Implicit learning of a recursive rule in an artificial grammar

Participants performed an artificial grammar learning task, in which the standard finite state grammar (J. Verb. Learn. Verb. Behavior 6 (1967) 855) was extended with a recursive rule generating self-embedded sequences. We studied the learnability of such a rule in two experiments. The results verify the general hypothesis that recursivity can be learned in an artificial grammar learning task. However this learning seems to be rather based on recognising chunks than on abstract rule induction. First, performance was better for strings with more than one level of self-embedding in the sequence, uncovering more clearly the self-embedding pattern. Second, the infinite repeatability of the recursive rule application was not spontaneously induced from the training, but it was when an additional cue about this possibility was given. Finally, participants were able to verbalise their knowledge of the fragments making up the sequences-especially in the crucial front and back positions-, whereas knowledge of the underlying structure, to the extent it was acquired, was not articulatable. The results are discussed in relation to previous studies on the implicit learnability of complex and abstract rules.     

Structural selection in implicit learning of artificial grammars

In the contextual cueing paradigm, Endo and Takeda (in Percept Psychophys 66:293–302, 2004) provided evidence that implicit learning involves selection of the aspect of a structure that is most useful to one’s task. The present study attempted to replicate this finding in artificial grammar learning to investigate whether or not implicit learning commonly involves such a selection. Participants in Experiment 1 were presented with an induction task that could be facilitated by several characteristics of the exemplars. For some participants, those characteristics included a perfectly predictive feature. The results suggested that the aspect of the structure that was most useful to the induction task was selected and learned implicitly. Experiment 2 provided evidence that, although salience affected participants’ awareness of the perfectly predictive feature, selection for implicit learning was mainly based on usefulness.     

Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies.     

Implicit learning in rule induction and problem solving

Using the Chinese Ring Puzzle (Kotovsky & Simon, ; P. J. Reber & Kotovsky, ), we studied the effect on rule discovery of having to plan actions or not in order to reach a goal state. This was done by asking participants to predict legal moves as in implicit learning tasks (Experiment 1) and by asking participants to make legal moves as in problem-solving tasks (Experiment 2). Our hypothesis was that having a specific goal state to reach has a dual effect on rule discovery. The first effect is positive and related to feedback from moves done in order to attain the goal: generalising the results of action and associating them to the conditions in which they were obtained allows discovery of the rule and learning it. The second effect is negative. In attempting to reach a specific goal, participants first tend to reduce the distance that separates the current state from the goal state (hill climbing) and so neglect the kind of exploration that facilitates rule and procedure discovery because this would seem to be a detour from the goal. Results show that having to plan actions improved performance in implicit learning tasks (Experiment 1), yet it impaired performance in problem-solving tasks (Experiment 2). Although implicit learning and problem solving are based on rule discovery, and entail noticing regularities in the material, in both cases, rule discovery processes appear to be task-dependent.     

Cognitive changes in conjunctive rule-based category learning: An ERP approach

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning.     

Role of prior knowledge in implicit and explicit learning of artificial grammars

Artificial grammar learning (AGL) performance reflects both implicit and explicit processes and has typically been modeled without incorporating any influence from general world knowledge. Our research provides a systematic investigation of the implicit vs. explicit nature of general knowledge and its interaction with knowledge types investigated by past AGL research (i.e., rule- and similarity-based knowledge). In an AGL experiment, a general knowledge manipulation involved expectations being either congruent or incongruent with training stimulus structure. Inconsistent observations paradoxically led to an advantage in structural knowledge and in the use of general world knowledge in both explicit (conscious) and implicit (unconscious) cases (as assessed by subjective measures). The above findings were obtained under conditions of reduced processing time and impaired executive resources. Key findings from our work are that implicit AGL can clearly be affected by general knowledge, and implicit learning can be enhanced by the violation of expectations.     

Implicit and explicit knowledge bases in artificial grammar learning.

Two experiments examined the claim for distinct implicit and explicit learning modes in the artificial grammar-learning task (Reber, 1967, 1989). Subjects initially attempted to memorize strings of letters generated by a finite-state grammar and then classified new grammatical and nongrammatical strings. Experiment 1 showed that subjects' assessment of isolated parts of strings was sufficient to account for their classification performance but that the rules elicited in free report were not sufficient. Experiment 2 showed that performing a concurrent random number generation task under different priorities interfered with free report and classification performance equally. Furthermore, giving different groups of subjects incidental or intentional learning instructions did not affect classification or free report.     

Mathematics of Totalities: an alternative to mathematics of sets

I dare say, a set is contranatural if some pair of its elements has a nonempty intersection. So, we consider only collections of disjoint nonempty elements and call them totalities. We propose the propositional logicTT, where a proposition letters some totality. The proposition is true if it letters the greatest totality. There are five connectives inTT: , , , , # and the last is called plexus. The truth of # means that any element of the totality has a nonempty intersection with any element of the totality . An imbeddingG of the classical predicate logicCPL inTT is defined. A formulaf ofCPL is a classical tautology if and only ifG(f) is always true inTT. So, mathematics may be expounded inTT, without quantifiers.     

An associative model of geometry learning: a modified choice rule

In a recent article, the authors (Miller & Shettleworth, 2007) showed how the apparently exceptional features of behavior in geometry learning ("reorientation") experiments can be modeled by assuming that geometric and other features at given locations in an arena are learned competitively as in the Rescorla-Wagner model and that the probability of visiting a location is proportional to the total associative strength of cues at that location relative to that of all relevant locations. Reinforced or unreinforced visits to locations drive changes in associative strengths. Dawson, Kelly, Spetch, and Dupuis (2008) have correctly pointed out that at parameter values outside the ranges the authors used to simulate a body of real experiments, our equation for choice probabilities can give impossible and/or wildly fluctuating results. Here, the authors show that a simple modification of the choice rule eliminates this problem while retaining the transparent way in which the model relates spatial choice to competitive associative learning of cue values.     

The artificial dispute over Implicit Personality Theory: An answer to Jackson, Strieker, and Mirels

In their evaluation of Tzeng and Tzeng's (1982) concerns over the level of sophistication currently evident in the study of Implicit Personality Theory (IPT), Jackson and Strieker (1982) and Mirels (1982a) reaffirm their disputed positions at the opposing extremes of the myth-vs.-fact continuum of IPT. The present paper re-examines major measurement and statistical issues in the empirical study of IPT that are still unacknowledged by either Jackson and Strieker, or Mirels, or both. It is demonstrated that their dispute is artificial and can be accounted for by their lack of measuremental sophistication and their statistical partialities. Researchers are cautioned to consider more than “mechanical computability” and “statistical adoptability” in conducting and/or judging similar empirical studies.     

Learning artificial grammars: No evidence for the acquisition of rules

Two experiments investigated whether there is evidence for acquisition of rules in implicit artificial grammar learning (AGL). Two different methods were used in meeting this goal, multiple regression analysis and analysis of receiver-operating characteristics (ROCs). By means of multiple regression analysis, several types of knowledge were identified that were used in judgments of grammaticality, for example, about single letters and about larger stimulus fragments. There was no evidence for the contribution of rule knowledge. The ROCs were in accord with a similarity-based account of AGL and thus did not support the notion that rule knowledge is acquired in AGL either. Simulations with a connectionist model corroborated the conclusion that the results were in accord with a similarity-based, associative account.     

Implicit learning: Below the subjective threshold

In this review, we consider three possible criteria by which knowledge might be regarded as implicit or inaccessible: It might be implicit only in the sense that it is difficult to articulate freely, or it might be implicit according to either an objective threshold or a subjective threshold. We evaluate evidence for these criteria in relation to artificial grammar learning, the control of complex systems, and sequence learning, respectively. We argue that the convincing evidence is not yet in, but construing the implicit nature of implicit learning in terms of a subjective threshold is most likely to prove fruitful for future research. Furthermore, the subjective threshold criterion may demarcate qualitatively different types of knowledge. We argue that (1) implicit, rather than explicit, knowledge is often relatively inflexible in transfer to different domains, (2) implicit, rather than explicit, learning occurs when attention is focused on specific items and not underlying rules, and (3) implicit learning and the resulting knowledge are often relatively robust.     

Implicit learning: Indirect, not unconscious

Dienes and Berry (1997) argue that there really are two kinds of knowledge, implicit and explicit, that can be characterized through a subjective threshold. We argue that the subjective threshold does not separate two forms of knowledge, but instead two uses of knowledge, for two different purposes. With Neal and Hesketh (1997), we suggest that an understanding of learning will be better served by investigating the processing that occurs under various circumstances than by attempting to isolate learning that occurs with awareness from that which occurs without it.     

Implicit learning: news from the front

Can we learn without awareness? While the current consensus is most likely to be `no', there is, however, considerable ongoing debate about the role that consciousness plays in cognition and about the nature of consciousness itself. In this article, we review recent advances in the field of implicit learning, based on three perspectives: empirical findings (including neuropsychological evidence), methodological issues, and theoretical positions (including computational models). The overall picture that emerges is complex and reflects a field that is very much in flux: while it seems undeniable that cognition involves some form of unconscious processing, it is as yet unclear how to best separate conscious and unconscious influences on learning, and how to best think about the status of the `cognitive unconscious'. We suggest that implicit learning is best construed as a complex form of priming taking place in continuously learning neural systems, and that the distributional knowledge so acquired can be causally efficacious in the absence of awareness that this knowledge was acquired or that it is currently influencing processing, that is, in the absence of metaknowledge.     

An evaluation of story grammars

We evaluate the “story grammar” approach to story understanding from three perspectives. We first examine the formal properties of the grammars and find only one to be formally adequate. We next evaluate the grammars empirically by asking whether they generate all simple stories and whether they generate only stories. We find many stories that they do not generate and one major class of nonstory that they do generate. We also evaluate the grammars' potential as comprehension models and find that they would add nothing to semantic models that focus on the story content. Hence we advocate a story content oriented approach to studying story understanding instead of the structural story grammar approach.     

Fluency does not express implicit knowledge of artificial grammars

It is commonly held that implicit knowledge expresses itself as fluency. A perceptual clarification task was used to examine the relationship between perceptual processing fluency, subjective familiarity, and grammaticality judgments in a task frequently used to produce implicit knowledge, artificial grammar learning (AGL). Four experiments examined the effects of naturally occurring differences and manipulated differences in perceptual fluency, where decisions were based on a brief exposure to test-strings (during the clarification task only) or normal exposure. When perceptual fluency was not manipulated, it was weakly related to familiarity and grammaticality judgments, but unrelated to grammatical status and hence not a source of accuracy. Counterbalanced grammatical and ungrammatical strings did not differ in perceptual fluency but differed substantially in subjective familiarity. When fluency was manipulated, faster clarifying strings were rated as more familiar and were more often endorsed as grammatical but only where exposure was brief. Results indicate that subjective familiarity derived from a source other than perceptual fluency, is the primary basis for accuracy in AGL. Perceptual fluency is found to be a dumb heuristic influencing responding only in the absence of actual implicit knowledge.     

Generate and test: An alternative route to knowledge elicitation in an implicit learning task

Three experiments are reported, which examine generation of knowledge in the McGeorge and Burton (1990) invariant learning task. In this task, participants are exposed to 30 four-digit numbers containing an invariant "3". Following this participants then demonstrate a preference for novel numbers containing this invariant over numbers without it. Despite above-chance performance on this pseudo-memory test, participants appear unable to verbalize anything pertinent to the invariant. Here we introduce a novel version of this task, relying on generation of items rather than a preference test. We argue that this new task engages different processing resources, resulting in different patterns of performance. In Experiment 1, invariant learning was demonstrated using a novel fragment completion test. Experiment 2 found that suppressing articulation inhibited learning, implying that this test task accesses phonological knowledge. It is suggested that using the fragment completion test engages different processing resources during test from those in a preference test. Experiment 3 reinforces this position by demonstrating that knowledge appears to transfer across surface features, a result that seems to contradict recent findings by Stadler, Warren, and Lesch (2000). A resolution is offered, drawing on episodic accounts of implicit learning.     

Implicit learning of tonality: a self-organizing approach

Tonal music is a highly structured system that is ubiquitous in our cultural environment. We demonstrate the acquisition of implicit knowledge of tonal structure through neural self-organization resulting from mere exposure to simultaneous and sequential combinations of tones. In the process of learning, a network with fundamental neural constraints comes to internalize the essential correlational structure of tonal music. After learning, the network was run through a range of experiments from the literature. The model provides a parsimonious account of a variety of empirical findings dealing with the processing of tone, chord, and key relationships, including relatedness judgments, memory judgments, and expectancies. It also illustrates the plausibility of activation being a unifying mechanism underlying a range of cognitive tasks.