Publications

Conference proceedings, talks, and abstracts can be found here.

2016

  • Hayes, T. & Petrov, A. (2016a). Mapping and Correcting the Influence of Gaze Position on Pupil Size Measurements. Behavior Research Methods, 48 (2), 510-527.
    Abstract,   Reprint (pdf),   Best Article of the Year Award
  • Hayes, T. & Petrov, A. (2016b). Pupil diameter tracks the exploration-exploitation tradeoff during analogical reasoning and explains individual differences in fluid intelligence. Journal of Cognitive Neuroscience, 28 (2), 308-318.
    Abstract,   Reprint (pdf)

2015

2014

  • O’Reilly, R. C., Petrov, A. A., Cohen, J. C., Lebiere, C. J., Herd, S. A., & Kriete, T. (2014). How Limited Systematicity Emerges: A Computational Cognitive Neuroscience Approach. In P. Calvo & J. Symons (Eds.), The architecture of cognition: Rethinking Fodor and Pylyshyn’s Systematicity Challenge (pp. 191-226). Cambridge, MA: MIT Press.
    Abstract,   Preprint (pdf),   Purchase on Amazon

2013

  • Petrov, A. A. (2013). Associative Memory-Based Reasoning: A Computational Model of Analogy-Making in a Decentralized Multi-Agent Cognitive Architecture. Saarbrücken, Germany: Lambert Academic Publishing. ISBN 978-3-659-26248-7.
    Abstract,   Full text (pdf),   Purchase on Amazon

2012

  • [PDF] Petrov, A., & Van Horn, N. M.. (2012). Motion aftereffect duration is not changed by perceptual learning: evidence against the representation modification hypothesis. Vision research, 61, 4-14.
    [Bibtex]
    @Article{PetrovVanHorn12,
    Abstract = {The representation modification hypothesis of
    perceptual learning attributes the practice-induced
    improvements in sensitivity and/or discriminability
    to changes in the early visual areas. We used motion
    aftereffects (MAE) to probe the representations of
    motion direction. In two experiments, 4 practice
    sessions on a fine direction-discrimination task
    caused large stimulus-specific improvements in d'
    but no significant stimulus-specific changes in
    either static or dynamic MAE duration at posttest
    relative to a pretest. Power analysis indicated that
    the data were approximately 100 times more likely
    given the hypothesis of no MAE change than the
    hypothesis of a 10\% relative change. In light of
    converging evidence in the MAE literature, this
    suggests that little or no change occurred in the
    cortical representations of visual motion up to and
    including area MT. The task specificity of the
    learning effect challenges the representation
    modification hypothesis and supports an alternative
    -- selective reweighting},
    author = {Petrov, Alexander and Van Horn, Nicholas M.},
    title = {Motion aftereffect duration is not changed by
    perceptual learning: Evidence against the
    representation modification hypothesis},
    journal = {Vision Research},
    year = 2012,
    doi = {10.1016/j.visres.2011.08.005},
    volume = 61,
    pages = {4-14}
    }

2011

  • [PDF] Petrov, A. A., Van Horn, N. M., & Todd, J.. (2011). The visual identification of relational categories. Journal of vision, 11(12), 1-11.
    [Bibtex]
    @Article{PetrovVanHornTodd11,
    author = {Petrov, Alexander A. and Van Horn, Nicholas M. and
    Todd, James},
    title = {The visual identification of relational categories},
    journal = {Journal of Vision},
    doi = {10.1016/j.visres.2011.08.005},
    year = 2011,
    volume = 11,
    number = 12,
    pages = {1--11}
    }
  • [PDF] Hayes, T. R., Petrov, A. A., & Sederberg, P.. (2011). A novel method for analyzing ſequential eye movements reveals ſtrategic ınfluence on raven’s advanced progressive matrices. Journal of vision, 11(10), 1-11.
    [Bibtex]
    @Article{HayesPetrovSederberg11,
    Abstract = {Eye-movements are an important data source in vision
    science. However, the vast majority of eye-movement
    studies ignore sequential information in the data
    and utilize only first-order statistics. Here we
    present a novel application of a temporal-difference
    learning algorithm to construct a scanpath successor
    representation (SR; Dayan, 1993) that captures
    statistical regularities in temporally-extended
    eye-movement sequences. We demonstrate the
    effectiveness of the scanpath SR on eye movement
    data from participants solving items from Raven's
    Advanced Progressive Matrices Test. Analysis of the
    SRs revealed individual differences in scanning
    patterns captured by two principal components that
    predicted individual Raven scores much better than
    existing methods. These scanpath SR components were
    highly interpretable and provided new insight into
    the role of strategic processing on the Raven
    test. The success of the scanpath SR in terms of
    prediction and interpretability suggests that this
    method could prove useful in a much broader context},
    Author = {Hayes, Taylor R. and Petrov, Alexander A. and
    Sederberg, Per},
    Journal = {Journal of Vision},
    Pages = {1--11},
    Title = {A Novel Method for Analyzing Sequential Eye
    Movements Reveals Strategic Influence on Raven's
    Advanced Progressive Matrices},
    Volume = 11,
    Year = 2011,
    number = 10
    }
  • [PDF] Petrov, A. A.. (2011). Category rating is based on prototypes and not instances: evidence from feedback-dependent context effects. Journal of experimental psychology: human perception & performance, 37(2), 336-356.
    [Bibtex]
    @Article{PetrovA11a,
    Abstract = {Context effects in category rating on a 7-point
    scale are shown to reverse direction depending on
    feedback. Context (skewed stimulus frequencies) was
    manipulated between and feedback within subjects in
    two experiments. The diverging predictions of
    prototype- and exemplar-based scaling theories were
    tested using two representative models: ANCHOR and
    INST. To gain coverage on one side of the continuum,
    a prototype-based category must lose on the opposite
    side. ANCHOR can exhibit both assimilative and
    compensatory context effects depending on
    feedback. INST always exhibits assimilative
    effects. The human data show a significant
    context-by-feedback interaction. The main context
    effect is assimilative in one data set and
    compensatory in the other. This pattern is
    consistent with ANCHOR but rules out INST, which
    fails to account for the compensatory effect and the
    interaction. This suggests that human category
    rating is based on unitary representations.},
    Author = {Petrov, Alexander A.},
    Doi = {10.1037/a0021436},
    Journal = {Journal of Experimental Psychology: Human Perception
    \& Performance},
    Number = 2,
    Pages = {336--356},
    Title = {Category rating is based on prototypes and not
    instances: Evidence from feedback-dependent context
    effects},
    Volume = 37,
    Year = 2011
    }
  • [PDF] Petrov, A. A., Van Horn, N. M., & Ratcliff, R.. (2011). Dissociable perceptual learning mechanisms revealed by diffusion-model analysis of the patterns of specificity. Psychonomic bulletin & review, 18(3), 490-497.
    [Bibtex]
    @Article{PetrovVanHornRatcliff11a,
    Abstract = {Performance on perceptual tasks improves with
    practice. Most theories address only accuracy data
    and tacitly assume that perceptual learning is a
    monolithic phenomenon. The present study pioneers
    the use of response time distributions in perceptual
    learning research. The 27 observers practiced a
    visual motion-direction discrimination task with
    filtered-noise textures for four sessions with
    feedback. Session 5 tested whether the learning
    effects transferred to the orthogonal direction. The
    diffusion model (Ratcliff, Psychological Review, 85,
    59--108, 1978) achieved good fits to the individual
    response time distributions from each session and
    identified two distinct learning mechanisms with
    markedly different specificities. A
    stimulus-specific increase in the drift-rate
    parameter indicated improved sensory input to the
    decision process, and a stimulus-general decrease in
    nondecision time variability suggested improved
    timing of the decision process onset relative to
    stimulus onset (which was preceded by a beep). A
    traditional d' analysis would miss the latter
    effect, but the diffusion-model analysis identified
    it in the response time data.},
    Author = {Petrov, Alexander A. and Van Horn, Nicholas M. and
    Ratcliff, Roger},
    Doi = {10.3758/s13423-011-0079-8},
    Journal = {Psychonomic Bulletin \& Review},
    Number = 3,
    Pages = {490--497},
    Title = {Dissociable perceptual learning mechanisms revealed
    by diffusion-model analysis of the patterns of
    specificity},
    Volume = 18,
    Year = 2011
    }

2010

  • [PDF] Petrov, A. A., Jilk, D. J., & O’Reilly, R. C.. (2010). The leabra architecture: ſpecialization without modularity. Behavioral and brain ſcience, 33(4), 286-287.
    [Bibtex]
    @Article{PetrovJilkOReilly10,
    Abstract = {The posterior cortex, hippocampus, and prefrontal
    cortex in the Leabra architecture are specialized in
    terms of various neural parameters, and thus are
    predilections for learning and processing, but
    domain-general in terms of cognitive functions such
    as face recognition. Also, these areas are not
    encapsulated and violate Fodorian criteria for
    modularity. Anderson's terminology obscures these
    important points, but we applaud his overall
    message.},
    Author = {Petrov, Alexander A. and Jilk, David J. and
    O'Reilly, Randall C.},
    Doi = {10.1017/S0140525X10001160},
    Journal = {Behavioral and Brain Science},
    Number = 4,
    Pages = {286--287},
    Title = {The Leabra architecture: Specialization without
    modularity},
    Volume = 33,
    Year = 2010
    }
  • [PDF] Petrov, A. A., & Hayes, T. R.. (2010). Asymmetric transfer of perceptual learning of luminance- and contrast-modulated motion. Journal of vision, 10(14:11), 1-22.
    [Bibtex]
    @Article{PetrovHayes10,
    Abstract = {Perceptual learning was used as a tool for studying
    motion perception. The pattern of transfer of
    learning of luminance- (LM) and contrast-modulated
    (CM) motion is diagnostic of how their respective
    processing pathways are integrated. Twenty observers
    practiced fine direction discrimination with either
    additive (LM) or multiplicative (CM) mixtures of a
    dynamic noise carrier and a radially isotropic
    texture modulator. The temporal frequency was 10 Hz,
    speed was 10 deg/s, and duration was 400 ms, with
    feedback. Group 1 pre-tested CM for 2 blocks,
    trained LM for 16 blocks, and post-tested CM for 6
    blocks during 6 sessions on separate days. In Group
    2, the LM and CM roles were reversed. The d'
    improved almost twofold in both groups. There seemed
    to be full transfer from CM to LM but no significant
    transfer from LM to CM. The pattern of post-switch
    improvement was asymmetric as well{\^a}€''no further
    learning during the LM post-test versus rapid
    relearning during the CM post-test. These strong
    asymmetries suggest a dual-pathway architecture with
    Fourier channels sensitive only to LM signals and
    non-Fourier channels sensitive to both LM and CM. We
    hypothesize that the channels tuned for the same
    motion direction but different carriers are
    integrated using a MAX operation},
    Author = {Petrov, Alexander A. and Hayes, Taylor R.},
    Doi = {10.1167/10.14.11},
    Journal = {Journal of Vision},
    Number = {14:11},
    Pages = {1--22},
    Title = {Asymmetric transfer of perceptual learning of
    luminance- and contrast-modulated motion},
    Volume = 10,
    Year = 2010
    }

2009

  • [PDF] Jeter, P. E., Dosher, B. A., Petrov, A. A., & Lu, Z.. (2009). Task precision at transfer determines specificity of perceptual learning. Journal of vision, 9(3), 1-13.
    [Bibtex]
    @Article{JeterDosherPetrovLu09,
    Abstract = {Perceptual learning, the improvement in performance
    with practice, reflects plasticity in the adult
    visual system. We challenge a standard claim that
    specificity of perceptual learning depends on task
    difficulty during training, instead showing that
    specificity, or conversely transfer, is primarily
    controlled by the precision demands (i.e.,
    orientation difference) of the transfer task. Thus,
    for an orientation discrimination task, transfer of
    performance improvement is observed in low-precision
    transfer tasks, while specificity of performance
    improvement is observed in high-precision transfer
    tasks, regardless of the precision of initial
    training. The nature of specificity places important
    constraints on mechanisms of transfer in visual
    learning. These results contribute to understanding
    generalization of practiced improvements that may be
    key to the development of expertise and for
    applications in remediation},
    Author = {Jeter, Pamela E. and Dosher, Barbara Anne and
    Petrov, Alexander A. and Lu, Zhong-Lin},
    Doi = {10.1167/9.3.1},
    Journal = {Journal of Vision},
    Number = 3,
    Pages = {1--13},
    Title = {Task precision at transfer determines specificity of
    perceptual learning},
    Volume = 9,
    Year = 2009
    }
  • [PDF] Petrov, A. A.. (2009). Symmetry-based methodology for decision-rule ıdentification in ſame-different experiments. Psychonomic bulletin & review, 16(6), 1011-1025.
    [Bibtex]
    @Article{PetrovA09a,
    Abstract = {The standard practice to reduce every same-different
    data set to two numbers (hits and false alarms) is
    wasteful because the response pattern to all four
    stimulus pairs carries information about the
    decision rule adopted by the observer. We describe
    eight rules organized in three families:
    differencing, covert classification, and likelihood
    ratio. We prove that each family produces a
    characteristic pattern of (in)equalities among the
    response probabilities. We propose two simple
    qualitative tests. Is the performance on stimulus
    pairs AA and BB statistically indistinguishable? If
    not, differencing and likelihood-ratio strategies
    can be rejected. Is the performance on pairs AB and
    BA indistinguishable? If not, covert classification
    can be rejected. We present algorithms for fitting
    two covert-classification models and illustrate the
    new methodology in a perceptual learning experiment
    on visual motion-direction discrimination. The
    standard assumption of symmetric decision criteria
    was violated},
    Author = {Petrov, Alexander A.},
    Doi = {10.3758/PBR.16.6.1011},
    Journal = {Psychonomic Bulletin \& Review},
    Number = 6,
    Pages = {1011-1025},
    Title = {Symmetry-Based Methodology for Decision-Rule
    Identification in Same-Different Experiments},
    Volume = 16,
    Year = 2009
    }

2008

  • [PDF] Petrov, A. A.. (2008). Relational priming plays a ſupporting but not leading role in adult analogy-making. Behavioral and brain ſciences, 31, 392-393.
    [Bibtex]
    @Article{PetrovA08b,
    Abstract = {Leech et al.'s analysis adds to an emerging
    consensus of the role of priming in
    analogy-making. However, their model cannot scale up
    to adult-level performance because not all relations
    can be cast as functions. One-size-fits-all accounts
    cannot capture the richness of analogy. Proportional
    analogies and transitive inferences can be made by
    nonstructural mechanisms. Therefore, these tasks do
    not generalize to tasks that require structure
    mapping},
    Author = {Petrov, Alexander A.},
    Doi = {10.1017/S0140525X08004627},
    Journal = {Behavioral and Brain Sciences},
    Pages = {392--393},
    Title = {Relational Priming Plays a Supporting But Not
    Leading Role in Adult Analogy-Making},
    Volume = 31,
    Year = 2008
    }
  • [PDF] Petrov, A. A.. (2008). Additive or multiplicative perceptual noise? two equivalent forms of the ANCHOR model. Journal of ſocial & psychological ſciences, 1(2), 123-143.
    [Bibtex]
    @Article{PetrovA08a,
    Abstract = {ANCHOR is an integrated memory-based scaling model
    that accounts for a wide range of phenomena in
    category rating and absolute identification. The
    model uses anchors stored in memory that serve as
    prototypes for each response category. The stimuli
    are represented by magnitudes. Two alternative
    formulations of the magnitude variability are
    considered: additive noise, which leads to
    logarithmic scales, and multiplicative noise, which
    leads to power scales. Both formulations are
    consistent with Weber's and Stevens's laws. Four
    variants of the ANCHOR framework systematically
    explore these alternative formulations. The
    performance of the models is evaluated against
    experimental data. The results show that the form of
    the perceptual equation is not critical for the
    operation of the model. Thus, the power
    vs. logarithmic controversy does not affect ANCHOR's
    central claim that human scaling performance is
    memory-based.},
    Author = {Petrov, Alexander A.},
    Journal = {Journal of Social \& Psychological Sciences},
    Number = 2,
    Pages = {123--143},
    Title = {Additive or multiplicative perceptual noise? Two
    equivalent forms of the {ANCHOR} model},
    Volume = 1,
    Year = 2008
    }

2006

  • [PDF] Petrov, A. A., Dosher, B. A., & Lu, Z.. (2006). Perceptual learning without feedback in non-ſtationary contexts: data and model. Vision research, 46(19), 3177-3197.
    [Bibtex]
    @Article{PetrovDosherLu06,
    Abstract = {The role of feedback in perceptual learning is
    probed in an orientation discrimination experiment
    under destabilizing non-stationary conditions, and
    explored in a neural network model. Experimentally,
    perceptual learning was examined with periodic
    alteration of a strong external noise context. The
    speed of learning, the performance loss at each
    change in external noise context (switch cost), and
    the asymptotic accuracy d' without feedback were
    very similar or identical to those with
    feedback. However, lack of feedback led to higher
    decision bias (error responses matching the external
    noise context). In the model, the stimulus
    representations are constant, whereas the read-out
    connections to a decision unit are learned by a
    Hebbian plasticity rule that may be augmented by
    additional feedback input and criterion control of
    decision bias},
    Author = {Petrov, Alexander A. and Dosher, Barbara Anne and
    Lu, Zhong-Lin},
    Doi = {doi:10.1016/j.visres.2006.03.022},
    Journal = {Vision Research},
    Number = 19,
    Pages = {3177--3197},
    Title = {Perceptual Learning Without Feedback in
    Non-Stationary Contexts: Data and Model},
    Volume = 46,
    Year = 2006
    }

2005

  • [PDF] Petrov, A. A., & Anderson, J. R.. (2005). The dynamics of ſcaling: a memory-based anchor model of category learning and absolute ıdentification. Psychological review, 112(2), 383-416.
    [Bibtex]
    @Article{PetrovAnderson05,
    Abstract = {A memory-based scaling model--ANCHOR--is proposed
    and tested. The perceived magnitude of the target
    stimulus is compared to a set of anchors in
    memory. Anchor selection is probabilistic and
    sensitive to similarity, base-level strength, and
    recency. The winning anchor provides a reference
    point near the target and thereby converts the
    global scaling problem into a local comparison. An
    explicit correction strategy determines the final
    response. Two incremental learning mechanisms update
    the locations and base-level activations of the
    anchors. This gives rise to sequential, context,
    transfer, practice, and other dynamic effects. The
    scale unfolds as an adaptive map. A hierarchy of
    models is tested on a battery of quantitative
    measures from two experiments in absolute
    identification and category rating},
    Author = {Petrov, Alexander A. and Anderson, John R.},
    Doi = {10.1037/0033-295X.112.2.383},
    Journal = {Psychological Review},
    Number = 2,
    Pages = {383--416},
    Title = {The Dynamics of Scaling: A Memory-Based Anchor Model
    of Category Learning and Absolute Identification},
    Volume = 112,
    Year = 2005
    }
  • [PDF] Petrov, A. A., Dosher, B. A., & Lu, Z.. (2005). The dynamics of perceptual learning: an ıncremental reweighting model. Psychological review, 112(4), 715-743.
    [Bibtex]
    @Article{PetrovDosherLu05,
    Abstract = {The mechanisms of perceptual learning are analyzed
    theoretically, probed in an
    orientation-discrimination experiment involving a
    novel non-stationary context manipulation, and
    instantiated in a detailed computational model. Two
    hypotheses are examined: modification of early
    cortical representations versus task-specific
    selective reweighting. Representation modification
    seems neither functionally necessary nor implied by
    the available psychophysical and physiological
    evidence. Computer simulations and mathematical
    analyses demonstrate the functional and empirical
    adequacy of selective reweighting as a perceptual
    learning mechanism. The stimulus images are
    processed by standard orientation- and
    frequency-tuned representational units, divisively
    normalized. Learning occurs only in the "read-out"
    connections to a decision unit; the stimulus
    representations never change. An incremental Hebbian
    rule tracks the task-dependent predictive value of
    each unit, thereby improving the signal-to-noise
    ratio of their weighted combination. Each abrupt
    change in the environmental statistics induces a
    switch cost in the learning curves as the system
    temporarily works with suboptimal weights},
    Author = {Petrov, Alexander A. and Dosher, Barbara Anne and
    Lu, Zhong-Lin},
    Doi = {10.1037/0033-295X.112.4.715},
    Journal = {Psychological Review},
    Number = 4,
    Pages = {715--743},
    Title = {The Dynamics of Perceptual Learning: An Incremental
    Reweighting Model},
    Volume = 112,
    Year = 2005
    }

2001

  • Kokinov, B. N., & Petrov, A. A.. (2001). Integrating memory and reasoning in analogy-making: the AMBR model. In Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.), In The analogical mind: perspectives from cognitive ſcience (, pp. 59-124). Cambridge, MA: MIT Press.
    [Bibtex]
    @InCollection{KokinovPetrov01,
    Abstract = {The authors take an integrative approach that tries
    to bring analogy and memory together. Their chapter
    addresses phenomena emphasized by constructivist
    approaches to memory, such as memory distortions and
    memory illusions, and show how these phenomena
    interact with analogy-making. They provide evidence
    for omissions, blending of episodes, intrusions from
    generic knowledge, and effects of context, priming,
    and order in analogical reminding. The authors
    explain these phenomena in terms of interactions
    among memory, mapping and perception. The chapter
    presents the latest development of their AMBR model,
    which simulates these phenomena by the parallel work
    and interplay of many subprocesses. This model uses
    dynamic emergent representations and computations
    performed by a society of hybrid micro-agents. AMBR
    is built on a general cognitive architecture, which
    makes it possible to integrate analogy with other
    cognitive processes and to provide a basis for
    unified explanations of phenomena such as
    context-sensitivity that cut across virtually all
    cognitive processes},
    Address = {Cambridge, MA},
    Author = {Kokinov, Boicho N. and Petrov, Alexander A.},
    Booktitle = {The Analogical Mind: Perspectives from Cognitive
    Science},
    Editor = {Gentner, Dedre and Holyoak, Keith J. and Kokinov,
    Boicho N.},
    Pages = {59--124},
    Publisher = {MIT Press},
    Title = {Integrating Memory and Reasoning in Analogy-Making:
    The {AMBR} Model},
    Year = 2001
    }

1998

  • Petrov, A. A., & Kokinov, B. N.. (1998). Mapping and access in analogy-making: ındependent or interactive? a simulation experiment with AMBR. In Holyoak, K. J., Gentner, D., & Kokinov, B. N. (Eds.), In Advances in analogy research: ıntegration of theory and data from the cognitive, computational, and neural sciences (, pp. 124-134). Sofia, Bulgaria: {NBU Press}.
    [Bibtex]
    @InCollection{PetrovKokinov98,
    Abstract = {This paper contrasts two views about the
    relationship between the processes of access and
    mapping in analogy-making. According to the modular
    view, analog access and mapping are two separate
    'phases' that run sequentially and relatively
    independently. The interactionist view assumes that
    they are interdependent subprocesses that run in
    parallel. The paper argues in favor of the second
    view and presents a simulation experiment
    demonstrating its advantages. The experiment is
    performed with the computational model AMBR and
    illustrates one particular way in which the
    subprocess of mapping can influence the subprocess
    of access},
    Address = {Sofia, Bulgaria},
    Author = {Petrov, Alexander A. and Kokinov, Boicho N.},
    Booktitle = {Advances in analogy research: Integration of theory
    and data from the cognitive, computational, and
    neural sciences},
    Editor = {Holyoak, Keith J. and Gentner, Dedre and Kokinov,
    Boicho N.},
    Pages = {124--134},
    Publisher = {{NBU Press}},
    Title = {Mapping and access in analogy-making: Independent or
    interactive? A simulation experiment with {AMBR}},
    Year = 1998
    }

1996

  • Kokinov, B. N., Nikolov, V., & Petrov, A. A.. (1996). Dynamics of emergent computation in DUAL. In Ramsay, A. (Ed.), In Artificial intelligence: methodology, systems, applications (, pp. 303-311). Amsterdam, The Netherlands: {IOS Press}.
    [Bibtex]
    @InCollection{KokinovNikolovPetrov96,
    Abstract = {Human cognitive processes exhibit three important
    qualities: flexibility, efficiency, and context
    sensitivity. The paper argues that these properties
    can be achieved in cognitive models by a combination
    of emergent computation and computational
    dynamics. We discuss the possibilities of building
    such models on the basis of DUAL--a distributed
    cognitive architecture. Computation in DUAL emerges
    from the collective behavior of a great number of
    specialized agents running in parallel and
    interacting through a network of relations. The
    agents work at individual speeds reflecting their
    relevance to the context. Moreover, new agents and
    new relations can be created in the course of
    computation. In this way the particular set of
    agents engaged in a cognitive task and the pattern
    of their interaction is formed dynamically. An
    example of such dynamic emergent
    computation--mapping between two symbolic
    structures--is discussed in more detail},
    Address = {Amsterdam, The Netherlands},
    Author = {Kokinov, Boicho N. and Nikolov, V. and Petrov,
    Alexander A.},
    Booktitle = {Artificial intelligence: Methodology, systems,
    applications},
    Editor = {Ramsay, A.},
    Pages = {303--311},
    Publisher = {{IOS Press}},
    Title = {Dynamics of emergent computation in {DUAL}},
    Year = 1996
    }

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