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

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.

Neural dynamics underlying the processing of implicit form-meaning connections: The dissociative roles of theta and alpha oscillations

Implicit learning plays an important role in the language acquisition. In addition to helping people acquire the form-level rules (e.g., the word order regularities), implicit learning can also facilitate the acquisition of word meanings (i.e., the establishment of connections between the word form and its meanings). Although some behavioral studies have explored the processing of implicit form-meaning connections, the neural dynamics underlying this processing remains unclear. Through examining whether participants could implicitly acquire the literal and metaphorical meanings of novel words, and applying the time-frequency analysis on the electroencephalogram (EEG) data collected in the testing phase, the neural oscillations corresponding to the processing of implicit form-literal and form-metaphorical meaning connections were explored. The results showed that participants in the experimental group could implicitly acquire the form-literal and form-metaphorical meaning connections after training, while participants in the control group who were not trained did not have access to such form-meaning connections. Meanwhile, during the processing of form-literal meaning connections, the greater suppression of alpha oscillations was induced by the testing items that follow the same rules as the training items (i.e., the regular testing items) in the experimental group, whereas the stronger enhancement of theta oscillations was elicited by the regular testing items in the experimental group during the processing of form-metaphorical meaning connections. Our study provides insights for understanding the processing of implicit form-literal and form-metaphorical meaning connections and the neural dynamics underlying the processing.

Modeling test learning and dual-task dissociations

Much of cognitive psychology is premised on the distinction between automatic and intentional processes, but the distinction often remains vague in practice and alternative explanations are often not followed through. For example, Hendricks, Conway and Kellogg (Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 491–1500, 2013) found that dual tasks at training versus at test dissociated performance in two different artificial grammar learning tasks. This was taken as evidence for underlying automatic and intentional processes. In this article, a different explanation is considered based on test learning and similarity, where participants are assumed to update their knowledge at test. Contrasting formal memory models of test learning are implemented, and it is concluded that the models account for the relevant dissociations without assuming a distinction between automatic and intentional processes.

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.

Applying an exemplar model to an implicit rule-learning task: Implicit learning of semantic structure

Studies of implicit learning often examine peoples' sensitivity to sequential structure. Computational accounts have evolved to reflect this bias. An experiment conducted by Neil and Higham [Neil, G. J., & Higham, P. A.(2012). Implicit learning of conjunctive rule sets: An alternative to artificial grammars. Consciousness and Cognition, 21, 1393-1400] points to limitations in the sequential approach. In the experiment, participants studied words selected according to a conjunctive rule. At test, participants discriminated rule-consistent from rule-violating words but could not verbalize the rule. Although the data elude explanation by sequential models, an exemplar model of implicit learning can explain them. To make the case, we simulate the full pattern of results by incorporating vector representations for the words used in the experiment, derived from the large-scale semantic space models LSA and BEAGLE, into an exemplar model of memory, MINERVA 2. We show that basic memory processes in a classic model of memory capture implicit learning of non-sequential rules, provided that stimuli are appropriately represented.

Fluency Expresses Implicit Knowledge of Tonal Symmetry

The purposes of the present study were twofold. First, we sought to establish whether tonal symmetry produces processing fluency. Second, we sought to explore whether symmetry and chunk strength express themselves differently in fluency, as an indication of different mechanisms being involved for sub- and supra-finite state processing. Across two experiments, participants were asked to listen to and memorize artificial poetry showing a mirror symmetry (an inversion, i.e., a type of cross serial dependency); after this training phase, people completed a four-choice RT task in which they were presented with new artificial poetry. Participants were required to identify the stimulus displayed. We found that symmetry sped up responding to the second half of strings, indicating a fluency effect. Furthermore, there was a dissociation between fluency effects arising from symmetry vs. chunk strength, with stronger fluency effects for symmetry rather than chunks in the second half of strings. Taken together, we conjecture a divide between finite state and supra-finite state mechanisms in learning grammatical sequences.

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.

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).

Implicit learning and reading: insights from typical children and children with developmental dyslexia using the artificial grammar learning (AGL) paradigm

We examined implicit learning in school-aged children with and without developmental dyslexia based on the proposal that implicit learning plays a significant role in mastering fluent reading. We ran two experiments with 16 typically developing children (9 to 11-years-old) and 16 age-matched children with developmental dyslexia using the artificial grammar learning (AGL) paradigm. In Experiment 1 (non-transfer task), children were trained on stimuli that followed patterns (rules) unknown to them. Subsequently, they were asked to decide from a novel set which stimuli follow the same rules (grammaticality judgments). In Experiment 2 (transfer task), training and testing stimuli differed in their superficial characteristics but followed the same rules. Again, children were asked to make grammaticality judgments. Our findings expand upon previous research by showing that children with developmental dyslexia show difficulties in implicit learning that are most likely specific to higher-order rule-like learning. These findings are discussed in relation to current theories of developmental dyslexia and of implicit learning.

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.

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.