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Integrating individual actions into coherent, organised behavioural units, a process called chunking, is a fundamental, evolutionarily conserved process that renders actions automatic. In vertebrates, evidence points to the basal ganglia - a complex network believed to be involved in action selection - as a key component of action sequence encoding, although the underlying mechanisms are only just beginning to be understood. Central pattern generators control many innate automatic behavioural sequences that form some of the most basic behaviours in an animal's repertoire, and in vertebrates, brainstem and spinal pattern generators are under the control of higher order structures such as the basal ganglia. Evidence suggests that the basal ganglia play a crucial role in the concatenation of simpler behaviours into more complex chunks, in the context of innate behavioural sequences such as chain grooming in rats, as well as sequences in which innate capabilities and learning interact such as birdsong, and sequences that are learned from scratch, such as lever press sequences in operant behaviour. It has been proposed that the role of the striatum, the largest input structure of the basal ganglia, might lie in selecting and allowing the relevant central pattern generators to gain access to the motor system in the correct order, while inhibiting other behaviours. As behaviours become more complex and flexible, the pattern generators seem to become more dependent on descending signals. Indeed, during learning, the striatum itself may adopt the functional characteristics of a higher order pattern generator, facilitated at the microcircuit level by striatal neuropeptides., (© 2023 the author(s), published by De Gruyter, Berlin/Boston.)

Statistical learning relies on detecting the frequency of co-occurrences of items and has been proposed to be crucial for a variety of learning problems, notably to learn and memorize words from fluent speech. Endress and Johnson (2021) (hereafter EJ) recently showed that such results can be explained based on simple memory-less correlational learning mechanisms such as Hebbian Learning. Tovar and Westermann (2022) (hereafter TW) reproduced these results with a different Hebbian model. We show that the main differences between the models are whether temporal decay acts on both the connection weights and the activations (in TW) or only on the activations (in EJ), and whether interference affects weights (in TW) or activations (in EJ). Given that weights and activations are linked through the Hebbian learning rule, the networks behave similarly. However, in contrast to TW, we do not believe that neurophysiological data are relevant to adjudicate between abstract psychological models with little biological detail. Taken together, both models show that different memory-less correlational learning mechanisms provide a parsimonious account of Statistical Learning results. They are consistent with evidence that Statistical Learning might not allow learners to learn and retain words, and Statistical Learning might support predictive processing instead., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Chunking mechanisms are central to several cognitive processes and notably to the acquisition of visuo-motor sequences. Individuals segment sequences into chunks of items to perform visuo-motor tasks more fluidly, rapidly, and accurately. However, the exact dynamics of chunking processes in the case of extended practice remain unclear. Using an operant conditioning device, 18 Guinea baboons (Papio papio) produced a fixed sequence of nine movements during 1000 trials by pointing to a moving target on a touch screen. Response times analyses revealed a specific chunking pattern of the sequence for each baboon. More importantly, we found that these patterns evolved during the course of the experiment, with chunks becoming progressively fewer and longer. We identified two chunk reorganization mechanisms: the recombination of preexisting chunks and the concatenation of two distinct chunks into a single one. These results provide new evidence on chunking mechanisms in sequence learning and challenge current models of associative and statistical learning., (© 2022 Cognitive Science Society LLC.)

Learning often requires splitting continuous signals into recurring units, such as the discrete words constituting fluent speech; these units then need to be encoded in memory. A prominent candidate mechanism involves statistical learning of co-occurrence statistics like transitional probabilities (TPs), reflecting the idea that items from the same unit (e.g., syllables within a word) predict each other better than items from different units. TP computations are surprisingly flexible and sophisticated. Humans are sensitive to forward and backward TPs, compute TPs between adjacent items and longer-distance items, and even recognize TPs in novel units. We explain these hallmarks of statistical learning with a simple model with tunable, Hebbian excitatory connections and inhibitory interactions controlling the overall activation. With weak forgetting, activations are long-lasting, yielding associations among all items; with strong forgetting, no associations ensue as activations do not outlast stimuli; with intermediate forgetting, the network reproduces the hallmarks above. Forgetting thus is a key determinant of these sophisticated learning abilities. Further, in line with earlier dissociations between statistical learning and memory encoding, our model reproduces the hallmarks of statistical learning in the absence of a memory store in which items could be placed., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Many motor skills are learned with the help of instructions. In the context of complex motor sequences, instructions often break down the movement into chunks that can then be practiced in isolation. Thus, instructions shape an initial cognitive representation of the skill, which in turn guides practice. Are there ways of breaking up a motor sequence that are better than others? If participants are instructed in a way that hinders performance, how much practice does it take to overcome the influence of the instruction? To answer these questions, we used a paradigm in which participants were asked to perform finger sequences as fast and accurately as possible on a keyboard-like device. In the initial phases of training, participants had to explicitly remember and practice two- or three-digit chunks. These chunks were then combined to form seven 11-digit sequences that participants practiced for the remainder of the study. Each sequence was broken up into chunks in a way such that the instruction was either aligned or misaligned with the basic execution-level constraints. We found that misaligned chunk instruction led to an initial performance deficit compared with the aligned chunk instruction. Overall, instructions still influenced the temporal pattern of performance after 10 days of subsequent training, with shorter interpress intervals within a chunk compared with between chunks. However, for the misaligned instructed sequences, this temporal pattern was altered more rapidly, such that participants could overcome the induced performance deficit in the last week. At the end of training, participants found idiosyncratic, but interindividually stable, ways of performing each sequence. NEW & NOTEWORTHY Instructions often break down motor sequences into smaller parts, such that they can be more easily remembered. Here, we show that different ways of breaking down a finger sequence can subsequently lead to better or worse performance. The initial instruction still influenced the temporal performance pattern after 10 days of practice. The results demonstrate that the initial cognitive representation of a motor skill strongly influences how a skill is learned and performed.

The acquisition of sequential knowledge is pivotal in forming skilled behavior. Despite extensive research of sequence learning, much remains unknown regarding what knowledge participants learn in such studies, and how that knowledge takes form over time. By tracking eye-movements made before stimuli appear on screen during a serial reaction time (SRT) task, we devised a method for assessing learning at the individual participant level in an item-based resolution. Our method enables uncovering what participants actually learn about the sequence presented to them, and when. Results demonstrate that learning is more heterogeneous than previously thought, driven by learning both of chunks and of statistics embedded in the sequence. Also, learning develops rapidly, but in a fragmented and non-sequential manner, eventually encompassing only a subset of available regularities. The tools developed in this work may aid in further dissociating processes and mechanisms underlying sequence learning and its impairments, in normal and in clinical populations., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short-term memory is fundamentally shaped by long-term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word-like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test-retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two-alternative forced-choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature., (© 2020 Cognitive Science Society, Inc.)

Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms for segmenting continuous streams of sensory input and establishing representations of dependencies remain largely unknown, as do the transformations and computations occurring between the brain regions involved in these aspects of sequence processing. We propose a blueprint for a neurobiologically informed and informing computational model of sequence processing (entitled: Vector-symbolic Sequencing of Binding INstantiating Dependencies, or VS-BIND). This model is designed to support the transformation of serially ordered elements in sensory sequences into structured representations of bound dependencies, readily operates on multiple timescales, and encodes or decodes sequences with respect to chunked items wherever dependencies occur in time. The model integrates established vector symbolic additive and conjunctive binding operators with neurobiologically plausible oscillatory dynamics, and is compatible with modern spiking neural network simulation methods. We show that the model is capable of simulating previous findings from structured sequence processing tasks that engage fronto-temporal regions, specifying mechanistic roles for regions such as prefrontal areas 44/45 and the frontal operculum during interactions with sensory representations in temporal cortex. Finally, we are able to make predictions based on the configuration of the model alone that underscore the importance of serial position information, which requires input from time-sensitive cells, known to reside in the hippocampus and dorsolateral prefrontal cortex. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.

Implicit learning and statistical learning are two contemporary approaches to the long-standing question in psychology and cognitive science of how organisms pick up on patterned regularities in their environment. Although both approaches focus on the learner's ability to use distributional properties to discover patterns in the input, the relevant research has largely been published in separate literatures and with surprisingly little cross-pollination between them. This has resulted in apparently opposing perspectives on the computations involved in learning, pitting chunk-based learning against probabilistic learning. In this paper, I trace the nearly century-long historical pedigree of the two approaches to learning and argue for their integration under the heading of "implicit statistical learning." Building on basic insights from the memory literature, I sketch a framework for statistically based chunking that aims to provide a unified basis for understanding implicit statistical learning., (Copyright © 2018 Cognitive Science Society, Inc.)

Much research has documented infants' sensitivity to statistical regularities in auditory and visual inputs, however the manner in which infants process and represent statistically defined information remains unclear. Two types of models have been proposed to account for this sensitivity: statistical models, which posit that learners represent statistical relations between elements in the input; and chunking models, which posit that learners represent statistically-coherent units of information from the input. Here, we evaluated the fit of these two types of models to behavioral data that we obtained from 8-month-old infants across four visual sequence-learning experiments. Experiments examined infants' representations of two types of structures about which statistical and chunking models make contrasting predictions: illusory sequences (Experiment 1) and embedded sequences (Experiments 2-4). In all four experiments, infants discriminated between high probability sequences and low probability part-sequences, providing strong evidence of learning. Critically, infants also discriminated between high probability sequences and statistically-matched sequences (illusory sequences in Experiment 1, embedded sequences in Experiments 2-3), suggesting that infants learned coherent chunks of elements. Experiment 4 examined the temporal nature of chunking, and demonstrated that the fate of embedded chunks depends on amount of exposure. These studies contribute important new data on infants' visual statistical learning ability, and suggest that the representations that result from infants' visual statistical learning are best captured by chunking models., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Why are children better language learners than adults despite being worse at a range of other cognitive tasks? Here, we explore the role of multiword sequences in explaining L1-L2 differences in learning. In particular, we propose that children and adults differ in their reliance on such multiword units (MWUs) in learning, and that this difference affects learning strategies and outcomes, and leads to difficulty in learning certain grammatical relations. In the first part, we review recent findings that suggest that MWUs play a facilitative role in learning. We then discuss the implications of these findings for L1-L2 differences: We hypothesize that adults are both less likely to extract MWUs and less capable of benefiting from them in the process of learning. In the next section, we draw on psycholinguistic, developmental, and computational findings to support these predictions. We end with a discussion of the relation between this proposal and other accounts of L1-L2 difficulty., (Copyright © 2017 Cognitive Science Society, Inc.)

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'., (© 2016 The Author(s).)

Infants, like adults, can maintain only a few items in working memory, but can overcome this limit by creating more efficient representations, or "chunks." Previous research shows that infants can form chunks using shared features or spatial proximity between objects. Here we asked whether infants also can create chunked representations using regularities that unfold over time. Thirteen-month old infants first were familiarized with four objects of different shapes and colors, presented in successive pairs. For some infants, the identities of objects in each pair varied randomly across familiarization (Experiment 1). For others, the objects within a pair always co-occurred, either in consistent relative spatial positions (Experiment 2a) or varying spatial positions (Experiment 2b). Following familiarization, infants saw all four objects hidden behind a screen and then saw the screen lifted to reveal either four objects or only three. Infants in Experiment 1, who had been familiarized with random object pairings, failed to look longer at the unexpected 3-object outcome; they showed the same inability to concurrently represent four objects as in other studies of infant working memory. In contrast, infants in Experiments 2a and 2b, who had been familiarized with regularly co-occurring pairs, looked longer at the unexpected outcome. These infants apparently used the co-occurrence between individual objects during familiarization to form chunked representations that were later deployed to track the objects as they were hidden at test. In Experiment 3, we confirmed that the familiarization affected infants' ability to remember the occluded objects rather than merely establishing longer-term memory for object pairs. Following familiarization to consistent pairs, infants who were not shown a hiding event (but merely saw the same test outcomes as in Experiments 2a and b) showed no preference for arrays of three versus four objects. Finally, in Experiments 4 and 5, we asked whether infants also remembered the specific identities of the objects in each chunk. In Experiment 4, we confirmed that infants remembered objects' identities in smaller arrays that did not require chunking. Next, in Experiment 5, we asked whether infants also remembered objects' identities in larger arrays that had been chunked on the basis of temporal regularities. Following a familiarization phase identical to that in Experiment 2a, we hid all four objects and then revealed either these same four objects, or four objects of which two had unexpectedly changed shape and color. Surprisingly, infants failed to look longer at the identity change outcome. Taken together, our results suggest that infants can use temporal regularities between objects to increase memory for objects' existence, but not necessarily for objects' identities., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Many everyday skills are unconsciously learned through repetitions of the same behaviour by binding independent motor acts into unified sets of actions. However, our ability to be consciously aware of producing newly and highly trained motor skills raises the question of the role played by conscious awareness of action upon skill acquisition. In this study we strengthened conscious awareness of self-produced sequential finger movements by way of asking participants to judge their performance in terms of maximal fluency after each trial. Control conditions in which participants did not make any judgment or performance-unrelated judgments were also included. Findings indicate that conscious awareness of action, enhanced via subjective appraisal of motor efficiency, potentiates sensorimotor learning and skilful motor production in optimising the processing and sequencing of action units, as compared to the control groups. The current work lends support to the claim that the learning and skilful expression of sensorimotor behaviours might be grounded upon our ability to be consciously aware of our own motor capability and efficiency., (Copyright © 2014 Elsevier B.V. All rights reserved.)

There is large evidence that infants are able to exploit statistical cues to discover the words of their language. However, how they proceed to do so is the object of enduring debates. The prevalent position is that words are extracted from the prior computation of statistics, in particular the transitional probabilities between syllables. As an alternative, chunk-based models posit that the sensitivity to statistics results from other processes, whereby many potential chunks are considered as candidate words, then selected as a function of their relevance. These two classes of models have proven to be difficult to dissociate. We propose here a procedure, which leads to contrasted predictions regarding the influence of a first language, L1, on the segmentation of a second language, L2. Simulations run with PARSER (Perruchet & Vinter, 1998), a chunk-based model, predict that when the words of L1 become word-external transitions of L2, learning of L2 should be depleted until reaching below chance level, at least before extensive exposure to L2 reverses the effect. In the same condition, a transitional-probability based model predicts above-chance performance whatever the duration of exposure to L2. PARSER's predictions were confirmed by experimental data: Performance on a two-alternative forced choice test between words and part-words from L2 was significantly below chance even though part-words were less cohesive in terms of transitional probabilities than words., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Although it is known that a change in a learned motor sequence slows performance down, it is yet unknown if this impairment varies depending on whether the changed element is early or late in the sequence. In Experiment 1, we showed greater impairment in performance when changing the third vs. the sixth element in a 7-element sequence. The impairment was greater for the deviant and the following elements than for the preceding ones. In Experiment 2, we replicated the results of Experiment 1 and expanded them by showing that a change in the third element of a 4-element sequence produced similar results to those of the late change condition in the long 7-element sequence. It is proposed that during practice, associative relations between the sequence elements are formed together with the representation of the whole chunk. Following the change in sequence, the chunk representation is impaired and performance mainly reflects the associative links between the elements. An early change hampers these associative relations to a greater extent than a late change, and as a consequence slows performance down more than a late change does. The implications and advantages of such a mechanism are discussed., (Copyright © 2013 Elsevier B.V. All rights reserved.)

A fundamental issue in cognitive science concerns the mentalprocesses that underlie the formation and retrieval of conceptsin the short-term and long-term memory (STM and LTMrespectively). This study advances Chunking Theory and itscomputational embodiment CHREST to propose a singlemodel that accounts for significant aspects of conceptformation in the domains of literature and music. The proposedmodel inherits CHREST’s architecture with its integratedSTM/LTM stores, while also adding a moving attentionwindow and an “LTM chunk activation” mechanism. Theseadditions address the overly destructive nature of primacyeffect in discrimination network based architectures andexpand Chunking Theory to account for learning, retrieval andcategorisation of complex sequential symbolic patterns – likereal-life text and written music scores. The model was trainedthrough exposure to labelled stimuli and learned to categoriseclassical poets/writers and composers. The model categorisedpreviously unseen literature pieces by Homer, Chaucer,Shakespeare, Walter Scott, Dickens and Joyce, as well asunseen sheet music scores by Bach, Mozart, Beethoven andChopin. These findings offer further support to mechanismsproposed by Chunking Theory and expand it into thepsychology of music.

A common assumption in the cognitive sciences is thatartificial and natural language learning rely on sharedmechanisms. However, attempts to bridge the two haveyielded ambiguous results. We suggest that an empiricaldisconnect between the computations employed duringlearning and the methods employed at test may explain thesemixed results. Further, we propose statistically-basedchunking as a potential computational link between artificialand natural language learning. We compare the acquisition ofnon-adjacent dependencies to that of natural languagestructure using two types of tasks: reflection-based 2AFCmeasures, and processing-based recall measures, the latterbeing more computationally analogous to the processes usedduring language acquisition. Our results demonstrate thattask-type significantly influences the correlations observedbetween artificial and natural language acquisition, withreflection-based and processing-based measures correlatingwithin – but not across – task-type. These findings havefundamental implications for artificial-to-natural languagecomparisons, both methodologically and theoretically.

Attempts to connect individual differences in statisticallearning with broader aspects of cognition have receivedconsiderable attention, but have yielded mixed results. Apossible explanation is that statistical learning is typicallytested using the two-alternative forced choice (2AFC) task.As a meta-cognitive task relying on explicit familiarityjudgments, 2AFC may not accurately capture implicitlyformed statistical computations. In this paper, we adapt theclassic serial-recall memory paradigm to implicitly teststatistical learning in a statistically-induced chunking recall(SICR) task. We hypothesized that artificial languageexposure would lead subjects to chunk recurring statisticalpatterns, facilitating recall of words from the input.Experiment 1 demonstrates that SICR offers more fine-grained insights into individual differences in statisticallearning than 2AFC. Experiment 2 shows that SICR hashigher test-retest reliability than that reported for 2AFC. Thus,SICR offers a more sensitive measure of individualdifferences, suggesting that basic chunking abilities mayexplain statistical learning.

Several recent empirical findings have reinforced the notionthat a basic learning and memory skill—chunking—plays afundamental role in language processing. Here, we provideevidence that chunking shapes sentence processing at multiplelevels of linguistic abstraction, consistent with a recenttheoretical proposal by Christiansen and Chater (2016).Individual differences in chunking ability at two differentlevels is shown to predict on-line sentence processing inseparate ways: i) phonological chunking ability, as assessedby a variation on the non-word repetition task, predictsprocessing of complex sentences featuring phonologicaloverlap; ii) multiword chunking ability, as assessed by avariation on the serial recall task, is shown to predict readingtimes for sentences featuring long-distance number agreementwith locally distracting number-marked nouns. Together, ourfindings suggest that individual differences in chunkingability shape language processing at multiple levels ofabstraction, consistent with the notion of language acquisitionas learning to process.

After elaborating the definition of working memory, the relationship between short-term memory and working memory, chunking in SLA and the relationship between short-term memory and chunking, this paper proves the importance of chunking through the experiment: the students' capacity in fast reading, reading in depth, listening and cloze from experimental group was affected by vocabulary depth through learning the theory of chunking and states how to apply chunking to second language acquisition. [ABSTRACT FROM AUTHOR]

The long-term effects of adolescent exposure to methylphenidate (MPD) on adult cognitive capacity are largely unknown. We utilized a serial multiple choice (SMC) task, which is a sequential learning paradigm for studying complex learning, to observe the effects of methylphenidate exposure during adolescence on later serial pattern acquisition during adulthood. Following 20.0 mg/kg/day MPD or saline exposure for 5 days/week for 5 weeks during adolescence, male rats were trained to produce a highly structured serial response pattern in an octagonal operant chamber for water reinforcement as adults. During a transfer phase, a violation to the previously-learned pattern structure was introduced as the last element of the sequential pattern. Results indicated that while rats in both groups were able to learn the training and transfer patterns, adolescent exposure to MPD impaired learning for some aspects of pattern learning in the training phase which are learned using discrimination learning or serial position learning. In contrast adolescent exposure to MPD had no effect on other aspects of pattern learning which have been shown to tap into rule learning mechanisms. Additionally, adolescent MPD exposure impaired learning for the violation element in the transfer phase. This indicates a deficit in multi-item learning previously shown to be responsible for violation element learning. Thus, these results clearly show that adolescent MPD produced multiple cognitive impairments in male rats that persisted into adulthood long after MPD exposure ended. [ABSTRACT FROM AUTHOR]

Humans are remarkably sensitive to the statistical structure of language. However, different mechanisms have been proposed to account for such statistical sensitivities. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks, which learn by predictive computation, and PARSER, which learns chunks as a byproduct of general principles of associative learning and memory. In the first stage, a semiartificial language paradigm was used to gather human data. In the second stage, a simulation paradigm was then used to compare the patterns of performance of the SRN and PARSER. After the human adults and the computational models were trained on sentences from the semiartificial language with probabilistic syntax, their learning outcomes were compared. Neither model was able to fully reproduce the human data, which may indicate less robust statistical learning effects in adults; however, PARSER was able to simulate more of the adult learning data than the SRN, suggesting a possible role for chunk formation in early phases of adult learning of second language syntax. [ABSTRACT FROM AUTHOR]

Understanding habits at a biological level requires a combination of behavioral observations and measures of ongoing neural activity. Theoretical frameworks as well as definitions of habitual behaviors emerging from classic behavioral research have been enriched by new approaches taking account of the identification of brain regions and circuits related to habitual behavior. Together, this combination of experimental and theoretical work has provided key insights into how brain circuits underlying action-learning and action-selection are organized, and how a balance between behavioral flexibility and fixity is achieved. New methods to monitor and manipulate neural activity in real time are allowing us to have a first look "under the hood" of a habit as it is formed and expressed. Here we discuss ideas emerging from such approaches. We pay special attention to the unexpected findings that have arisen from our own experiments suggesting that habitual behaviors likely require the simultaneous activity of multiple distinct components, or operators, seen as responsible for the contrasting dynamics of neural activity in both cortico-limbic and sensorimotor circuits recorded concurrently during different stages of habit learning. The neural dynamics identified thus far do not fully meet expectations derived from traditional models of the structure of habits, and the behavioral measures of habits that we have made also are not fully aligned with these models. We explore these new clues as opportunities to refine an understanding of habits. [ABSTRACT FROM AUTHOR]

Abstract: In human designer usage, symbols have a rich semantics, grounded on experience, which permits flexible usage — e.g. design ideation is improved by meanings triggered by contrastive words. In computational usage however, symbols are syntactic tokens whose semantics is mostly left to the implementation, resulting in brittle failures in many knowledge-based systems. Here we ask if one may define symbols in computational design as {label,meaning} pairs, as opposed to merely the label. We consider three questions that must be answered to bootstrap a symbol learning process: (a) which concepts are most relevant in a given domain, (b) how to define the semantics of such symbols, and (c) how to learn labels for these so as to form a grounded symbol. We propose that relevant symbols may be discovered by learning patterns of functional viability. The stable patterns are information-conserving codes, also called chunks in cognitive science, which relate to the process of acquiring expertise in humans. Regions of a design space that contain functionally superior designs can be mapped to a lower-dimensional manifold; the inter-relations of the design variables discovered thus constitute the chunks. Using these as the initial semantics for symbols, we show how the system can acquire labels for them by communicating with human designers. We demonstrate the first steps in this process in our baby designer approach, by learning two early grounded symbols, tight and loose. [Copyright &y& Elsevier]

Abstract: Previous brain imaging studies investigating motor sequence complexity have mainly examined the effect of increasing the length of pre-learned sequences. The novel contribution of this research is that we varied the structure of complex visuo-motor sequences along two different dimensions using mxn paradigm. The complexity of sequences is increased from 12 movements (organized as a 2×6 task) to 24 movements (organized as 4×6 and 2×12 tasks). Behavioral results indicate that although the success rate attained was similar across the two complex tasks (2×12 and 4×6), a greater decrease in response times was observed for the 2×12 compared to the 4×6 condition at an intermediate learning stage. This decrease is possibly related to successful chunking across sets in the 2×12 task. In line with this, we observed a selective activation of the fronto-parietal network. Shifts of activation were observed from the ventral to dorsal prefrontal, lateral to medial premotor and inferior to superior parietal cortex from the early to intermediate learning stage concomitant with an increase in hyperset length. We suggest that these selective activations and shifts in activity during complex sequence learning are possibly related to chunking of motor sequences. [Copyright &y& Elsevier]

In the present study, we investigated possible influences on the unitization of responses. In Experiments 1, 2, 3, and 6, we found that when the same small fragment (i.e., a few consecutive responses in a sequence) was presented as part of two larger sequences, participants responded to it faster when it was part of the sequence that was presented more often. This indicates that chunking can be driven by task-relevant considerations, as opposed to co-occurrence. The results are discussed in the context of chunking theories and the relevant motor learning literature. [ABSTRACT FROM AUTHOR]

Abstract: We investigated the role of practice in the acquisition of chess expertise by submitting a questionnaire to 104 players of different skill levels. Players had to report their chess rating, the number of hours of individual and group practice, their use of different learning resources and activities, and whether they had been trained by a coach. The use of archival data enabled us to track the rating of some of the players throughout their career. We found that there was a strong correlation between chess skill and number of hours of practice. Moreover, group practice was a better predictor of high-level performance than individual practice. We also found that masters had a higher chess rating than expert players after only three years of serious dedication to chess, although there were no differences in the number of hours of practice. The difference that may explain the variation in rating is that masters start practising at an earlier age than experts. Finally, we found that activities such as reading books and using computer software (game databases, but not playing programs) were important for the development of high-level performance. Together with previous data and theories of expert performance, our results indicate limits in the deliberate practice framework and make suggestions on how best to carry out learning in chess and in other fields. [Copyright &y& Elsevier]

The ability to remember visual stimuli over a short delay period is limited by the small capacity of visual working memory (VWM). Here the authors investigate the role of learning in enhancing VWM. Participants saw 2 spatial arrays separated by a 1-s interval. The 2 arrays were identical except for 1 location. Participants had to detect the difference. Unknown to the participants, some spatial arrays would repeat once every dozen trials or so for up to 32 repetitions. Spatial VWM performance increased significantly when the same location changed across display repetitions, but not at all when different locations changed from one display repetition to another. The authors suggest that a major role of learning in VWM is to mediate which information gets retained, rather than to directly increase VWM capacity. [ABSTRACT FROM AUTHOR]

A novel hierarchical neural network architecture for sensory-motor learning and behavior generation is proposed. Two levels of forward model neural networks are operated on different time scales while parametric interactions are allowed between the two network levels in the bottom-up and top-down directions. The models are examined through experiments of behavior learning and generation using a real robot arm equipped with a vision system. The results of the learning experiments showed that the behavioral patterns are learned by self-organizing the behavioral primitives in the lower level and combining the primitives sequentially in the higher level. The results contrast with prior work by Pawelzik et al. [Neural Comput. 8 (1996) 340], Tani and Nolfi [From animals to animats, 1998], and Wolpert and Kawato [Neural Networks 11 (1998) 1317] in that the primitives are represented in a distributed manner in the network in the present scheme whereas, in the prior work, the primitives were localized in specific modules in the network. Further experiments of on-line planning showed that the behavior could be generated robustly against a background of real world noise while the behavior plans could be modified flexibly in response to changes in the environment. It is concluded that the interaction between the bottom-up process of recalling the past and the top-down process of predicting the future enables both robust and flexible situated behavior. [Copyright &y& Elsevier]