Elsevier

Brain and Cognition

Volume 79, Issue 3, August 2012, Pages 221-244
Brain and Cognition

How chunks, long-term working memory and templates offer a cognitive explanation for neuroimaging data on expertise acquisition: A two-stage framework

https://doi.org/10.1016/j.bandc.2012.01.010Get rights and content

Abstract

Our review of research on PET and fMRI neuroimaging of experts and expertise acquisition reveals two apparently discordant patterns in working-memory-related tasks. When experts are involved, studies show activations in brain regions typically activated during long-term memory tasks that are not observed with novices, a result that is compatible with functional brain reorganization. By contrast, when involving novices and training programs, studies show a decrease in brain regions typically activated during working memory tasks, with no functional reorganization. We suggest that the latter result is a consequence of practice periods that do not allow important structures to be completely acquired: knowledge structures (i.e., Ericsson and Kintsch’s retrieval structures; Gobet and Simon’s templates) and in a lesser way, chunks. These structures allow individuals to improve performance on working-memory tasks, by enabling them to use part of long-term memory as working memory, causing a cerebral functional reorganization. Our hypothesis is that the two brain activation patterns observed in the literature are not discordant, but involve the same process of expertise acquisition in two stages: from decreased activation to brain functional reorganization. The dynamic of these two physiological stages depend on the two above-mentioned psychological constructs: chunks and knowledge structures.

Highlights

► We review neuroimaging studies on expertise acquisition in WM-related tasks. ► We observe two patterns: a decrease of activity, a cerebral functional reorganization. ► We propose a two-physiological-stage process framework for expertise acquisition. ► We link the first stage to chunks, the second stage to retrieval structures and templates.

Introduction

One of the fundamental questions about working memory (WM) concerns its limit. Miller (1956) famously proposed that the amount of information that can be kept in mind at one time is about seven chunks or meaningful units of information. More recently, Ericsson and Kintsch (1995), with their long-term working memory theory (LT-WMT), and Gobet and Simon (1996a), with their template theory (TT), have proposed that, in the case of expertise, part of long-term memory (LTM) can be used during WM tasks in order to circumvent the limit imposed by the magical number 7 (Miller, 1956); this would explain, for example, the performance of experts who are able to recall more than 100 digits (e.g., Chase & Ericsson, 1981). Recent developments in brain imaging via functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have provided partial support for this idea. Two patterns of results can be observed during WM-related tasks. When the experiments involve expert individuals, these do indeed tend to show an activation of brain regions typically activated during LTM tasks (LTM areas hereafter). This pattern of results could correspond to a cerebral functional reorganization related to the acquisition of expertise, where functional reorganization is seen as the recruitment of new activation areas and a shift in the cognitive process underlying task performances (Poldrack, 2000), which in this case means that resources previously (before expertise acquisition) allocated to WM are later (after expertise acquisition) allocated to LTM.

By contrast, when the experiments involve novices who undergo extended practice with WM-related tasks, the results tend to show a decrease in activation of brain regions typically activated during WM tasks (WM areas hereafter), with no cerebral functional reorganization. The latter result could be a consequence of practice periods that do not allow important cognitive structures to be completely acquired, mainly knowledge structures1 (i.e., retrieval structures, Ericsson & Kintsch, 1995; templates, Gobet & Simon, 1996a) and in a less important way chunks (Chase & Simon, 1973a).

In this paper, after presenting chunking theory and two more recent theories of expertise (LT-WMT and TT), we will first establish using the Bayes Factor that it is possible to separate episodic long-term memory activation from working memory activation and then review the studies that report the two different brain activation patterns mentioned above. We will then propose an explanation that makes these results more coherent by suggesting that the two brain patterns are two stages of the same process occurring during expertise acquisition. We will conclude by discussing the psychological constructs – chunks and knowledge structures – that underpin expertise in this two-stage view, linking them with physiological processes.

Section snippets

Chunking theory

One important element for understanding WM limits is chunking. This mechanism is important not only in understanding standard cognitive performance, but also in explaining the differences between novices and experts. The chunking mechanism was initially described by de Groot (1946/1978) and Miller (1956), and then theorized by Chase and Simon (1973a). A current definition is given by Gobet et al. (2001, p. 236): a chunk refers to “…a collection of elements having strong associations with one

Long-term working memory and template theory: a same core idea

LT-WMT and TT revolve around the same fundamental core idea: with expertise, part of LTM can be used as WM, thus expanding an individual’s memory storage and processing capacities. In both theories, this is possible only when knowledge structures have been built. These structures have been called “retrieval structures” in Ericsson and Kintsch’s (1995) theory and “templates” in Gobet and Simon’s theory (1996a). We briefly introduce the principal features of these two structures.

Separating episodic long-term memory activation from working memory activation

Our main assumption is that it is possible to separate brain activations that are from episodic LTM and brain activations that are from WM. In the literature, there seems to be evidence supporting the idea that WM and episodic LTM are different mental functions underpinned by specific and different neural pathways (for reviews, see for example Baddeley, 2003, Gazzaniga et al., 2009, Kandel et al., 2000, Smith and Kosslyn, 2007, Squire and Wixted, 2011). However, the main difficulty when

Neuroimaging of experts

Chronologically, the first study using experts that exhibited results consistent with a cerebral functional reorganization implicating LTM areas was the one by Pesenti et al. (2001). The authors contrasted an expert prodigy (R. Gamm, 6 year of practice) with a group of non-experts, asking all the participants to carry out simple multiplications (the scan base-line condition) and complex ones. While simple multiplications were supposed to be just retrieved, complex ones necessitated retrieval and

Neuroimaging of trained novices

The results from the fMRI and PET studies that we have reviewed above in different tasks and in different domains clearly suggest that experts and novices do not utilize the same brain areas in tasks related to WM. We have argued that LTM areas are strongly involved in the case of experts (for a summary of the results, see Table 1) and not in the case of novices. We consider this finding as evidence in favor of a functional cerebral reorganization involving LTM areas related to expertise

A two-stage view of expertise acquisition

Based on the description of the state of the art we have provided above, there seems to be a clear discrepancy between (a) the data collected with novices who are trained to become experts, which mainly show a decrease of cerebral activity in WM areas, and (b) the data collected with experts who are compared to novices, which are compatible with a cerebral functional reorganization from WM areas to WM and LTM areas. We think that an important factor explaining this discrepancy could be the

Explaining decreases in brain activation: from chunk creation to chunk retrieval

Among the reviewed authors who observed a decrease of brain activation, two explain the decrease via the acquisition of knowledge, appealing to the chunking theory (Jansma et al., 2001, Landau et al., 2004). We completely agree and think this theory offers a good explanation of the decrease in activation in WM-related tasks. We have introduced the chunking mechanism at the beginning of the article. We would like to go further by separating two important chunking mechanisms: (a) chunk creation –

Establishing true brain functional reorganization

The functional reorganization of brain activity is considered to constitute a combined pattern of activation increases and decreases across brain areas (Kelly & Garavan, 2005). At present, two types of functional reorganization have been pinpointed: “scaffolding” (Petersen, van Mier, Fiez, & Raichle, 1998; also labeled “redistribution” and “pseudo-reorganization” by Kelly and Garavan (2005)), and “true reorganization” (Kelly & Garavan, 2005).

According to Kelly and Garavan (2005) two criteria

General summary

The aim of this article was to try to make the PET and fMRI neuroimaging data on the role of expertise and practice in WM-related tasks more coherent by linking the psychological and physiological bases of expertise acquisition. As we have seen, results from neuroimaging studies that concern expertise in WM-related tasks are of two kinds: (a) studies using novices and a training program mostly show a decrease of cerebral activity in WM areas after a period of training and no cerebral functional

Concluding remarks

To the best of our knowledge, this is the first time that neuroimaging studies using trained novices and neuroimaging studies using experts have been contrasted in order to be compared, thus achieving, we believe, a more in-depth analysis. We think that it is this specific comparison that allowed us to perceive the two physiological stages (decreased brain activation and brain functional reorganization) of expertise acquisition that we have proposed. And it is the proposals (concerning chunks

Acknowledgments

Hubert Tardieu, our dear friend, collaborator and colleague passed away during the submission of this article. We feel privileged to have shared moments of his life. We will never forget his sense of humor, wit and genuine kindness.

We are grateful to Dr. Olivier Coubard, Pr. Pascale Piolino, Dr. Yvonnick Noël and Dr. Richard Ll Smith for comments on earlier versions of this paper and we want to deeply thank Pr. Axel Mecklinger, Dr. Martin Lövdén and one anonymous reviewer for their insightful

References (220)

  • W.G. Chase et al.

    Perception in chess

    Cognitive Psychology

    (1973)
  • W.G. Chase et al.

    The mind’s eye in chess

  • F. Chen et al.

    Neural correlates of serial abacus mental calculation in children: A functional MRI study

    Neuroscience Letters

    (2006)
  • C.L. Chen et al.

    Prospective demonstration of brain plasticity after intensive abacus-based mental calculation training: An fMRI study

    Nuclear Instruments and Methods in Physics Research A

    (2006)
  • L. Cloutman et al.

    Where (in the brain) do semantic errors come from?

    Cortex

    (2009)
  • F. Collette et al.

    Exploration of the neural substrates of executive functioning by functional neuroimaging

    Neuroscience

    (2006)
  • S.F. Cowell et al.

    The functional neuroanatomy of simple calculation and number repetition: A parametric PET activation study

    NeuroImage

    (2000)
  • S.J. Crutch et al.

    Preserved calculation skills in a case of semantic dementia

    Cortex

    (2002)
  • H. Damasio et al.

    Neural systems behind word and concept retrieval

    Cognition

    (2004)
  • M. Daneman et al.

    Individual differences in working memory and reading

    Journal of Verbal Learning and Verbal Behavior

    (1980)
  • S. Dehaene et al.

    Cerebral activations during number multiplication and comparison: A PET study

    Neuropsychologia

    (1996)
  • M. Delazer et al.

    Number processing in posterior cortical atrophy: A neuropsychological case study

    Neuropsychologia

    (2006)
  • K.A. Ericsson et al.

    Uncovering the structure of a memorist’s superior “basic’’ memory capacity

    Cognitive Psychology

    (2004)
  • M. Glanzer et al.

    Short-term storage in the processing of text

    Journal of Verbal Learning and Verbal Behavior

    (1981)
  • M. Glanzer et al.

    Short-term storage in reading

    Journal of Verbal Learning and Verbal Behavior

    (1984)
  • F. Gobet

    Expert memory: A comparison of four theories

    Cognition

    (1998)
  • F. Gobet et al.

    In search of templates

    Cognitive Systems Research

    (2002)
  • F. Gobet et al.

    Chunking mechanisms in human learning

    Trends in Cognitive Sciences

    (2001)
  • F. Gobet et al.

    Templates in chess memory: A mechanism for recalling several boards

    Cognitive Psychology

    (1996)
  • F. Gobet et al.

    Five seconds or sixty? Presentation time in expert memory

    Cognitive Science

    (2000)
  • T. Hanakawa et al.

    Neural correlates underlying mental calculation in abacus experts: A functional magnetic resonance imaging study

    Neuroimage

    (2003)
  • L. Hasher et al.

    Working memory, comprehension, and aging: A review and a new view

  • G.K. Aguirre et al.

    Stimulus inversion and the responses of face and object-sensitive cortical areas

    NeuroReport

    (1999)
  • A.D. Baddeley

    Working memory

    (1986)
  • A.D. Baddeley

    The magic number and the episodic buffer (Commentary on “The magical number 4 in short-term memory: A reconsideration of mental storage capacity” by N. Cowan)

    Behavioural and Brain Sciences

    (2001)
  • A.D. Baddeley

    Working memory: Looking back and looking forward

    Nature Reviews: Neuroscience

    (2003)
  • F.C. Bartlett

    Remembering: A study in experimental and social psychology

    (1932)
  • A. Bechara et al.

    Emotion, decision making and the orbitofrontal cortex

    Cerebral Cortex

    (2000)
  • J.R. Binder et al.

    Conceptual processing during the conscious resting state: A functional MRI study

    Journal of Cognitive Neuroscience

    (1999)
  • J.R. Binder et al.

    Human brain language areas identified by functional magnetic resonance imaging

    Journal of Neuroscience

    (1997)
  • R. Cabeza et al.

    Imaging cognition II: An empirical review of 275 PET and fMRI studies

    Journal of Cognitive Neuroscience

    (2000)
  • G. Campitelli et al.

    Brain localisation of memory chunks in chessplayers

    International Journal of Neuroscience

    (2007)
  • M. Cappelletti et al.

    Dissociations in numerical abilities revealed by progressive cognitive decline in a patient with semantic dementia

    Cognitive Neuropsychology

    (2005)
  • A. Caramazza et al.

    The organization of conceptual knowledge in the brain: The future’s past and some future directions

    Cognitive Neuropsychology

    (2006)
  • C.B. Cave et al.

    Intact verbal and nonverbal short-term memory following damage to the human hippocampus

    Hippocampus

    (1992)
  • N. Charness

    Memory for chess positions: Resistance to interference

    Journal of Experimental Psychology: Human Learning and Memory

    (1976)
  • W.G. Chase et al.

    Skilled memory

  • M.W. Chee et al.

    Auditory and visual word processing studied with fMRI

    Human Brain Mapping

    (1999)
  • Z. Chen et al.

    Chunk limits and length limits in immediate recall: A reconciliation

    Journal of Experimental Psychology: Learning, Memory, and Cognition

    (2005)
  • F. Chochon et al.

    Differential contributions of the left and right inferior parietal lobules to number processing

    Journal of Cognitive Neuroscience

    (1999)
  • Cited by (0)

    View full text