Showing papers on "Sequence learning published in 2003"

Taking the load off a learner's mind: Instructional design for complex learning.

Abstract: Complex learning aims at the integration of knowledge, skills, and attitudes; the coordination of qualitatively different constituent skills; and the transfer of what is learned to daily life or work settings. Recent instructional theories stress authentic learning tasks as the driving force for learning; but due to the complexity of those tasks, learning may be hampered by the limited processing capacity of the human mind. In this article we present a framework for scaffolding practice and just-in-time information presentation, aiming to control cognitive load effectively. We briefly describe a design model for complex learning consistent with cognitive load theory. Theoretical and practical implications of the presented framework are discussed.

The cognitive and neural architecture of sequence representation.

Abstract: The authors theorize that 2 neurocognitive sequence-learning systems can be distinguished in serial reaction time experiments, one dorsal (parietal and supplementary motor cortex) and the other ventral (temporal and lateral prefrontal cortex). Dorsal system learning is implicit and associates noncategorized stimuli within dimensional modules. Ventral system learning can be implicit or explicit It also allows associating events across dimensions and therefore is the basis of cross-task integration or interference, depending on degree of cross-task correlation of signals. Accordingly, lack of correlation rather than limited capacity is responsible for dual-task effects on learning. The theory is relevant to issues of attentional effects on learning; the representational basis of complex, sequential skills; hippocampal-versus basal ganglia-based learning; procedural versus declarative memory; and implicit versus explicit memory.

Swinging in the brain: shared neural substrates for behaviors related to sequencing and music.

Abstract: Music consists of precisely patterned sequences of both movement and sound that engage the mind in a multitude of experiences. We move in response to music and we move in order to make music. Because of the intimate coupling between perception and action, music provides a panoramic window through which we can examine the neural organization of complex behaviors that are at the core of human nature. Although the cognitive neuroscience of music is still in its infancy, a considerable behavioral and neuroimaging literature has amassed that pertains to neural mechanisms that underlie musical experience. Here we review neuroimaging studies of explicit sequence learning and temporal production—findings that ultimately lay the groundwork for understanding how more complex musical sequences are represented and produced by the brain. These studies are also brought into an existing framework concerning the interaction of attention and time-keeping mechanisms in perceiving complex patterns of information that are distributed in time, such as those that occur in music.

Chunking during human visuomotor sequence learning

Abstract: Motor sequence learning is a process whereby a series of elementary movements is re-coded into an efficient representation for the entire sequence. Here we show that human subjects learn a visuomotor sequence by spontaneously chunking the elementary movements, while each chunk acts as a single memory unit. The subjects learned to press a sequence of 10 sets of two buttons through trial and error. By examining the temporal patterns with which subjects performed a visuomotor sequence, we found that the subjects performed the 10 sets as several clusters of sets, which were separated by long time gaps. While the overall performance time decreased by repeating the same sequence, the clusters became clearer and more consistent. The cluster pattern was uncorrelated with the distance of hand movements and was different across subjects who learned the same sequence. We then split a learned sequence into three segments, while preserving or destroying the clusters in the learned sequence, and shuffled the segments. The performance on the shuffled sequence was more accurate and quicker when the clusters in the original sequence were preserved than when they were destroyed. The results suggest that each cluster is processed as a single memory unit, a chunk, and is necessary for efficient sequence processing. A learned visuomotor sequence is hierarchically represented as chunks that contain several elementary movements. We also found that the temporal patterns of sequence performance transferred from the nondominant to dominant hand, but not vice versa. This may suggest a role of the dominant hemisphere in storage of learned chunks. Together with our previous unit-recording and imaging studies that used the same learning paradigm, we predict specific roles of the dominant parietal area, basal ganglia, and presupplementary motor area in the chunking.

Attention and implicit learning

Abstract: 1. Acknowledgement 2. Contributors 3. Introduction: Attention to implicit learning (by Jimenez, Luis) 4. Part 1. The cognitive debate 5. Attention and awareness in "implicit" sequence learning (by Shanks, David R.) 6. Intention, attention, and consciousness in probabilistic sequence learning (by Jimenez, Luis) 7. Part 2. Neuroscientific and computational approaches 8. Neural structures that support implicit sequence learning (by Hazeltine, Eliot) 9. The cognitive neuroscience of implicit category learning (by Ashby, F. Gregory) 10. Structure and function in sequence learning: Evidence from experimental, neuropsychological and simulation studies (by Dominey, Peter F.) 11. Temporal effects in sequence learning (by Destrebecqz, Arnaud) 12. Implicit and explicit learning in a unified architecture of cognition (by Wallach, Dieter) 13. Part 3. Reciprocal influences: Implicit learning, attention, and beyond 14. Visual orienting, learning and conscious awareness (by Lambert, Tony) 15. Contextual cueing: Reciprocal influences between attention and implicit learning (by Jiang, Yuhong) 16. Attention and implicit memory (by Mulligan, Neil W.) 17. The route from implicit learning to verbal expression of what has been learned: Verbal report of incidentally experienced environmental regularity (by Frensch, Peter A.) 18. Author index 19. Subject index

Phonological memory and rule learning

Abstract: The definitive version is available at www3.interscience.wiley.com Copyright Wiley [Full text of this article is not available in the UHRA]

Spatial and Temporal Sequence Learning in Patients with Parkinson's Disease or Cerebellar Lesions

Abstract: The functional role of different subcortical areas in sequence learning is not clear. In the current study, Parkinson's patients, patients with cerebellar damage, and age-matched control participants performed a serial reaction time task in which a spatial sequence and a temporal sequence were presented simultaneously. The responses were based on the spatial sequence, and the temporal sequence was incidental to the task. The two sequences were of the same length, and the phase relationship between them was held constant throughout training. Sequence learning was assessed comparing performance when both sequences were present versus when the dimension of interest was randomized. In addition, sequence integration was assessed by introducing phase-shift blocks. A functional dissociation was found between the two patient groups. Whereas the Parkinson's patients learned the spatial and temporal sequences individually, they did not learn the relationship between the two sequences, suggesting the basal ganglia play a functional role in sequence integration. In contrast, the cerebellar patients did not show any evidence of sequence learning at all, suggesting the cerebellum might play a general role in forming sequential associations.

Pure perceptual-based sequence learning.

Abstract: Learning a sequence of target locations when the sequence is uncorrelated with a sequence of responses and target location is not the response dimension (pure perceptual-based sequence learning) was examined. Using probabilistic sequences of target locations, the author shows that such learning can be implicit, is unaffected by distance between target locations, and is mostly limited to first-order transition probabilities. Moreover, the mechanism underlying learning affords processing of information at anticipated target locations and appears to be attention based. Implications for hypotheses of implicit sequence learning are discussed.

Incremental planning in sequence production.

Abstract: People produce long sequences such as speech and music with incremental planning: mental preparation of a subset of sequence events. The authors model in music performance the sequence events that can be retrieved and prepared during production. Events are encoded in terms of their serial order and timing relative to other events in a planning increment, a contextually determined distribution of event activations. Planning is facilitated by events' metrical similarity and serial/temporal proximity and by developmental changes in short-term memory. The model's predictions of larger planning increments as production rate decreases and as producers' age-experience increases are confirmed in serial-ordering errors produced by adults and children. Incremental planning is considered as a general retrieval constraint in serially ordered behaviors.

Temporal effects in sequence learning

Abstract: Through the use of double task conditions, the sequence learning (SL) paradigm offers unique opportunities to study the relationships between learning and attention. In their original study, Nissen & Bullemer (1987) argued that a secondary tone-counting task prevents SL because it exhausts participants’ attentional resources. Other authors have instead suggested that the detrimental effects of tone-counting are due to scheduling conflicts between performing the main and secondary tasks rather than to attentional load. Frensch & Miner (1994), for instance, suggested that the secondary task impairs sequence learning because it lengthens the response-to-stimulus interval (RSI) and hence makes it less likely for relevant contingencies to be represented together in short-term memory, — a condition for learning. Stadler (1995), on the other hand, argued that the secondary task introduces variability in the RSI and disrupts the organization of the sequence into chunks. Further, according to Willingham, Greenberg & Cannon Thomas (1997) manipulation of the RSI influences performance but not sequence learning per se. The goal of this paper is to further explore and clarify the role of the RSI in the SL paradigm. To do so, we systematically manipulated the RSI, and assessed performance through different objective and subjective measures. In contrast to previous results, we found that increasing the RSI improves explicit SL. We further show how a neural network model based on the Simple Recurrent Network can account for our data, even though the model neither uses decay nor develops chunked, declarative representations of the sequence. These findings suggest that RSI effects in SL are rooted in the temporal dynamics of learning. Temporal effects in sequence learning 3

Relationship between priming and recognition in deterministic and probabilistic sequence learning.

Abstract: Exposure to a repeating sequence of target stimuli in a speeded localization task can support both priming of sequence-consistent responses and recognition of sequence components. In 3 experiments with both deterministic and probabilistic sequences, the authors used a novel procedure in which measures or priming and recognition were taken concurrently and asked whether these measures can be dissociated. In all of these experiments, both measures were above chance at the group level and no evidence of dissociation was found. Item-level analyses of the data in Experiment 3 did reveal dissociations in that (a) recognition judgments were affected by response speed independently of old-new status and (b) items that were not discriminated in recognition nonetheless showed priming. However, the authors show that these data, together with the group-level results, are compatible with a formal model in which priming and recognition are based on a single common memory variable.

The differential effect of PD and normal aging on early explicit sequence learning

Abstract: Background: Motor sequence learning is abnormal in PD. However, it is not known whether this defect is present during the earliest stages of the illness or whether it reflects specific limitations in dividing attention between cognitive and motor requirements. Methods: Fifteen patients with early stage PD and 10 age-matched and 9 younger normal controls moved the right dominant hand on a digitizing tablet to eight targets presented on a screen in synchrony with a tone at 1-second intervals. The tasks were as follows: 1) CCW—a timed-response task where targets appeared in a predictable counterclockwise order; 2) RAN—a reaction time task where targets were random and unpredictable; 3) SEQ—a task with multiple demands emphasizing explicit learning and target anticipation in which subjects learned a sequence while reaching for targets; and 4) VSEQ—subjects learned a visual sequence without moving. Results: CCW and RAN yielded similar results in all groups. In patients with PD, sequence learning was the same in SEQ and VSEQ and was slower compared to both control groups. In older controls, learning was faster in VSEQ than in SEQ, whereas younger controls learned equally fast in both tasks. Conclusions: Despite normal motor execution, the initial phases of sequence learning are impaired in early PD independent of task requirements, possibly reflecting reduced working memory. Learning was slower in older than younger controls only in tasks with multiple demands, presumably due to reduced attentional resources.

From visuo-motor self learning to early imitation-a neural architecture for humanoid learning

Abstract: Behavior imitation ability will be a key technology for future human friendly robots. In order to understand the principles and mechanisms of imitation, we take a synthetic cognitive developmental approach, starting with minimum components and create a system that can learn to imitate others. We developed a visuo-motor neural learning system which consists of orientation selective visual movement representation, distributed arm movement representation, and a high-dimensional temporal sequence learning mechanism. The vision and the movement representations model the findings in primate brain, i.e. macaque area MT(or human area V5) and the primary motor area. The learning mechanism is inspired by the finding that there are excessive connections in neonate brain. As our robot explores the visuo-motor self movement patterns, it learns coherent patterns as high-dimensional trajectory attractors. After the learning, a human comes in front of the robot showing arm movements which are similar to the ones in self learning. Although the robot has never seen or programmed to interpret human arm movement, and the detail of visual stimuli are very different, the robot identifies some of the patterns as similar to those in self learning, and responded by generating the previously learned arm movement. In other words, the robot exhibits early imitation ability based on self exploratory learning.

Isotropic sequence order learning

Abstract: In this article, we present an isotropic unsupervised algorithm for temporal sequence learning. No special reward signal is used such that all inputs are completely isotropic. All input signals are bandpass filtered before converging onto a linear output neuron. All synaptic weights change according to the correlation of bandpass-filtered inputs with the derivative of the output. We investigate the algorithm in an open- and a closed-loop condition, the latter being defined by embedding the learning system into a behavioral feedback loop. In the open-loop condition, we find that the linear structure of the algorithm allows analytically calculating the shape of the weight change, which is strictly heterosynaptic and follows the shape of the weight change curves found in spike-time-dependent plasticity. Furthermore, we show that synaptic weights stabilize automatically when no more temporal differences exist between the inputs without additional normalizing measures. In the second part of this study, the algorithm is is placed in an environment that leads to closed sensor-motor loop. To this end, a robot is programmed with a prewired retraction reflex reaction in response to collisions. Through isotropic sequence order (ISO) learning, the robot achieves collision avoidance by learning the correlation between his early range-finder signals and the later occurring collision signal. Synaptic weights stabilize at the end of learning as theoretically predicted. Finally, we discuss the relation of ISO learning with other drive reinforcement models and with the commonly used temporal difference learning algorithm. This study is followed up by a mathematical analysis of the closed-loop situation in the companion article in this issue, "ISO Learning Approximates a Solution to the Inverse-Controller Problem in an Unsupervised Behavioral Paradigm" (pp. 865-884).

Lessons Learned: Generalizing Learning Across Games

Abstract: This paper synthesizes findings from an ongoing research program on learning in signaling games. The present paper focuses on crossgame learning (the ability of subjects to take what has been learned in one game and generalize it to related games), an issue that has been ignored in most of the learning literature. We begin by laying out the basic experimental design and recapitulating early results characterizing the learning process. We then report results from an initial experiment in which we find a surprising degree of positive cross-game learning, contrary to the predictions of commonly employed learning models and to the findings of cognitive psychologists. We next explore two features of the environment that help to explain when and why this positive transfer occurs. First, we examine the effects of

Enhancement of brain activation during trial-and-error sequence learning in early PD.

Abstract: Background: Although the pathophysiology remains unknown, most nondemented patients with PD have difficulty with frontal tasks, including trial-and-error sequence learning. If given time, they can perform cognitive tasks of moderate difficulty as well as controls. However, it is not known how brain function is altered during this time period to preserve higher cortical function in the face of PD pathology. Method: To evaluate this phenomenon, the authors matched sequence learning between PD and control subjects for the last 30 seconds of a PET scan. Learning during the initial 50 seconds of PET was unconstrained. Results: Learning indices were equivalent between groups during the last 30 seconds of the scan, whereas rates of acquisition, correct movements, and forgetting differed in the first 30 seconds. In normal controls sequence learning was associated with activations in the right prefrontal, premotor, parietal, rostral supplementary motor area, and precuneus regions. To achieve equal performance, the PD group activated greater volume within these same regions, and also their left sided cortical homologs and the lateral cerebellum bilaterally. Conclusions: Mildly affected patients with PD demonstrated only modest impairment of learning during the first 30 seconds of the task and performed equivalently with controls thereafter. However, the mechanism by which they achieved equiperformance involved considerable changes in brain function. The PD group had to activate four times as much neural tissue as the controls, including recruiting brain from homologous cortical regions and bilateral lateral cerebellum.

An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection

Abstract: This paper introduces the computer security domain of anomaly detection and formulates it as a machine learning task on temporal sequence data. In this domain, the goal is to develop a model or profile of the normal working state of a system user and to detect anomalous conditions as long-term deviations from the expected behavior patterns. We introduce two approaches to this problem: one employing instance-based learning (IBL) and the other using hidden Markov models (HMMs). Though not suitable for a comprehensive security solution, both approaches achieve anomaly identification performance sufficient for a low-level “focus of attention” detector in a multitier security system. Further, we evaluate model scaling techniques for the two approaches: two clustering techniques for the IBL approach and variation of the number of hidden states for the HMM approach. We find that over both model classes and a wide range of model scales, there is no significant difference in performance at recognizing the profiled user. We take this invariance as evidence that, in this security domain, limited memory models (e.g., fixed-length instances or low-order Markov models) can learn only part of the user identity information in which we're interested and that substantially different models will be necessary if dramatic improvements in user-based anomaly detection are to be achieved.

Mathematical Learning Difficulties and PASS Cognitive Processes

Abstract: This study examined the relationships between mathematical learning difficulties (MLD) and the planning, attention, simultaneous, successive (PASS) theory of cognitive processing. The Cognitive Assessment System (CAS) was used to measure the PASS processes for a group of 267 Dutch students with MLD who attended either general or special education. The results showed that students with MLD performed lower than their peers on all CAS scales and that the MLD group contained many students with cognitive weaknesses in planning or successive processing. Moreover, students who had specific difficulties with the acquisition of basic math facts, the automatization of such facts, or word-problem solving were found to have distinct PASS cognitive profiles. In order to investigate the relationships between cognitive abilities and improvement in the mastery of basic math facts and problem solving, 165 of the students with MLD were given a special multiplication intervention. It appeared that the effectiveness of this particular intervention did not differ across the groups of students with specific cognitive weaknesses.

Intermanual transfer of procedural learning after extended practice of probabilistic sequences

Abstract: Previous studies using simple, repeating patterns have suggested that the knowledge gained in early sequence learning is not effector-specific in that it transfers to muscle groups other than those used during training. The current experiments extended these findings to transfer after extensive practice with probabilistic sequences using a task on which people fail to gain declarative knowledge of the regularity. Specifically, an alternating serial reaction time (ASRT) task was used in which predictable and unpredictable trials alternated. Participants responded for the first five sessions using their right hand, then switched to the left hand for the sixth session. Stimuli were spatial in the first experiment and nonspatial in the second. Significant near-perfect transfer of pattern knowledge was seen in both experiments, suggesting that muscle-specific information for either the fingers or the eyes cannot explain the observed learning.

Sequence learning by action and observation: Evidence for separate mechanisms

Abstract: In the Serial Reaction Time (SRT) task, participants respond to a set of stimuli the order of which is apparently random, but which consists of repeating sub-sequences. Participants can become sensitive to this regularity, as measured by an indirect test of reaction time, but can remain apparently unaware of the sequence, as measured by direct tests of prediction or recognition. Some researchers have claimed that this learning may take place by observation alone. We suggest that observational learning may be due to explicit acquired knowledge of the sequence, and is not mediated by the same processes which give rise to learning by action. In Expt 1, we show that it is very difficult to acquire explicit sequence knowledge under dual task conditions, even when participants are told that a regular sequence exists. In Expt 2, we use the same conditions to compare actors, who respond to the sequence during learning, and observers, who merely watch the stimuli. Furthermore, we manipulate the salience of the sequence, in order to encourage learning. There is no evidence of observational learning in these conditions, despite the usual effects of learning being demonstrated by actors. In Expt 3, we show that observational learning does occur, but only when observers have no secondary task and even then only reliably for a sequence which has been made salient by chunking subcomponents. We conclude that sequence learning by observation is mediated by explicit processes, and is eliminated under conditions which support learning by action, but make it difficult to acquire explicit knowledge.

Nonintentional task set activation: Evidence from implicit task sequence learning

Abstract: Studies have shown that task sets could be configured endogenously (i.e., on the basis of memory) according to an explicit sequence or exogenously according to a task cue. In two experiments, we examined whether an implicitly learned sequence could facilitate task set configuration without participants’ intention. These experiments led to opposite conclusions regarding this question, but their methodology made it impossible to distinguish between the interpretations. We altered the task-switching paradigm by embedding a hidden task sequence, while randomizing all other aspects, including perceptual (i.e., task cues) and motor elements. We found that a sequence of tasks, proper, was learned implicitly and that the memory of that sequence endogenously facilitated task decision processes without the participants’ explicit knowledge.

Learning Myopia: An Adaptive Recency Effect in Category Learning

Abstract: Recency effects (REs) have been well established in memory and probability learning paradigms but have received little attention in category learning research. Extant categorization models predict REs to be unaffected by learning, whereas a functional interpretation of REs, suggested by results in other domains, predicts that people are able to learn sequential dependencies and incorporate this information into their responses. These contrasting predictions were tested in 2 experiments involving a classification task in which outcome sequences were autocorrelated. Experiment 1 showed that reliance on recent outcomes adapts to the structure of the task, in contrast to models’ predictions. Experiment 2 provided constraints on how sequential information is learned and suggested possible extensions to current models to account for this learning. Recency effects (REs) are a robust phenomenon in cognitive psychology. REs are said to occur whenever more recent experiences are better remembered or are more influential in judgments about present situations. For example, in research on verbal working memory, REs are arguably among the most fundamental established phenomena, most commonly seen as increased performance on the final positions in free- or serial-recall tasks (e.g., Crowder, 1972; Murdock, 1962). Similar results have since been observed in visuo-spatial working memory (Broadbent & Broadbent, 1981), as well as in animals (Thompson & Herman, 1977; Wright, Santiago, Sands, Kendrick, & Cook, 1985). REs in working memory have often been attributed to spontaneous decay of stored information (Baddeley, 1986; Burgess & Hitch, 1999); however, this simple interpretation has been called into question by recent results showing that the rate of information loss can change, adaptively, in response to temporal statistics of the task (R. B. Anderson, Tweney, Rivardo, & Duncan, 1997). This flexibility is more consistent with a functional account of working memory (J. R. Anderson & Schooler, 1991, 2000; Schacter, 1999) and suggests that there is more underlying the phenomenon than simple architectural constraints. Another area in which REs commonly arise is animal conditioning experiments. Common learning phenomena that depend on trial order, such as extinction, counterconditioning, and discrimination-reversal learning, all fall into the category of REs because they are characterized by behavior at the conclusion of learning being based primarily on the most recent (second) phase of training, rather than an average of both phases. However, the existence and magnitude of such trial order effects depend crucially on the relationship of physical and temporal contexts among