Spanning seven orders of magnitude: a challenge for

This article provides an overview of the EPIC architecture being developed by Kieras and Meyer for modeling human cognition and performance (Kieras, Wood, & Meyer, 1997; Meyer & Kieras, 1997a, 1997b). EPIC is similar in spirit to the Model Human Processor (MHP; Card, Moran, & Newell, 1983), but EPIC incorporates many recent theoretical and empiri- cal results about human performance in the form of a software framework for computer simulation modeling. Using EPIC, a model can be con- structed that represents the general procedures required to perform a complex multimodal task a a set of production rules. When the model is supplied with the external stimuli for a specffic task, it will then execute the procedures in whatever way the task requires, thus simulating a human 's performing the task and generating the predicted actions in simulated real time. EPIC is an architecture for constructing models of 394 performance. It is notet a learning system and so has no mechanisms for learning how to perform a task. Rather, the purpose of EPIC is to repre- sent in detail the perceptual, motor, and cognitive constraints on the human ability to perform tasks

Goal-directed cognition is often discussed in terms of specialized memory structures like the "goal stack." The goal-activation model presented here analyzes goal-directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means-ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed.

While researchers have made great strides in evaluating and comparing user interfaces using computational models and frameworks, their work has focused almost exclusively on interfaces that serve as the only or primary task for the user. This paper presents an approach to evaluating and comparing interfaces that users interact with as secondary tasks while executing a more critical primary task. The approach centers on the integration of two computational behavioral models, one for the primary task and another for the secondary task. The resulting integrated model can then execute both tasks together and generate a priori predictions about the effects of one task on the other. The paper focuses in particular on the domain of driving and the comparison of four dialing interfaces for in-car cellular phones. Using the ACT-R cognitive architecture (Anderson & Lebiere, 1998) as a computational framework, behavioral models for each possible dialing interface were integrated with an existing model of driver behavior (Salvucci, Boer, & Liu, in press). The integrated model predicted that two different manual-dialing interfaces would have significant effects on driver steering performance while two different voice-dialing interfaces would have no significant effect on performance. An empirical study conducted with human drivers in a driving simulator showed that while model and human performance differed with respect to overall magnitudes, the model correctly predicted the overall pattern of effects for human drivers. These results suggest that the integration of computational behavioral models provides a useful, practical method for predicting the effects of secondary-task interface use on primary-task performance.

this article are also gratefully acknowledged

Recent computational models of cognition have made good progress in accounting for the visual processes needed to encode external stimuli. However, these models typically incorporate simplified models of visual processing that assume a constant encoding time for all visual objects and do not distinguish between eye movements and shifts of attention. This paper presents a domain-independent computational model, EMMA, that provides a more rigorous account of eye movements and visual encoding and their interaction with a cognitive processor. The visual-encoding component of the model describes the effects of frequency and foveal eccentricity when encoding visual objects as internal representations. The eye-movement component describes the temporal and spatial characteristics of eye movements as they arise from shifts of visual attention. When integrated with a cognitive model, EMMA generates quantitative predictions concerning when and where the eyes move, thus serving to relate higher-level cognitive processes and attention shifts with lower-level eye-movement behavior. The paper evaluates EMMA in three illustrative domains — equation solving, reading, and visual search — and demonstrates how the model accounts for aspects of behavior that simpler models of cognitive and visual processing fail to explain.

Future research on cognitive control must precisely characterize the supervisory functions of executive mental processes. The achievement of this objective will be facilitated by formal concepts and algorithms from contemporary computer operating systems. In particular, operating-system fundamentals can help to advance work with the Executive-Process Interactive Control (EPIC) architecture, a theoretical framework for computational modeling of human multiple-task performance. EPIC models that incorporate general executive processes like those of operating systems provide insights about how people schedule tasks, allocate perceptual-motor resources, and coordinate task processes during multiple-task performance under both laboratory and real-world conditions. Such insights may lead to discoveries about the acquisition of procedural task knowledge and efficient multitasking skills.

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.

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. The role of implicit learning in skill acquisition and the distinction between implicit and explicit learning have been widely recognized in recent years (see, e.g., Cleeremans, Destrebecqz, &

We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model’s complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity.