Applying an exemplar model to the artificial-grammar task: inferring grammaticality from similarity

Can frequent long stimulus onset ansynchronies (SOAs) foster the representation of two separated task-sets in dual-tasking?

Recent findings suggest that in dual-tasking the elements of the two tasks are associated across tasks and are stored in a conjoint memory episode, meaning that the tasks are not represented as isolated task-sets. In the current study, we tested whether frequent long stimulus onset ansynchronies (SOAs) can foster the representation of two separated task-sets thereby reducing or even hindering participants to generate conjoint memory episodes-compared to an integrated task-set representation induced by frequent short SOAs. Alternatively, it is conceivable that conjoint memory episodes are an inevitable consequence of presenting two tasks within a single trial. In two dual-task experiments, we tested between consecutive trials whether repeating the stimulus-response bindings of both tasks would lead to faster responses than repeating only one of the two tasks' stimulus-response bindings. The dual-task consisted of a visual-manual search task (VST) and an auditory-manual discrimination task (ADT). Overall, the results suggest that, after processing two tasks within a single trial, generating a conjoint memory episode seems to be a default process, regardless of SOA frequency. However, the respective SOA frequency affected the participants' strategy to group the processing of the two tasks or not, thereby modulating the impact of the reactivated memory episode on task performance.

Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar

Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model's predictions corresponded to participants' decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn.

Repeated exposure to exemplars does not enhance implicit learning: A puzzle for models of learning and memory

We learn regularities in the world around us, frequently without conscious effort, a phenomenon known as implicit learning. These regularities are often impossible to verbalise. One example of implicit learning is the structural effect, in which participants learn a rule set combining two factors, such as lexical frequency and concreteness. Theories of implicit learning predict that repetition of exemplar words would result in improved learning of the rule set, increasing the magnitude of the structural effect. Over four experiments, we demonstrate that this is, in fact, not the case. In Experiments 1 and 2, three repetitions of exemplar words result in superior item memory, but no change in the magnitude of the structural effect, compared with individually presented words. In Experiments 3 and 4, the structural effect is shown to be invariant to five repetitions of exemplar words and at high and low numbers of exemplars. In all four experiments, participants were unable to describe the rule set underlying the structural effect. However, confidence ratings demonstrated sensitivity to the structure and this sensitivity, unlike endorsements, increased with strength. The results are discussed in reference to differentiation, structural versus judgement knowledge, and flexible learning systems.

Cross-cultural differences in implicit learning of chunks versus symmetries

Three experiments explore whether knowledge of grammars defining global versus local regularities has an advantage in implicit acquisition and whether this advantage is affected by cultural differences. Participants were asked to listen to and memorize a number of strings of 10 syllables instantiating an inversion (i.e. a global pattern); after the training phase, they were required to judge whether new strings were well formed. In Experiment 1, Western people implicitly acquired the inversion rule defined over the Chinese tones in a similar way as Chinese participants when alternative structures (specifically, chunking and repetition structures) were controlled. In Experiments 2 and 3, we directly pitted knowledge of the inversion (global) against chunk (local) knowledge, and found that Chinese participants had a striking global advantage in implicit learning, which was greater than that of Western participants. Taken together, we show for the first time cross-cultural differences in the type of regularities implicitly acquired.

Evaluative Conditioning as Memory-Based Judgment

The article proposes a view of evaluative conditioning (EC) as resulting from judgments based on learning instances stored in memory. It is based on the formal episodic memory model MINERVA 2. Additional assumptions specify how the information retrieved from memory is used to inform specific evaluative dependent measures. The present approach goes beyond previous accounts in that it uses a well-specified formal model of episodic memory; it is however more limited in scope as it aims to explain EC phenomena that do not involve reasoning processes. The article illustrates how the memory-based-judgment view accounts for several empirical findings in the EC literature that are often discussed as evidence for dual-process models of attitude learning. It sketches novel predictions, discusses limitations of the present approach, and identifies challenges and opportunities for its future development.

Computational and empirical simulations of selective memory impairments: Converging evidence for a single-system account of memory dissociations

Current theory has divided memory into multiple systems, resulting in a fractionated account of human behaviour. By an alternative perspective, memory is a single system. However, debate over the details of different single-system theories has overshadowed the converging agreement among them, slowing the reunification of memory. Evidence in favour of dividing memory often takes the form of dissociations observed in amnesia, where amnesic patients are impaired on some memory tasks but not others. The dissociations are taken as evidence for separate explicit and implicit memory systems. We argue against this perspective. We simulate two key dissociations between classification and recognition in a computational model of memory, A Theory of Nonanalytic Association. We assume that amnesia reflects a quantitative difference in the quality of encoding. We also present empirical evidence that replicates the dissociations in healthy participants, simulating amnesic behaviour by reducing study time. In both analyses, we successfully reproduce the dissociations. We integrate our computational and empirical successes with the success of alternative models and manipulations and argue that our demonstrations, taken in concert with similar demonstrations with similar models, provide converging evidence for a more general set of single-system analyses that support the conclusion that a wide variety of memory phenomena can be explained by a unified and coherent set of principles.

Evolving building blocks of rhythm: how human cognition creates music via cultural transmission

Why does musical rhythm have the structure it does? Musical rhythm, in all its cross-cultural diversity, exhibits commonalities across world cultures. Traditionally, music research has been split into two fields. Some scientists focused on musicality, namely the human biocognitive predispositions for music, with an emphasis on cross-cultural similarities. Other scholars investigated music, seen as a cultural product, focusing on the variation in world musical cultures. Recent experiments found deep connections between music and musicality, reconciling these opposing views. Here, we address the question of how individual cognitive biases affect the process of cultural evolution of music. Data from two experiments are analyzed using two complementary techniques. In the experiments, participants hear drumming patterns and imitate them. These patterns are then given to the same or another participant to imitate. The structure of these initially random patterns is tracked along experimental "generations." Frequentist statistics show how participants' biases are amplified by cultural transmission, making drumming patterns more structured. Structure is achieved faster in transmission within rather than between participants. A Bayesian model approximates the motif structures participants learned and created. Our data and models suggest that individual biases for musicality may shape the cultural transmission of musical rhythm.

Impact of Response Stimulus Interval on Transfer of Non-local Dependent Rules in Implicit Learning: An ERP Investigation

In the literature on implicit learning, controversy exists regarding whether the knowledge obtained from implicit sequence learning consists of context-bound superficial features or context-free structural rules. To explore the nature of implicit knowledge, event related potentials (ERP) recordings of participants’ performances in a non-local dependent transfer task under two response-stimulus-interval (RSI) conditions (250 and 750 ms) were obtained. In the behavioral data, a transfer effect was found in the 750 ms RSI condition but not in the 250 ms RSI condition, suggesting that a long RSI is the basis for the occurrence of non-local dependent transfer, as which might have provided enough reaction time for participants to process and capture the implicit rule. Moreover, P300 amplitude was found to be sensitive to the impact of RSI on the training process (i.e., the longer RSI elicited higher P300 amplitudes), while variations in both N200 (i.e., a significant increase) and P300 amplitudes (i.e., a significant decrease) were found to be related to the presence of a transfer effect. Our results supported the claim that implicit learning can involve abstract rule knowledge acquisition under an appropriate RSI condition, and that amplitude variation in early ERP components (i.e., N200 and P300) can be useful indexes of non-local dependent learning and transfer effects.

Measuring Individual Differences in Implicit Learning with Artificial Grammar Learning Tasks

Abstract. Implicit learning can be defined as learning without intention or awareness. We discuss conceptually and investigate empirically how individual differences in implicit learning can be measured with artificial grammar learning (AGL) tasks. We address whether participants should be instructed to rate the grammaticality or the novelty of letter strings and look at the impact of a knowledge test on measurement quality. We discuss these issues from a conceptual perspective and report three experiments which suggest that (1) the reliability of AGL is moderate and too low for individual assessments, (2) a knowledge test decreases task consistency and increases the correlation with reportable grammar knowledge, and (3) performance in AGL tasks is independent from general intelligence and educational attainment.

The effects of ordinal load on incidental temporal learning

How can we grasp the temporal structure of events? A few studies have indicated that representations of temporal structure are acquired when there is an intention to learn, but not when learning is incidental. Response-to-stimulus intervals, uncorrelated temporal structures, unpredictable ordinal information, and lack of metrical organization have been pointed out as key obstacles to incidental temporal learning, but the literature includes piecemeal demonstrations of learning under all these circumstances. We suggest that the unacknowledged effects of ordinal load may help reconcile these conflicting findings, ordinal load referring to the cost of identifying the sequence of events (e.g., tones, locations) where a temporal pattern is embedded. In a first experiment, we manipulated ordinal load into simple and complex levels. Participants learned ordinal-simple sequences, despite their uncorrelated temporal structure and lack of metrical organization. They did not learn ordinal-complex sequences, even though there were no response-to-stimulus intervals nor unpredictable ordinal information. In a second experiment, we probed learning of ordinal-complex sequences with strong metrical organization, and again there was no learning. We conclude that ordinal load is a key obstacle to incidental temporal learning. Further analyses showed that the effect of ordinal load is to mask the expression of temporal knowledge, rather than to prevent learning.

Implicit learning is order dependent

We report two experiments using the artificial-grammar task that demonstrate order dependence in implicit learning. Studying grammatical training strings in different orders did not affect participants' discrimination of grammatical from ungrammatical test strings, but it did affect their judgments about specific test strings. Current accounts of learning in the artificial-grammar task focus on category-level discrimination and largely ignore item-level discrimination. Hence, the results highlight the importance of moving theory from a category- to an item-level of analysis and point to a new way to evaluate and to refine accounts of implicit learning.

Implicit and explicit contributions to statistical learning

Statistical learning allows learners to detect regularities in the environment and appears to emerge automatically as a consequence of experience. Statistical learning paradigms bear many similarities to those of artificial grammar learning and other types of implicit learning. However, whether learning effects in statistical learning tasks are driven by implicit knowledge has not been thoroughly examined. The present study addressed this gap by examining the role of implicit and explicit knowledge within the context of a typical auditory statistical learning paradigm. Learners were exposed to a continuous stream of repeating nonsense words. Learning was tested (a) directly via a forced-choice recognition test combined with a remember/know procedure and (b) indirectly through a novel reaction time (RT) test. Behavior and brain potentials revealed statistical learning effects with both tests. On the recognition test, accurate responses were associated with subjective feelings of stronger recollection, and learned nonsense words relative to nonword foils elicited an enhanced late positive potential indicative of explicit knowledge. On the RT test, both RTs and P300 amplitudes differed as a function of syllable position, reflecting facilitation attributable to statistical learning. Explicit stimulus recognition did not correlate with RT or P300 effects on the RT test. These results provide evidence that explicit knowledge is accrued during statistical learning, while bringing out the possibility that dissociable implicit representations are acquired in parallel. The commonly used recognition measure primarily reflects explicit knowledge, and thus may underestimate the total amount of knowledge produced by statistical learning. Indirect measures may be more sensitive indices of learning, capturing knowledge above and beyond what is reflected by recognition accuracy.

Information theory and artificial grammar learning: inferring grammaticality from redundancy

In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute).

Cross-situational word learning is both implicit and strategic

For decades, implicit learning researchers have examined a variety of cognitive tasks in which people seem to automatically extract structure from the environment. Similarly, recent statistical learning studies have shown that people can learn word-object mappings from the repeated co-occurrence of words and objects in individually ambiguous situations. In light of this, the goal of the present paper is to investigate whether adult cross-situational learners require an explicit effort to learn word-object mappings, or if it may take place incidentally, only requiring attention to the stimuli. In two implicit learning experiments with incidental tasks directing participants' attention to different aspects of the stimuli, we found evidence of learning, suggesting that cross-situational learning mechanisms can operate incidentally, without explicit effort. However, performance was superior under explicit study instructions, indicating that strategic processes also play a role. Moreover, performance under instruction to learn word meanings did not differ from performance at counting co-occurrences, which may indicate these tasks engage similar strategies.

A Visual-Familiarity Account of Evidence for Orthographic Processing in Baboons (Papio papio)

Grainger, Dufau, Montant, Ziegler, and Fagot (2012a) taught 6 baboons to discriminate words from nonwords in an analogue of the lexical decision task. The baboons more readily identified novel words than novel nonwords as words, and they had difficulty rejecting nonwords that were orthographically similar to learned words. In a subsequent test (Ziegler, Hannagan, et al., 2013), responses from the same animals evinced a transposed-letter effect. These three effects, when seen in skilled human readers, are taken as hallmarks of orthographic processing. We show, by simulation of the unique learning trajectory of each baboon, that the results can be interpreted equally well as an example of simple, familiarity-based discrimination of pixel maps without orthographic processing.

The nature of the memory buffer in implicit learning: learning Chinese tonal symmetries

Previous research has established that people can implicitly learn chunks, which (in terms of formal language theory) do not require a memory buffer to process. The present study explores the implicit learning of nonlocal dependencies generated by higher than finite-state grammars, specifically, Chinese tonal retrogrades (i.e. centre embeddings generated from a context-free grammar) and inversions (i.e. cross-serial dependencies generated from a mildly context-sensitive grammar), which do require buffers (for example, last in-first out and first in-first out, respectively). People were asked to listen to and memorize artificial poetry instantiating one of the two grammars; after this training phase, people were informed of the existence of rules and asked to classify new poems, while providing attributions of the basis of their judgments. People acquired unconscious structural knowledge of both tonal retrogrades and inversions. Moreover, inversions were implicitly learnt more easily than retrogrades constraining the nature of the memory buffer in computational models of implicit learning.

Implicit learning of conjunctive rule sets: an alternative to artificial grammars

A single experiment is reported that investigated implicit learning using a conjunctive rule set applied to natural words. Participants memorized a training list consisting of words that were either rare-concrete and common-abstract or common-concrete and rare-abstract. At test, they were told of the rule set, but not told what it was. Instead, they were shown all four word types and asked to classify words as rule-consistent words or not. Participants classified the items above chance, but were unable to verbalize the rules, even when shown a list that included the categories that made up the conjunctive rule and asked to select them. Most participants identified familiarity as the reason for classifying the items as they did. An analysis of the materials demonstrated that conscious micro-rules (i.e., chunk knowledge) could not have driven performance. We propose that such materials offer an alternative to artificial grammar for studies of implicit learning.

An instance theory of associative learning

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

Grammaticality is inferred from global similarity: A reply to Kinder (2010)

Jamieson and Mewhort (2009b) proposed an account of performance in the artificial-grammar judgement-of-grammaticality task based on Hintzman's (1986) model of retrieval, Minerva 2. In the account, each letter is represented by a unique vector of random elements, and each exemplar is represented by concatenating its constituent letter vectors. Although successful in simulating several experiments, Kinder (2010) showed that the model fails for three selected experiments. We track the model's failure to a constraint introduced by concatenating letter vectors to construct the exemplar representation. To fix the problem, we use a holographic representation. Holographic representation not only provides the flexibility missing with the concatenation scheme but also acknowledges variability in what subjects notice when they inspect training exemplars. Armed with holographic representations, we show that the model successfully captures the three problematic data sets. We argue for retrospective accounts, like the present one, that acknowledge subjects' skill in drawing unexpected inferences based on memory of studied items against prospective accounts that require subjects to learn statistical regularities in the training set in anticipation of an undefined classification test.

Is grammaticality inferred from global similarity? Comment on Jamieson and Mewhort (2009)

In a recent article, Jamieson and Mewhort (2009) proposed a novel account of artificial grammar learning (AGL), which is based on a multitrace model of episodic memory, the Minerva 2 model. According to this account, test performance in AGL is based on an assessment of global similarity of the test strings to the memory traces of the training strings. In this article, simulation studies are presented, showing for three different AGL experiments that the predictions of the Minerva 2 model strikingly deviate from participants' performance. It is argued that participants' test performance is not generally based on general similarity.

Applying an exemplar model to the artificial-grammar task: String completion and performance on individual items

Jamieson and Mewhort (2009a) demonstrated that performance in the artificial-grammar task could be understood using an exemplar model of memory. We reinforce the position by testing the model against data for individual test items both in a standard artificial-grammar experiment and in a string-completion variant of the standard procedure. We argue that retrieval is sensitive to structure in memory. The work ties performance in the artificial-grammar task to principles of explicit memory.

Applying an exemplar model to the serial reaction-time task: Anticipating from experience

We present a serial reaction time (SRT) task in which participants identified the location of a target by pressing a key mapped to the location. The location of successive targets was determined by the rules of a grammar, and we varied the redundancy of the grammar. Increasing both practice and the redundancy of the grammar reduced response time, but the participants were unable to describe the grammar. Such results are usually discussed as examples of implicit learning. Instead, we treat performance in terms of retrieval from a multitrace memory. In our account, after each trial, participants store a trace comprising the current stimulus, the response associated with it, and the context provided by the immediately preceding response. When a target is presented, it is used as a prompt to retrieve the response mapped to it. As participants practise the task, the redundancy of the series helps point to the correct response and, thereby, speeds retrieval of the response. The model captured performance in the experiment and in classic SRT studies from the literature. Its success shows that the SRT task can be understood in terms of retrieval from memory without implying implicit learning.