Statistical and Chunking Processes in Adults' Visual Sequence Learning (original) (raw)
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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 a...
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Trends in Cognitive Sciences, 2001
Pioneering work in the 1940s and 1950s suggested that the concept of 'chunking' might be important in many processes of perception, learning and cognition in humans and animals. We summarize here the major sources of evidence for chunking mechanisms, and consider how such mechanisms have been implemented in computational models of the learning process. We distinguish two forms of chunking: the first deliberate, under strategic control, and goal-oriented; the second automatic, continuous, and linked to perceptual processes. Recent work with discrimination-network computational models of long-and short-term memory (EPAM/CHREST) has produced a diverse range of applications of perceptual chunking. We focus on recent successes in verbal learning, expert memory, language acquisition and learning multiple representations, to illustrate the implementation and use of chunking mechanisms within contemporary models of human learning.
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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.
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Memory (Hove, England), 2017
We report four experiments premised upon the work of Horton et al. [(2008). Hebb repetition effects in visual memory: The roles of verbal rehearsal and distinctiveness. Quarterly Journal of Experimental Psychology, 61(12), 1769-1777] and Page et al. [(2013). Repetition-spacing and item-overlap effects in the Hebb repetition task. Journal of Memory and Language, 69(4), 506-526], and explore conditions under which the visual Hebb repetition effect is observed. Experiment 1 showed that repetition learning is evident when the items comprising the non-repeated (filler) sequences and the repeated (Hebb) sequences are different (no-overlap). However, learning is abolished when the filler and Hebb sequences comprise the same items (full-overlap). Learning of the repeated sequence persisted when repetition spacing was increased to six trials (Experiment 2), consistent with that shown for verbal stimuli (Page et al., 2013 ). In Experiment 3, it was shown that learning for the repeated sequenc...
New insights into statistical learning and chunk learning in implicit sequence acquisition
Psychonomic Bulletin & Review
Implicit sequence learning is ubiquitous in our daily life. However, it is unclear whether the initial acquisition of sequences results from learning to chunk items (i.e., chunk learning) or learning the underlying statistical regularities (i.e., statistical learning). By grouping responses with or without a distinct chunk or statistical structure into segments and comparing these responses, previous studies have demonstrated both chunk and statistical learning. However, few studies have considered the response sequence as a whole and examined the temporal dependency of the entire sequence, where the temporal dependencies could disclose the internal representations of chunk and statistical learning. Participants performed a serial reaction time (SRT) task under different stimulus interval conditions. We found that sequence learning reflected by reaction time (RT) rather than motor improvements represented by movement time (MT). The temporal dependency of RT and MT revealed that both RT and MT displayed recursive patterns caused by biomechanical effects of response locations and foot transitions. Chunking was noticeable only in the presence of the recurring RT or MT but vanished after the recursive component was removed, implying that chunk formation may result from biomechanical constraints rather than learning itself. In addition, we observed notable first-order autocorrelations in RT. This trial-to-trial association enhanced as learning progressed regardless of stimulus intervals, reflecting the internal cognitive representation of the first-order stimulus contingencies. Our results suggest that initial acquisition of implicit sequences may arise from first-order statistical learning rather than chunk learning.
Visuomotor sequence learning requires participants to learn a two-fold process of learning the correct visuomotor transformation (spatial or arbitrary rules) and learning the correct order of performing the sequence. A motor sequence is performed with a specific timing pattern by grouping a number of elementary movements into chunks. The present research extends previous findings examining whether chunking phenomenon is observable for visuo-motor sequences with arbitrary transformations in addition to spatially defined targets. This research tests for dominant chunking patterns and individual variability.
Statistical learning and memory
Cognition, 2020
Learners often need to identify and remember recurring units in continuous sequences, but the underlying mechanisms are debated. A particularly prominent candidate mechanism relies on distributional statistics such as Transitional Probabilities (TPs). However, it is unclear what the outputs of statistical segmentation mechanisms are, and if learners store these outputs as discrete chunks in memory. We critically review the evidence for the possibility that statistically coherent items are stored in memory and outline difficulties in interpreting past research. We use Slone and Johnson's (2018) experiments as a case study to show that it is difficult to delineate the different mechanisms learners might use to solve a learning problem. Slone and Johnson (2018) reported that 8-month-old infants learned coherent chunks of shapes in visual sequences. Here, we describe an alternate interpretation of their findings based on a multiple-cue integration perspective. First, when multiple cues to statistical structure were available, infants' looking behavior seemed to track with the strength of the strongest onebackward TPs, suggesting that infants process multiple cues simultaneously and select the strongest one. Second, like adults, infants are exquisitely sensitive to chunks, but may require multiple cues to extract them. In Slone and Johnson's (2018) experiments, these cues were provided by immediate chunk repetitions during familiarization. Accordingly, infants showed strongest evidence of chunking following familiarization sequences in which immediate repetitions were more frequent. These interpretations provide a strong argument for infants' processing of multiple cues and the potential importance of multiple cues for chunk recognition in infancy.
Chunking by colors: Assessing discrete learning in a continuous serial reaction-time task
Acta Psychologica, 2011
Chunk learning (the process by which a sequence is learned and retrieved from memory in smaller, decomposed units of information) has been postulated as the main learning mechanism underlying sequence learning . However, the evidence for chunk formation has been elusive in the continuous serial reaction-time task, whereas other continuous, statistical processes of learning account well for the results observed in this task. This article proposes a new index to capture segmentation in learning, based on the variance of responding to different parts of a sequence. We assess the validity of this measure by comparing performance in a control group with that of another group in which color codes were used to induce a uniform segmentation. Results showed that evidence of chunking was obtained when the color codes were consistently coupled to responses, but that chunking was not maintained after the colors were removed.
Psychological research, 2008
When exposed to a regular sequence, people learn to exploit its predictable structure. There have been two major ways of thinking about learning under these conditions: either as the acquisition of general statistical information about the transition probabilities displayed by the sequence or as a process of memorizing and using separate chunks that can later become progressively composed with extended practice. Even though chunk learning has been adopted by some theories of skill acquisition as their main building block, the evidence for chunk formation is scarce in some areas, and is especially so in the continuous serial reaction-time (SRT) task, which has become a major research tool in the study of implicit learning. This article presents a reappraisal, replication and extension of an experiment that stands so far as one of the few alleged demonstrations of chunk learning in the SRT task (Koch and HoVmann, Psychological Res., 63:22-35, 2000). It shows that the eVects which were taken as evidence for chunk learning can indeed be obtained before any systematic training and thus surely reXect a preexistent tendency rather than a learned outcome. Further analyses of the eVects after extended practice conWrm that this tendency remains essentially unchanged over continuous training unlike what could be expected from a chunk-based account of sequence learning.