Robert C. Mathews

Role of Implicit and Explicit Processes in Learning From Examples: A Synergistic Effect

ness of Implicit Knowledge Reber (1969, 1976) claims that implicit knowledge is abstract and readily generalizes to different symbol sets when

Insight without awareness: On the interaction of verbalization, instruction and practice in a simulated process control task

Four experiments in which subjects learned to control two versions of a complex simulated process control task show that verbalizable knowledge of procedures used to perform these tasks is very limited and is acquired late in learning. Individual learning curves associated with these tasks showed sudden improvements in performance, which were not accompanied by a similar increase in verbalizable knowledge. It was also found that verbal instructions consisting of exemplar memorization, strategies for rule induction, simple heuristics, and experts’ instructions were all effective in enhancing novice subjects’ performance. A theoretical framework is proposed in which subjects draw on two separate but interacting knowledge structures to perform these tasks. One knowledge structure is based on memory for past experiences (close analogies), and the other is based on ones current mental model of the task. Implicit sets of competing rules that control response selection are derived from both sources of knowledge. It is suggested that dissociations between task performance and verbalizing occur because memory-based processing tends to have more control over response selection because of its greater specificity, whereas a mental model tends to be the preferred mode for verbal reporting because of its greater accessibility.

Modeling meta-cognition in a cognitive architecture

This paper describes how meta-cognitive processes (i.e., the self monitoring and regulating of cognitive processes) may be captured within a cognitive architecture Clarion. Some currently popular cognitive architectures lack sufficiently complex built-in meta-cognitive mechanisms. However, a sufficiently complex meta-cognitive mechanism is important, in that it is an essential part of cognition and without it, human cognition may not function properly. We contend that such a meta-cognitive mechanism should be an integral part of a cognitive architecture. Thus, such a mechanism has been developed within the Clarion cognitive architecture. The paper demonstrates how human data of two meta-cognitive experiments are simulated using Clarion. The simulations show that the meta-cognitive processes represented by the experimental data (and beyond) can be adequately captured within the Clarion framework.

2007 Special Issue: The interaction of implicit learning, explicit hypothesis testing learning and implicit-to-explicit knowledge extraction

To further explore the interaction between the implicit and explicit learning processes in skill acquisition (which have been tackled before, e.g. in [Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skill to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, 25(2), 203-244; Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, 112(1), 159-192]), this paper explores details of the interaction of different learning modes: implicit learning, explicit hypothesis testing learning, and implicit-to-explicit knowledge extraction. Contrary to the common tendency in the literature to study each type of learning in isolation, this paper highlights the interaction among them and various effects of the interaction on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit learning processes; moreover, it also uniquely embodies a bottom-up (implicit-to-explicit) learning approach in addition to other types of learning. The paper shows that this model accounts for various effects in the human behavioural data from the psychological experiments with the process control task, in addition to accounting for other data in other psychological experiments (which has been reported elsewhere). The paper shows that to account for these effects, implicit learning, bottom-up implicit-to-explicit extraction and explicit hypothesis testing learning are all needed.

The role of explicit and implicit learning processes in concept discovery

Analysis of individual learning curves and concurrent verbal protocols from three experiments concerning discovery of a non-salient verbal concept and a pictorial analogue (Chinese ideograph) of the concept show that a substantial transition phase occurs in which discrimination of exemplars from non-exemplars of the concept is above chance but not yet asymptotic. Under most conditions the ability to verbalize knowledge of the concept occurred almost simultaneously with the onset of the transition phase. However, the addition of noise in the form of false feedback (Experiment 3) created a temporary dissociation between task performance and verbalizable knowledge. Additional results suggest that individual hypothesis revision/rejection strategies affect the length of the transition phase of learning, whereas the size of the domain of hypotheses being sampled affects the number of trial blocks before the transition phase begins. The effect of feedback error on the relation between early rates of hypothesis generation and subsequent transition phase length also suggests that a strategy of quick rejection of falsified hypotheses becomes less adaptive in noisy task environments (e.g. when there are many exceptions to a rule or the concept is probabilistic). Finally, failure to find effects of variables known to affect implicit learning suggests that implicit learning processes do not play a large role in the discovery of this type of concept.

2009 Special Issue: A motivationally-based simulation of performance degradation under pressure

The CLARION cognitive architecture has been shown to be capable of simulating and explaining a wide range of psychological tasks and data. Currently, two theories exist to explain the psychological phenomenon of performance degradation under pressure: the distraction theory and the explicit-monitoring theory. However, neither provides a detailed mechanistic explanation of the exact processes involved. We propose such a detailed theory within the CLARION cognitive architecture that takes into account motivation and the interaction between explicit and implicit processes. We then use our theory to provide a plausible explanation of some existing data. The data are simulated using the theory within the CLARION cognitive architecture.

Effects of three types of repetition on cued and noncued recall of words

Three list-item memory experiments explored the effects of three types of repetition of a basic unit consisting of two words in an explicitly designated conceptual category. The types were repetition of the complete basic unit, repetition of the category name with two new instances, and repetition of the category name only. All types of repetition showed a large facilitative effect on recall of the basic unit. The effect was considerably larger when repetitions were spaced rather than massed, and it manifested itself mainly in enhanced accessibility of higher-order units (categories) rather than access to elements within these units (words within accessible categories). The results as a whole suggest that the structure of the higher-order memory units is hierarchical, with access to the unit possible only through its control element.

An Iterative Assessment Approach to Improve Technology Adoption and Implementation Decisions by Healthcare Managers 1

The U.S. healthcare industry spends over 36 billion dollars annually on information technology (Frost & Sullivan, 2004). Fueling this economy is an increasingly competitive healthcare market and the perception that new technologies will add value by cutting costs, saving time, improving workflow efficiency, and reducing medical errors. Implementing a new medical informatics solution is a difficult, time consuming, and expensive process, and is only worth undertaking if that added value will be realized. Although ITmanagers innearly all industries are under increased pressure to deliver technology solutions which provide return on investment (ROI), managers in healthcare settings have a number of unique problems. For example, hospital administrators must be as concerned with patient care outcomes as they are with traditional financial metrics of success. Further, healthcare IT is often designed to be used in the delivery of patient care, and the IT users are highly skilled professionals with complex skill sets. Errors that occur in healthcare delivery can be particularly consequential in terms of their effect on patients’ health, and the financial well-being of the institution (e.g., from medical malpractice suits). Although these errors certainly occur in the absence of IT (Institute of Medicine, 1999), they can also occur when new technologies disrupt the workflow of healthcare professionals through lack of familiarity with the technology, or simplybecause it is poorlydesigned. This is somewhat ironic, as the introduction of IT has been billed as one of the primary ways to reduce medical errors (e.g., Institute of Medicine, 2006). Thus, healthcare IT managers face the daunting task of choosing and implementing technology solutions which are reliable, costeffective, and improve the quality of healthcare delivery, while introducing technology in a manner that fits the complex workflow IN THIS ISSUE

Is research painting a biased picture of implicit learning? The dangers of methodological purity in scientific debate

The properties of implicit learning in natural settings are contrasted with those found in research. It is suggested that the search for pure cases and the necessity of finding features that clearly discriminate the two types of learning lead to bias in our estimation of the power of implicit processes. In more natural settings, such as face recognition, object perception, and natural language processing, implicit processes operate with flexibility and adapt to changes in environmental conditions. It is suggested that the search for pure cases of implicit processes has led to focusing on relevant but atypical examples of these processes. Additional research that emphasizes high levels of skill in control of complex systems may reveal greater adaptive power of implicit processes. However, such research may require less methodological purity and more emphasis on synthesis of theoretical ideas rather than analysis into pure cases.

Developing rich and quickly accessed knowledge of an artificial grammar

In contrast to prior research, our results demonstrate that it is possible to acquire rich, highly accurate, and quickly accessed knowledge of an artificial grammar. Across two experiments, we trained participants by using a string-edit task and highlighting relatively low-level (letters), medium-level (chunks), or high-level (structural; i.e., grammar diagram) information to increase the efficiency of grammar acquisition. In both experiments, participants who had structural information available during training generated more highly accurate strings during a cued generation test than did those in other conditions, with equivalent speed. Experiment 2 revealed that structural information enhanced acquisition only when relevant features were highlighted during the task using animation. We suggest that two critical components for producing enhanced performance from provided model-based knowledge involve (1) using the model to acquire experience-based knowledge, rather than using a representation of the model to generate responses, and (2) receiving that knowledge precisely when it is needed during training.