Intact Artificial Grammar Learning in Amnesia: Dissociation of Classification Learning and Explicit Memory for Specific Instances

Perceptual addition of continuous magnitudes in an 'artificial algebra'

Although there is substantial evidence for an innate 'number sense' that scaffolds learning about mathematics, whether the underlying representations are based on discrete or continuous perceptual magnitudes has been controversial. Yet the nature of the computations supported by these representations has been neglected in this debate. While basic computation of discrete non-symbolic quantities has been reliably demonstrated in adults, infants, and non-humans, far less consideration has been given to the capacity for computation of continuous perceptual magnitudes. Here we used a novel experimental task to ask if humans can learn to add non-symbolic, continuous magnitudes in accord with the properties of an algebraic group, by feedback and without explicit instruction. Three pairs of experiments tested perceptual addition under the group properties of commutativity (Experiments 1a-b), identity and inverses (Experiments 2a-b) and associativity (Experiments 3a-b), with both line length and brightness modalities. Transfer designs were used in which participants responded on trials with feedback based on sums of magnitudes and later were tested with novel stimulus configurations. In all experiments, correlations of average responses with magnitude sums were high on trials with feedback. Responding on transfer trials was accurate and provided strong support for addition under all of the group axioms with line length, and for all except associativity with brightness. Our results confirm that adult human subjects can implicitly add continuous quantities in a manner consistent with symbolic addition over the integers, and that an 'artificial algebra' task can be used to study implicit computation.

The effect of hippocampal subfield damage on rapid temporal integration through statistical learning and associative inference

INTRODUCTION

The hippocampus (HPC) supports integration of information across time, often indexed by associative inference (AI) and statistical learning (SL) tasks. In AI, an indirect association between stimuli that never appeared together is inferred, whereas SL involves learning item relationships by extracting regularities across experiences. A recent model of hippocampal function (Schapiro et al., 2017) proposes that the HPC can support temporal integration in both paradigms through its two distinct pathways.

METHODS

We tested this models' predictions in four patients with varying degrees of bilateral HPC damage and matched healthy controls, with two patients with complementary damage to either the monosynaptic or trisynaptic pathway. During AI, participants studied overlapping paired associates (AB, BC) and their memory was tested for premise pairs (AB) and for inferred pairs (AC). During SL, participants passively viewed a continuous picture sequence that contained an underlying structure of triplets that later had to be recognized.

RESULTS

Binomial distributions were used to calculate above chance performance at the individual level. For AI, patients with focal HPC damage were impaired at inference but could correctly infer pairs above chance once premise pair acquisition was equated to controls; however, the patient with HPC and cortical damage showed severe impairment at recalling premise and inferred pairs, regardless of accounting for premise pair performance. For SL, none of the patients performed above chance, but notably neither did most controls.

CONCLUSIONS

Associative inference of indirect relationships can be intact with HPC damage to either hippocampal pathways or the HPC more broadly, provided premise pairs can first be formed. Inference may remain intact through residual HPC tissue supporting premise pair acquisition, and/or through extra-hippocampal structures supporting inference at retrieval. Clear conclusions about hippocampal contributions to SL are precluded by low performance in controls, which we caution is not dissimilar to previous amnesic studies using the same task. This complicates interpretations of studies claiming necessity of hippocampal contributions to SL and warrants the use of a common and reliable task before conclusions can be drawn.

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.

Artificial Grammar Learning in Children With Developmental Language Disorder

PURPOSE

The purpose of the study was to evaluate implicit learning in children with developmental language disorder (DLD) by employing a visual artificial grammar learning task.

METHOD

Thirteen children with DLD and 24 children with typical language development between the ages of 8 and 12 years completed a visual artificial grammar learning task. During the training phase of the task, participants were presented with strings of shapes that followed the underlying structure of a finite grammar. During the testing phase, participants were asked to judge whether new strings were grammatical or nongrammatical. Grammatical judgment of new strings served to measure generalization of the underlying grammatical structure. Endorsement based on chunk strength, or similarity to training exemplars, served to evaluate the extent to which children relied on surface features to guide their task performance.

RESULTS

As a group, children with typical development performed better on the artificial grammar learning task, compared with children with DLD, and accepted more grammatical strings regardless of their similarity to training exemplars. Task performance in both groups was not affected by surface features. Performance of children with DLD whose test accuracy exceeded the learning threshold of 0.5 was consistent with a generalization of the underlying grammatical structure that was unaffected by surface features.

CONCLUSIONS

The study found group differences in learning outcomes between children with and without DLD. Consistent with previous reports, children with typical development correctly endorsed more grammatical strings than children with DLD, suggesting a better acquisition of the grammatical structure. However, there was no evidence to suggest that children in the DLD group (learners and nonlearners) relied on surface features (i.e., familiarity to training exemplars) in their grammatical judgment. These results refute our hypothesis that children in the DLD group would show judgment based on familiarity.

Animal consciousness: Should a new behavioral correlate in monkeys persuade agnostics?

After human subjects learn to look away from visible cues, their attention can still be captured by cues so brief that they cause no conscious perception. A new study has found evidence that this behavior also occurs in monkeys. Is this further evidence for consciousness in a nonhuman animal?

Project DyAdd: Non-linguistic Theories of Dyslexia Predict Intelligence

Two themes have puzzled the research on developmental and learning disorders for decades. First, some of the risk and protective factors behind developmental challenges are suggested to be shared and some are suggested to be specific for a given condition. Second, language-based learning difficulties like dyslexia are suggested to result from or correlate with non-linguistic aspects of information processing as well. In the current study, we investigated how adults with developmental dyslexia or ADHD as well as healthy controls cluster across various dimensions designed to tap the prominent non-linguistic theories of dyslexia. Participants were 18–55-year-old adults with dyslexia (n = 36), ADHD (n = 22), and controls (n = 35). Non-linguistic theories investigated with experimental designs included temporal processing impairment, abnormal cerebellar functioning, procedural learning difficulties, as well as visual processing and attention deficits. Latent profile analysis (LPA) was used to investigate the emerging groups and patterns of results across these experimental designs. LPA suggested three groups: (1) a large group with average performance in the experimental designs, (2) participants predominantly from the clinical groups but with enhanced conditioning learning, and (3) participants predominantly from the dyslexia group with temporal processing as well as visual processing and attention deficits. Despite the presence of these distinct patterns, participants did not cluster very well based on their original status, nor did the LPA groups differ in their dyslexia or ADHD-related neuropsychological profiles. Remarkably, the LPA groups did differ in their intelligence. These results highlight the continuous and overlapping nature of the observed difficulties and support the multiple deficit model of developmental disorders, which suggests shared risk factors for developmental challenges. It also appears that some of the risk factors suggested by the prominent non-linguistic theories of dyslexia relate to the general level of functioning in tests of intelligence.

Explicit memory and cognition in monkeys

Taxonomies of human memory, influenced heavily by Endel Tulving, make a fundamental distinction between explicit and implicit memory. Humans are aware of explicit memories, whereas implicit memories control behavior even though we are not aware of them. Efforts to understand the evolution of memory, and to use nonhuman animals to model human memory, will be facilitated by better understanding the extent to which this critical distinction exists in nonhuman animals. Work with metacognition paradigms in the past 20 years has produced a strong case for the existence of explicit memory in nonhuman primates and possibly other nonhuman animals. Clear dissociations of explicit and implicit memory by metacognition have yet to be demonstrated in nonhumans, although dissociations between memory systems by other behavioral techniques, and by brain manipulations, suggest that the explicit-implicit distinction applies to nonhumans. Neurobehavioral studies of metamemory are beginning to identify neural substrates for memory monitoring in the frontal cortex of monkeys. We have strong evidence that at least some memory systems are explicit in rhesus monkeys, but we need to learn more about the distribution of explicit processes across cognitive systems within monkeys, and across species.

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.

Evidence of stable individual differences in implicit learning

There is a fundamental psychological and neuropsychological distinction between explicit and implicit memory, and it has been proposed that whereas there are stable trait individual differences in explicit memory ability, there are not such differences across people for implicit learning. There is, however, little evidence about whether or not there are stable trait differences in implicit learning. Here we performed a test-retest reliability study with healthy young adults in which they performed four implicit learning tasks (artificial grammar learning, probabilistic classification, serial response, and implicit category learning) twice, about a week apart. We found medium (by Cohen's guidelines) test-retest reliability for three of the tasks: probabilistic classification, serial response, and implicit category learning, suggesting that differences in implicit learning ability are more stable than originally thought. In addition, implicit learning on all tasks was unrelated to explicit measures: we did not find any correlation between implicit learning measures and independent measures of IQ, working memory, or explicit learning ability. These findings indicate that implicit learning, like explicit learning, varies reliably across individuals.

Statistical Learning, Implicit Learning, and First Language Acquisition: A Critical Evaluation of Two Developmental Predictions

The role of distributional information in language learning, and learning more generally, has been studied extensively in both the statistical learning and the implicit learning literatures. Despite the similarity in research questions, the two literatures have remained largely separate. Here, we draw on findings from the two traditions to critically evaluate two developmental predictions that are central to both. The first is the question of age invariance: Does learning improve during development or is it fully developed in infancy? The combined findings suggest that both implicit and statistical learning improve during childhood, contra the age invariance prediction. This raises questions about the role of implicit statistical learning (ISL) in explaining the age-related deterioration in language learning: Children's better language learning abilities cannot be attributed to their improved distributional learning skills. The second issue we examine is the predictive relation to language outcomes: Does variation in learning predict variation in language outcomes? While there is evidence for such links, there is concern in both research traditions about the reliability of the tasks used with children. We present data suggesting that commonly used statistical learning measures may not capture stable individual differences in children, undermining their utility for assessing the link to language outcomes in developmental samples. The evaluation of both predictions highlights the empirical parallels between the implicit and statistical learning literatures, and the need to better integrate their developmental investigation. We go on to discuss several of the open challenges facing the study of ISL during development.

Rhesus monkeys metacognitively monitor memories of the order of events

Human working memory is a capacity- and duration-limited system in which retention and manipulation of information is subject to metacognitive monitoring and control. At least some nonhuman animals appear to also monitor and control the contents of working memory, but only relatively simple cases where animals monitor or control the presence or absence of single memories have been studied. Here we combine a comparatively complex order memory task with methodology that assesses the capacity to introspect about memory. Monkeys observed sequential presentations of five images, and at test, reported which of two images from the list had appeared first during study. Concurrently, they chose to complete or avoid these tests on a trial-by-trial basis. Monkeys "knew when they knew" the correct response. They were less accurate discriminating images that had appeared close in time to one another during study and were more likely to avoid these difficult tests than they were to avoid easier tests. These results indicate that monkeys can metacognitively monitor relatively complex properties of the contents of working memory, including the quality of representations of temporal relations among images.

The effect of encoding conditions on learning in the prototype distortion task

The prototype distortion task demonstrates that it is possible to learn about a category of physically similar stimuli through mere observation. However, there have been few attempts to test whether different encoding conditions affect learning in this task. This study compared prototypicality gradients produced under incidental learning conditions in which participants performed a visual search task, with those produced under intentional learning conditions in which participants were required to memorize the stimuli. Experiment 1 showed that similar prototypicality gradients could be obtained for category endorsement and familiarity ratings, but also found (weaker) prototypicality gradients in the absence of exposure. In Experiments 2 and 3, memorization was found to strengthen prototypicality gradients in familiarity ratings in comparison to visual search, but there were no group differences in participants' ability to discriminate between novel and presented exemplars. Although the Search groups in Experiments 2 and 3 produced prototypicality gradients, they were no different in magnitude to those produced in the absence of stimulus exposure in Experiment 1, suggesting that incidental learning during visual search was not conducive to producing prototypicality gradients. This study suggests that learning in the prototype distortion task is not implicit in the sense of resulting automatically from exposure, is affected by the nature of encoding, and should be considered in light of potential learning-at-test effects.

Knowledge and learning of verb biases in amnesia

Verb bias-the co-occurrence frequencies between a verb and the syntactic structures it may appear with-is a critical and reliable linguistic cue for online sentence processing. In particular, listeners use this information to disambiguate sentences with multiple potential syntactic parses (e.g., Feel the frog with the feather.). Further, listeners dynamically update their representations of specific verbs in the face of new evidence about verb-structure co-occurrence. Yet, little is known about the biological memory systems that support the use and dynamic updating of verb bias. We propose that hippocampal-dependent declarative (relational) memory represents a likely candidate system because it has been implicated in the flexible binding of relational co-occurrences and in statistical learning. We explore this question by testing patients with severe and selective deficits in declarative memory (anterograde amnesia), and demographically matched healthy participants, in their on-line interpretation of ambiguous sentences and the ability to update their verb bias with experience. We find that (1) patients and their healthy counterparts use existing verb bias to successfully interpret on-line ambiguity, however (2) unlike healthy young adults, neither group updated these biases in response to recent exposure. These findings demonstrate that using existing representations of verb bias does not necessitate involvement of the declarative memory system, but leave open the question of whether the ability to update representations of verb-specific biases requires hippocampal engagement.

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.

The Necessity of the Hippocampus for Statistical Learning

Converging evidence points to a role for the hippocampus in statistical learning, but open questions about its necessity remain. Evidence for necessity comes from Schapiro and colleagues who report that a single patient with damage to hippocampus and broader medial temporal lobe cortex was unable to discriminate new from old sequences in several statistical learning tasks. The aim of the current study was to replicate these methods in a larger group of patients who have either damage localized to hippocampus or broader medial temporal lobe damage, to ascertain the necessity of the hippocampus in statistical learning. Patients with hippocampal damage consistently showed less learning overall compared with healthy comparison participants, consistent with an emerging consensus for hippocampal contributions to statistical learning. Interestingly, lesion size did not reliably predict performance. However, patients with hippocampal damage were not uniformly at chance and demonstrated above-chance performance in some task variants. These results suggest that hippocampus is necessary for statistical learning levels achieved by most healthy comparison participants but significant hippocampal pathology alone does not abolish such learning.

Classification and recognition in artificial grammar learning: Analysis of receiver operating characteristics

In two experiments we investigated recognition and classification judgements using an artificial grammar learning paradigm. In Experiment 1, when only new test items had to be judged, analysis of z-transformed receiver operating characteristics ( z-ROCs) revealed no differences between classification and recognition. In Experiment 2, where we included old test items, z-ROCs in the two tasks differed, suggesting that judgements relied on different types of information. The results are interpreted in terms of heuristics that people use when making classification and recognition judgements.

Interactions between Mental Models Used in Categorization and Experiential Knowledge of Specific Cases

Three experiments investigated the relation between recognition of specific cases and categorization in a double-task paradigm that requires both types of information (Estes, 1986b). Results indicated that recognition and categorization were often affected differently by experimental variables. However, mental models used in categorization sometimes hindered development of experiential (case-based) knowledge, leading to lower levels of case recognition and suboptimal categorization performance. When mental models were complex or difficult to discover (non-salient), subjects often used experiential knowledge to classify into categories, resulting in dependence between categorization and recognition. A model of interactions between the two tasks is proposed that postulates two separate but interacting types of knowledge.

Neuropsychological Investigations of Human Amnesia: Insights Into the Role of the Medial Temporal Lobes in Cognition

The past 30 years of research on human amnesia has yielded important changes in our understanding of the role of the medial temporal lobes (MTL) in memory. On the one hand, this body of evidence has highlighted that not all types of memory are impaired in patients with MTL lesions. On the other hand, this research has made apparent that the role of the MTL extends beyond the domain of long-term memory, to include working memory, perception, and future thinking. In this article, we review the discoveries and controversies that have characterized this literature and that set the stage for a new conceptualization of the role of the MTL in cognition. This shift toward a more nuanced understanding of MTL function has direct relevance for a range of clinical disorders in which the MTL is implicated, potentially shaping not only theoretical understanding but also clinical practice. (JINS, 2017, 23, 732-740).

Problems with visual statistical learning in developmental dyslexia

Previous research shows that dyslexic readers are impaired in their recognition of faces and other complex objects, and show hypoactivation in ventral visual stream regions that support word and object recognition. Responses of these brain regions are shaped by visual statistical learning. If such learning is compromised, people should be less sensitive to statistically likely feature combinations in words and other objects, and impaired visual word and object recognition should be expected. We therefore tested whether people with dyslexia showed diminished capability for visual statistical learning. Matched dyslexic and typical readers participated in tests of visual statistical learning of pairs of novel shapes that frequently appeared together. Dyslexic readers on average recognized fewer pairs than typical readers, indicating some problems with visual statistical learning. These group differences were not accounted for by differences in intelligence, ability to remember individual shapes, or spatial attention paid to the stimuli, but other attentional problems could play a mediating role. Deficiencies in visual statistical learning may in some cases prevent appropriate experience-driven shaping of neuronal responses in the ventral visual stream, hampering visual word and object recognition.

Dual-Process Models in Social and Cognitive Psychology: Conceptual Integration and Links to Underlying Memory Systems

Models postulating 2 distinct processing modes have been proposed in several topic areas within social and cognitive psychology. We advance a new conceptual model of the 2 processing modes. The structural basis of the new model is the idea, supported by psychological and neuropsychological evidence, that humans possess 2 memory systems. One system slowly learns general regularities, whereas the other can quickly form representations of unique or novel events. Associative retrieval or pattern completion in the slow-learning system elicited by a salient cue constitutes the effortless processing mode. The second processing mode is more conscious and effortful; it involves the intentional retrieval of explicit, symbolically represented rulesfrom either memory system and their use to guide processing. After presenting our model, we review existing dual-process models in several areas, emphasizing their similar assumptions of a quick, effortless processing mode that rests on well-learned prior associations and a second, more effortful processing mode that involves rule-based inferences and is employed only when people have both cognitive capacity and motivation. New insights and implications of the model for several topic areas are outlined.

A Unified Theoretical Framework for Cognitive Sequencing

The capacity to sequence information is central to human performance. Sequencing ability forms the foundation stone for higher order cognition related to language and goal-directed planning. Information related to the order of items, their timing, chunking and hierarchical organization are important aspects in sequencing. Past research on sequencing has emphasized two distinct and independent dichotomies: implicit vs. explicit and goal-directed vs. habits. We propose a theoretical framework unifying these two streams. Our proposal relies on brain's ability to implicitly extract statistical regularities from the stream of stimuli and with attentional engagement organizing sequences explicitly and hierarchically. Similarly, sequences that need to be assembled purposively to accomplish a goal require engagement of attentional processes. With repetition, these goal-directed plans become habits with concomitant disengagement of attention. Thus, attention and awareness play a crucial role in the implicit-to-explicit transition as well as in how goal-directed plans become automatic habits. Cortico-subcortical loops basal ganglia-frontal cortex and hippocampus-frontal cortex loops mediate the transition process. We show how the computational principles of model-free and model-based learning paradigms, along with a pivotal role for attention and awareness, offer a unifying framework for these two dichotomies. Based on this framework, we make testable predictions related to the potential influence of response-to-stimulus interval (RSI) on developing awareness in implicit learning tasks.

Memory Processes in Classification Learning: An Investigation of Amnesic Performance in Categorization of Dot Patterns and Artistic Styles

The role of conscious memory retrieval in category learning was investigated, using amnesic patients and matched control subjects on two classification tasks. Subjects attempted to learn to categorize both dot patterns and the paintings of three Italian Renaissance artists. The amnesic patients were capable of normal classification performance on the dot-pattern stimuli, but were severely impaired at learning to categorize the paintings. On tests of recognition of the classification stimuli, the control subjects were very accurate on the paintings, and only moderately accurate on the dot patterns; the amnesic patients were poorer than the control subjects on both item types, though they were above chance in their recognition of the paintings. These data suggest that amnesic patients are capable of simple, perceptually based category learning, but that richer, more complex stimuli may demand memory retrieval processes that they lack.

Developmental dissociation between the maturation of procedural memory and declarative memory

Declarative memory and procedural memory are known to be two fundamentally different kinds of memory that are dissociable in their psychological characteristics and measurement (explicit vs. implicit) and in the neural systems that subserve each kind of memory. Declarative memory abilities are known to improve from childhood through young adulthood, but the developmental maturation of procedural memory is largely unknown. We compared 10-year-old children and young adults on measures of declarative memory and working memory capacity and on four measures of procedural memory that have been strongly dissociated from declarative memory (mirror tracing, rotary pursuit, probabilistic classification, and artificial grammar). Children had lesser declarative memory ability and lesser working memory capacity than adults, but children exhibited learning equivalent to adults on all four measures of procedural memory. Therefore, declarative memory and procedural memory are developmentally dissociable, with procedural memory being adult-like by age 10years and declarative memory continuing to mature into young adulthood.

Event-related potentials reveal the relations between feature representations at different levels of abstraction

In this paper, we use behavioural methods and event-related potentials (ERPs) to explore the relations between informational and instantiated features, as well as the relation between feature abstraction and rule type. Participants are trained to categorize two species of fictitious animals and then identify perceptually novel exemplars. Critically, two groups are given a perfectly predictive counting rule that, according to Hannah and Brooks (2009. Featuring familiarity: How a familiar feature instantiation influences categorization. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 63, 263-275. Retrieved from http://doi.org/10.1037/a0017919), should orient them to using abstract informational features when categorizing the novel transfer items. A third group is taught a feature list rule, which should orient them to using detailed instantiated features. One counting-rule group were taught their rule before any exposure to the actual stimuli, and the other immediately after training, having learned the instantiations first. The feature-list group were also taught their rule after training. The ERP results suggest that at test, the two counting-rule groups processed items differently, despite their identical rule. This not only supports the distinction that informational and instantiated features are qualitatively different feature representations, but also implies that rules can readily operate over concrete inputs, in contradiction to traditional approaches that assume that rules necessarily act on abstract inputs.

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.

Domain generality versus modality specificity: the paradox of statistical learning

Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.

Neurocognitive mechanisms of statistical-sequential learning: what do event-related potentials tell us?

Statistical-sequential learning (SL) is the ability to process patterns of environmental stimuli, such as spoken language, music, or one's motor actions, that unfold in time. The underlying neurocognitive mechanisms of SL and the associated cognitive representations are still not well understood as reflected by the heterogeneity of the reviewed cognitive models. The purpose of this review is: (1) to provide a general overview of the primary models and theories of SL, (2) to describe the empirical research - with a focus on the event-related potential (ERP) literature - in support of these models while also highlighting the current limitations of this research, and (3) to present a set of new lines of ERP research to overcome these limitations. The review is articulated around three descriptive dimensions in relation to SL: the level of abstractness of the representations learned through SL, the effect of the level of attention and consciousness on SL, and the developmental trajectory of SL across the life-span. We conclude with a new tentative model that takes into account these three dimensions and also point to several promising new lines of SL research.

Project DyAdd: implicit learning in adult dyslexia and ADHD

In this study of the project DyAdd, implicit learning was investigated through two paradigms in adults (18-55 years) with dyslexia (n = 36) or with attention deficit/hyperactivity disorder (ADHD, n = 22) and in controls (n = 35). In the serial reaction time (SRT) task, there were no group differences in learning. However, those with ADHD exhibited faster RTs compared to other groups. In the artificial grammar learning (AGL) task, the groups did not differ from each other in their learning (i.e., grammaticality accuracy or similarity choices). Further, all three groups were sensitive to fragment overlap between learning and test-phase items (i.e., similarity choices were above chance). Grammaticality performance of control participants was above chance, but that of participants with dyslexia and participants with ADHD failed to differ from chance, indicating impaired grammaticality learning in these groups. While the main indices of AGL performance, grammaticality accuracy and similarity choices did not correlate with the neuropsychological variables that reflected dyslexia-related (phonological processing, reading, spelling, arithmetic) or ADHD-related characteristics (executive functions, attention), or intelligence, the explicit knowledge for the AGL grammar (i.e., ability to freely generate grammatical strings) correlated positively with the variables of phonological processing and reading. Further, SRT reaction times correlated positively with full scale intelligence quotient (FIQ). We conclude that, in AGL, learning difficulties of the underlying rule structure (as measured by grammaticality) are associated with dyslexia and ADHD. However, learning in AGL is not related to the defining neuropsychological features of dyslexia or ADHD. Instead, the resulting explicit knowledge relates to characteristics of dyslexia.

The necessity of the medial temporal lobe for statistical learning

The sensory input that we experience is highly patterned, and we are experts at detecting these regularities. Although the extraction of such regularities, or statistical learning (SL), is typically viewed as a cortical process, recent studies have implicated the medial temporal lobe (MTL), including the hippocampus. These studies have employed fMRI, leaving open the possibility that the MTL is involved but not necessary for SL. Here, we examined this issue in a case study of LSJ, a patient with complete bilateral hippocampal loss and broader MTL damage. In Experiments 1 and 2, LSJ and matched control participants were passively exposed to a continuous sequence of shapes, syllables, scenes, or tones containing temporal regularities in the co-occurrence of items. In a subsequent test phase, the control groups exhibited reliable SL in all conditions, successfully discriminating regularities from recombinations of the same items into novel foil sequences. LSJ, however, exhibited no SL, failing to discriminate regularities from foils. Experiment 3 ruled out more general explanations for this failure, such as inattention during exposure or difficulty following test instructions, by showing that LSJ could discriminate which individual items had been exposed. These findings provide converging support for the importance of the MTL in extracting temporal regularities.

Nonlinguistic learning in individuals with aphasia: effects of training method and stimulus characteristics

PURPOSE

The purpose of the current study was to explore nonlinguistic learning ability in individuals with aphasia, examining the impact of stimulus typicality and feedback on success with learning.

METHOD

Eighteen individuals with aphasia and 8 nonaphasic controls participated in this study. All participants completed 4 computerized, nonlinguistic category-learning tasks. Learning ability was probed under 2 methods of instruction: feedback-based (FB) and paired-associate (PA). The impact of task complexity on learning ability was also examined, comparing 2 stimulus conditions: typical and atypical. Performance was compared between groups and across conditions.

RESULTS

The controls were able to successfully learn categories under all conditions. For the individuals with aphasia, 2 patterns of performance arose: One subgroup of individuals was able to maintain learning across task manipulations and conditions; the other subgroup demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions.

CONCLUSION

Results support the hypothesis that impairments of general learning are present in individuals with aphasia. Some individuals demonstrated the ability to extract category information under complex training conditions; others learned only under conditions that were simplified and that emphasized salient category features. Overall, the typical training condition facilitated learning for all of the participants. Findings have implications for treatment, which are discussed.

Training intuitive decision making in a simulated real-world environment

OBJECTIVE

We investigated whether naturalistic, intuitive (pattern recognition-based) decision making can be developed via implicit statistical learning in a simulated real-world environment.

BACKGROUND

To our knowledge, no definitive studies have actually shown that implicit learning plays a causal role in the development of intuitive decision making when the latter is defined as pattern recognition of real-world, or simulated real-world, environmental situations.

METHOD

The simulated environment was presented dynamically so as to induce a sense of simulated locomotion through the scene and over sequences of objects on the ground. During training, participants passively viewed the objects sequences; during test, participants made intuitive decisions about related or unrelated sequences.

RESULTS

Intuitive decision making can be developed via implicit learning. Articulatory suppression, which affects working memory, exerted a significant inhibitory effect on the training of intuitive decision making. Intuitive decision making trained in the simulated environment fully transferred to a flat display (but not vice versa).

CONCLUSION

Intuitive decision making is developed by an implicit learning process that is engaged by the meaning inherent in naturalistic scenes.

APPLICATION

Implicit learning can be used for training intuitive decision making.

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.

Sources of confidence judgments in implicit cognition

Subjective reports of confidence are frequently used as a measure of awareness in a variety of fields, including artificial grammar learning. However, little is known about what information is used to make confidence judgments and whether there are any possible sources of information used to discriminate between items that are unrelated to confidence. The data reported here replicate an earlier experiment by Vokey and Brooks (1992) and show that grammaticality decisions are based on both the grammatical status of items and their similarity to study exemplars. The key finding is that confidence ratings made on a continuous scale (50%-100%) are closely related to grammaticality but are unrelated to all of the measures of similarity that were tested. By contrast, confidence ratings made on a binary scale (high vs. low) are related to both grammaticality and similarity. The data confirm an earlier finding (Tunney & Shanks, 2003) that binary confidence ratings are more sensitive to low levels of awareness than continuous ratings are and suggest that participants are conscious of all the information acquired in artificial grammar 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.

The role of the basal ganglia in learning and memory: insight from Parkinson's disease

It has long been known that memory is not a single process. Rather, there are different kinds of memory that are supported by distinct neural systems. This idea stemmed from early findings of dissociable patterns of memory impairments in patients with selective damage to different brain regions. These studies highlighted the role of the basal ganglia in non-declarative memory, such as procedural or habit learning, contrasting it with the known role of the medial temporal lobes in declarative memory. In recent years, major advances across multiple areas of neuroscience have revealed an important role for the basal ganglia in motivation and decision making. These findings have led to new discoveries about the role of the basal ganglia in learning and highlighted the essential role of dopamine in specific forms of learning. Here we review these recent advances with an emphasis on novel discoveries from studies of learning in patients with Parkinson's disease. We discuss how these findings promote the development of current theories away from accounts that emphasize the verbalizability of the contents of memory and towards a focus on the specific computations carried out by distinct brain regions. Finally, we discuss new challenges that arise in the face of accumulating evidence for dynamic and interconnected memory systems that jointly contribute to learning.

A model for memory systems based on processing modes rather than consciousness

Prominent models of human long-term memory distinguish between memory systems on the basis of whether learning and retrieval occur consciously or unconsciously. Episodic memory formation requires the rapid encoding of associations between different aspects of an event which, according to these models, depends on the hippocampus and on consciousness. However, recent evidence indicates that the hippocampus mediates rapid associative learning with and without consciousness in humans and animals, for long-term and short-term retention. Consciousness seems to be a poor criterion for differentiating between declarative (or explicit) and non declarative (or implicit) types of memory. A new model is therefore required in which memory systems are distinguished based on the processing operations involved rather than by consciousness.

Unconscious knowledge: A survey

The concept of unconscious knowledge is fundamental for an understanding of human thought processes and mentation in general; however, the psychological community at large is not familiar with it. This paper offers a survey of the main psychological research currently being carried out into cognitive processes, and examines pathways that can be integrated into a discipline of unconscious knowledge. It shows that the field has already a defined history and discusses some of the features that all kinds of unconscious knowledge seem to share at a deeper level. With the aim of promoting further research, we discuss the main challenges which the postulation of unconscious cognition faces within the psychological community.

How successful is errorless learning in supporting memory for high and low-level knowledge in dementia?

Errorless learning has been shown to be very successful in the rehabilitation of memory problems particularly in patients with severe forms of memory impairment. Much of this research has focused on testing knowledge of specific details studied, ignoring any additional, higher-level knowledge that patients may have acquired during the learning process. Hence, it is pertinent to ask whether errorless learning is equally successful in the acquisition of high and low-level knowledge. In this paper, we present results of several studies comparing the effectiveness of errorless and standard trial-and-error methods in acquisition of high and low-level knowledge in people diagnosed with dementia and non-impaired controls. In Study 1, participants were asked to learn novel face-name-occupation associations; and knowledge across a range of levels, from very general (i.e., high-level) to very specific (i.e., low-level), was examined. For patients with probable Alzheimer's disease and controls there was evidence of increased benefit from errorless training in general, but the technique was most beneficial for patients attempting to retrieve specific detail. Study 2 was conducted to address the problem raised by the failure in Study 1 to manipulate learning condition at our highest knowledge level. This novel manipulation was successful, but neither of the patients received the standard benefit from errorless training. Study 3, involving a small group of dementia patients with mixed diagnoses, was conducted to replicate findings from Study 1. Results from the group analysis confirmed that the benefit obtained from errorless learning increased as a function of knowledge specificity, but again several patients failed to show a consistent effect of learning condition. Implications for use of the errorless technique are discussed.