previous response signal experiment involving briefly studied materials and in a new experiment involving well-learned materials. High fan lowered asymptotic accuracy or the rate of rise in accuracy across lags, or both. We developed an Adaptive Control of Thought-Rational (ACT-R) model for the response signal procedure to explain these effects. The model assumes that high fan results in weak associative activation that slows memory retrieval, thereby decreasing the probability that retrieval finishes in time and producing a speed–accuracy tradeoff function. The ACT-R model provided an excellent account of the data, yielding quantitative fits that were as good as those of the best descriptive model for response signal data.

ACT-R Model of EEG Latency Data

Anderson and Lebiere's (1998) modeling system ACT-R (Adaptive Control of Thought – Rational) has been a leading contributor to advances in cognitive science. Despite the modeling system's success there are areas in which it may be improved. The present research advocates a suggested approach to improving ACT-R's predictive capacity by using EEG (electroencephalography) latency data to predict the time it takes to achieve certain mental steps. A model is presented which successfully represents EEG data from a simple auditory experiment. Implications of this modeling approach to ACT-R and to the field of cognitive science are discussed.

Human Symbol Manipulation Within an Integrated Cognitive Architecture

This article describes the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture (Anderson et al., 2004; Anderson & Lebiere, 1998) and its detailed application to the learning of algebraic symbol manipulation. The theory is applied to modeling the data from a study by Qin, Anderson, Silk, Stenger, & Carter (2004) in which children learn to solve linear equations and perfect their skills over a 6-day period. Functional MRI data show that: (a) a motor region tracks the output of equation solutions, (b) a prefrontal region tracks the retrieval of declarative information, (c) a parietal region tracks the transformation of mental representations of the equation, (d) an anterior cingulate region tracks the setting of goal information to control the information flow, and (e) a caudate region tracks the firing of productions in the ACT-R model. The article concludes with an architectural comparison of the competence children display in this task and the competence that monkeys have shown in tasks that require manipulations of sequences of elements.

Task preparation and task repetition: Two-component model of task switching.

The switch cost (the disadvantage of performing a new task vs. a repeated task) has been attributed to lack of preparation for the switched task or priming of the repeated task. These sources were examined by manipulating foreknowledge of task transition (repeat or switch), response-to-stimulus interval (RSI), and practice level. Regardless of foreknowledge, the cost decreased with RSI and practice. The reduction was greater with foreknowledge than with no foreknowledge, and the amount of switch cost did not depend on foreknowledge. These results suggest that the switch cost with foreknowledge may consist of both inadequate preparation and repetition benefit but the switch cost with no foreknowledge may reflect repetition benefit only. An ACT-R (adaptive control of thought-rational) model was proposed, accommodating both preparation and priming effect with 2 independent processes: conflict resolution among productions and decay of chunk activation.

The fan effect: New results and new theories.

The fan effect (J. R. Anderson, 1974) has been attributed to interference among competing associations to a concept. Recently, it has been suggested that such effects might be due to multiple mental models (G. A. Radvansky. D. H. Spieler, & R. T. Zacks, 1993) or suppression of concepts (M. C. Anderson & B. A. Spellman, 1995; A. R. A. Conway & R. W. Engle, 1994). It was found that the Adaptive Control of Thought-Rational (ACT-R) theory, which embodies associative interference, is consistent with the results of G. A. Radvansky et al. and that there is no evidence for concept suppression in a new fan experiment. The ACT-R model provides good quantitative fits to the results, as shown in a variety of experiments. The 3 key concepts in these fits are (a) the associative strength between 2 concepts reflects the degree to which one concept predicts the other; (b) foils are rejected by retrieving mismatching facts; and (c) participants can adjust the relative weights they give to various cues in retrieval.