Two studies examined the use of think aloud (TA) protocol as a means for collecting data of cognitive processes during performance in golf. In study 1, TA was employed to examine if different verbalisation (Level 2 or Level 3 TA) instructions influence performance of high and low skilled golfers. Participants performed 30 putts using TA at either Level 2, Level 3, or no verbalization condition. Although Level 3 verbalization produced a higher volume of verbal data than Level 2, TA at (...) either level 2 or 3 did not impair putting performance compared to no verbalization. Study 2 examined the congruence between data collected via TA at Level 3 and cued retrospective recall of cognitive processes during golf performance. Experienced golfers performed six holes of golf whilst engaging in Level 3 TA. After performance, three semi-structured retrospective interviews were conducted (ten minutes after performance, 24 hours after performance and 48 hours after performance). A comparison of the themes identified large discrepancies between the information reported during TA and at interview, with only 38-41% similarity in variables reported to influence decision making on each hole. Both studies suggest TA is a valuable method for recording cognitive processes of individuals during task performance. TA provides richer verbal data regarding decisions than cued retrospective recall, and TA does not negatively impact performance. (shrink)

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. (PsycINFO Database Record (c) 2012 APA, all rights reserved). (shrink)

In his book, On Reflection, Hilary Kornblith criticizes what he regards as a chronic tendency in philosophy towards inflating the significance of reflection in ways that manifest a combination of philosophical naiveté and scientific ignorance about how reflection actually works. In these comments, I respond to Kornblith's challenge by sketching an account of the philosophical significance of reflection in the theory of epistemic justification.

In this paper I articulate a view of doxastic control that helps defend the legitimacy of our practice of blaming people for their beliefs. I distinguish between three types of doxastic control: intention-based, reason-based, and influence-based. First I argue that, although we lack direct intention-based control over our beliefs, such control is not necessary for legitimate doxastic blame. Second, I suggest that we distinguish two types of reason-responsiveness: sensitivity to reasons and appreciation of reasons. I argue that while both capacities (...) are necessary for satisfying the control condition, neither is sufficient. Finally, I defend an influence-based view of doxastic control according to which we have the capacity to execute intentions to engage in reflection that causally influences our beliefs in positive epistemic ways. This capacity is both necessary and sufficient for satisfying the control condition for legitimate doxastic blame. I end by defending the view from two objections: that reflection is not necessary for meeting the control condition, and that it is not sufficient. (shrink)

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...) and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects. (shrink)

When asked to explain their solutions to a problem, both adults and children gesture as they talk. These gestures at times convey information that is not conveyed in speech and thus reveal thoughts that are distinct from those revealed in speech. In this study, we use the classic Tower of Hanoi puzzle to validate the claim that gesture and speech taken together can reflect the activation of two cognitive strategies within a single response. The Tower of Hanoi is a well‐studied (...) puzzle, known to be most efficiently solved by activating subroutines at theoretically defined choice points. When asked to explain how they solved the Tower of Hanoi puzzle, both adults and children produced significantly more gesture‐speech mismatches—explanations in which speech conveyed one path and gesture another—at these theoretically defined choice points than they produced at non‐choice points. Even when the participants did not solve the problem efficiently, gesture could be used to indicate where the participants were deciding between alternative paths. Gesture can, thus, serve as a useful adjunct to speech when attempting to discover cognitive processes in problem‐solving. (shrink)

The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (shrink)

The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (shrink)

We respond to Morris and Richardson 's claim that Pickering and Chater's arguments about the lack of a relation between cognitive science and folk psychology are flawed. We note that possible controversies about the appropriate uses for the two terms do not affect our arguments. We then address their claim that computational explanation of knowledge-rich processes has proved possible in the domains of problem solving, scientific discovery, and reasoning. We argue that, in all cases, computational explanation is only possible for (...) aspects of those processes that do not make reference to general knowledge. We conclude that consideration of the issues raised by Morris and Richardson reinforces our original claim that there are two fundamentally distinct projects for understanding the mind, one based on justification, and the other on computational explanation, and that these apply to non-overlapping aspects of mental life. (shrink)

In a series of works Sosa (in: Knowledge in perspective, Cambridge University Press, Cambridge, 1991; A virtue epistemology: apt belief and reflective knowledge, Oxford University Press, Oxford, 2007; Reflective knowledge: apt belief and reflective knowledge, Oxford University Press, Oxford, 2009; ‘How Competence Matters in Epistemology’, Philos Perspect 24(1):465–475, 2010; Knowing full well, Princeton University Press, Princeton, 2011; Judgment and agency, Oxford University Press, Oxford, 2015; Epistemology, Princeton University Press, Princeton, 2017) has defended the view that there are two kinds or (...) ‘grades’ of knowledge, animal and reflective. One of the most persistent critics of Sosa’s attempts to bifurcate knowledge is Kornblith (in: Greco (ed) Ernest sosa and his critics, Wiley, Hoboken, 2004; ‘Sosa in Perspective’, Philos Stud 144(1):127–136, 2009; On reflection, Oxford University Press, Oxford, 2012). Our aim in this paper is to outline and evaluate Kornblith’s criticisms. We will argue that, while they raise a range of difficult (exegetical and substantive) questions about Sosa’s ‘bi-level’ epistemology, Sosa has the resources to adequately respond to all of them. Thus, this paper is a (qualified) defence of Sosa’s bi-level epistemology. (shrink)

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving (...) practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use. (shrink)

Recent proposals for a return to introspective methods make it necessary to review the central problems that led psychologists to abandon those methods as sources of scientific data in the early twentieth century. These problems and other related challenges to verbal reports collected during the cognitive revolution during the 1960s and 1970s were discussed in Ericsson and Simon's proposal for a theoretically motivated procedure to elicit valid and non- reactive concurrent verbalization of thoughts while subjects were performing tasks. The same (...) proposal explains why other verbal reports, such as introspections, detailed descriptions or explanations, require additional cognitive activity that often leads to reactivity and invalid reports. Finally, a new proposal is sketched for how the generation of introspective reports might be incorporated within a framework for non-reactive and valid verbalization of thoughts. (shrink)

This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its effects on learning. The work presents an (...) integrated model of skill learning that takes into account both implicit and explicit processes and both top-down and bottom-up learning. We examine and simulate human data in the Tower of Hanoi task. The paper shows how the quantitative data in this task may be captured using either top-down or bottom-up approaches, although top-down learning is a more apt explanation of the human data currently available. These results illustrate the two different directions of learning (top-down versus bottom-up), and thereby provide a new perspective on skill learning. Ó 2003 Elsevier B.V. All rights reserved. (shrink)