Online research in philosophy

Previous research suggests that early performance of amnesic individuals in a probabilistic category learning task is relatively unimpaired. When combined with impaired declarative knowledge, this is taken as evidence for the existence of separate implicit and explicit memory systems. The present study contains a more fine-grained analysis of learning than earlier studies. Using a dynamic lens model approach with plausible learning models, we found that the learning process is indeed indistinguishable between an amnesic and control group. However, in contrast to earlier findings, we found that explicit knowledge of the task structure is also good in both the amnesic and the control group. This is inconsistent with a crucial prediction from the multiple-systems account. The results can be explained from a single system account and previously found differences in later categorization performance can be accounted for by a difference in learning rate.

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.

Running head: Implicit sequence learning ABSTRACT Can we learn without awareness? Although this issue has been extensively explored through studies of implicit learning, there is currently no agreement about the extent to which knowledge can be acquired and projected onto performance in an unconscious way. The controversy, like that surrounding implicit memory, seems to be at least in part attributable to unquestioned acceptance of the unrealistic assumption that tasks are process-pure, that is, that a given task exclusively involves either implicit or explicit knowledge.

Jackson and Jackson (1995) argue that most current tests used to assess awareness of sequential material are flawed because of their emphasis on accuracy. They propose to distinguish two forms of sequence knowledge: Serial knowledge, that is, knowledge about the specific sequence that stimuli follow, which involves information about the statistical relationship between many sequence elements, and statistical knowledge, or knowledge about the probability of different transitions between adjacent sequence elements. Further, they suggest a new method to analyze generation performance, which involves considering the correlation between subjects' responses and the distribution of transition probabilities, regardless of the accuracy of generation performance. In this comment, we first suggest that the distinction between serial and statistical knowledge is unwarranted except in one case which is not addressed by Jackson and Jackson. We propose instead that all sequence knowledge is essentially statistical in nature. Second, we suggest that using probabilistic instead of deterministic sequences is a better way to approach the assessment of explicit knowledge, and illustrate this contention with empirical and simulated examples based on previous and current research (Cleeremans, 1993; Cleeremans and McClelland, 1991; Jimenez, Mendez and Cleeremans.

The ability to process events in their temporal and sequential context is a fundamental skill made mandatory by constant interaction with a dynamic environment. Sequence learning studies have demonstrated that subjects exhibit detailed — and often implicit — sensitivity to the sequential structure of streams of stimuli. Current connectionist models of performance in the so-called Serial Reaction Time Task (SRT), however, fail to capture the fact that sequence learning can be based not only on sensitivity to the sequential associations between successive stimuli, but also on sensitivity to the associations between successive responses, and on the predictive relationships that exist between these sequences of responses and their effects in the environment. In this paper, we offer an initial exploration of an alternative architecture for sequence learning, based on the principles of Forward Models.

Jackson and Jackson (1995) argue that most current tests used to assess awareness of sequential material are flawed because of their emphasis on accuracy. They propose to distinguish two forms of sequence knowledge: Serial knowledge, that is, knowledge about the specific sequence that stimuli follow, which involves information about the statistical relationship between many sequence elements, and statistical knowledge, or knowledge about the probability of different transitions between adjacent sequence elements. Further, they suggest a new method to analyze generation performance, which involves considering the correlation between subjects' responses and the distribution of transition probabilities, regardless of the accuracy of generation performance. In this comment, we first suggest that the distinction between serial and statistical knowledge is unwarranted except in one case which is not addressed by Jackson and Jackson. We propose instead that all sequence knowledge is essentially statistical in nature. Second, we suggest that using probabilistic instead of deterministic sequences is a better way to approach the assessment of explicit knowledge, and illustrate this contention with empirical and simulated examples based on previous and current research (Cleeremans, 1993; Cleeremans and McClelland, 1991; Jimenez, Mendez and Cleeremans, 1996).

Comparing the relative sensitivity of direct and indirect measures of learning is proposed as the best way to provide evidence for unconscious learning when both conceptual and operative definitions of awareness are lacking. This approach was first proposed by Reingold & Merikle (1988) in the context of subliminal perception. In this paper, we apply it to a choice reaction time task in which the material is generated based on a probabilistic finite-state grammar (Cleeremans, 1993). We show (1) that participants progressively learn about the statistical structure of the stimulus material over training with the choice reaction time task, and (2) that they can use some of this knowledge to predict the location of the next stimulus in a subsequent “generation” task. However, detailed partial correlational analyses of the correspondence between performance during the reaction time task and the statistical structure of the training material showed that large effects remained even when controlling for explicit knowledge as assessed by the generation task. Hence we conclude (1) that at least some of the knowledge expressed through reaction time performance can not be characterized as conscious, and (2) that even when associations are found at a global level of analysis, dissociations can still be obtained when more detailed analyses are conducted. Finally, we also show that participants are limited in the depth of the contingencies they can learn about, and that these limitations are shared by the Simple Recurrent Network model of Cleeremans & McClelland (1991).

Comparing the relative sensitivity of direct and indirect measures of learning is proposed as the best way to provide evidence for unconscious learning when both conceptual and operative definitions of awareness are lacking. This approach was first proposed by Reingold & Merikle (1988) in the context of subliminal perception. In this paper, we apply it to a choice reaction time task in which the material is generated based on a probabilistic finite-state grammar (Cleeremans, 1993). We show (1) that participants progressively learn about the statistical structure of the stimulus material over training with the choice reaction time task, and (2) that they can use some of this knowledge to predict the location of the next stimulus in a subsequent “generation” task. However, detailed partial correlational analyses of the correspondence between performance during the reaction time task and the statistical structure of the training material showed that large effects remained even when controlling for explicit knowledge as assessed by the generation task. Hence we conclude (1) that at least some of the knowledge expressed through reaction time performance can not be characterized as conscious, and (2) that even when associations are found at a global level of analysis, dissociations can still be obtained when more detailed analyses are conducted. Finally, we also show that participants are limited in the depth of the contingencies they can learn about, and that these limitations are shared by the Simple Recurrent Network model of Cleeremans & McClelland (1991).

Comparing the relative sensitivity of direct and indirect measures of learning is proposed as the best way to provide evidence for unconscious learning when both conceptual and operative definitions of awareness are lacking. This approach was first proposed by Reingold & Merikle (1988) in the context of subliminal perception. In this paper, we apply it to a choice reaction task in which the material is generated based on a probabilistic finite-state grammar (Cleeremans, 1993). We show (1) that subjects progressively learn about the statistical structure of the stimulus material over training with the choice reaction task, and (2) that they can use some of this knowledge to predict the location of the next stimulus in a subsequent explicit prediction task. However, detailed partial correlational analyses of the correspondence between CRT performance and the conditional probability of each stimulus showed that large effects remained even when controlling for explicit knowledge as assessed by the prediction task. Hence we conclude (1) that at least some of the knowledge expressed in CRT performance can not be characterized as conscious, and (2) that even when associations are found at a global level of analysis, dissociations can still be obtained when more detailed analyses are conducted. Finally, we also show that..

In two H215O PET scan experiments, we investigated the cerebral correlates of explicit and implicit knowledge in a serial reaction time (SRT) task. To do so, we used a novel application of the Process Dissociation Procedure, a behavioral paradigm that makes it possible to separately assess conscious and unconscious contributions to performance during a subsequent sequence generation task. To manipulate the extent to which the repeating sequential pattern was learned explicitly, we varied the pace of the choice reaction time task—a variable that is known to have differential effects on the extent to which sensitivity to sequence structure involves implicit or explicit knowledge. Results showed that activity in the striatum subtends the implicit component of performance during recollection of a learned sequence, whereas the anterior cingulate/mesial prefrontal cortex (ACC/MPFC) supports the explicit component. Most importantly, we found that the ACC/MPFC exerts control on the activity of the striatum during retrieval of the sequence after explicit learning, whereas the activity of these regions is uncoupled when learning had been essentially implicit. These data suggest that implicit learning processes can be successfully controlled by conscious knowledge when learning is essentially explicit. They also supply further evidence for a partial dissociation between the neural substrates supporting conscious and nonconscious components of performance during recollection of a learned sequence.