Organizing conceptual knowledge in humans with a gridlike code
Open accessAbstract
Coding abstract concepts in the brain: Grid cells are thought to provide the neuronal code that underlies spatial knowledge in the brain. Grid cells have mostly been studied in the context of path integration. However, recent theoretical studies have suggested that they may have a broader role in the organization of general knowledge. Constantinescu et al. investigated whether the neural representation of concepts follows a structure similar to the representation of space in the entorhinal cortex. Several brain regions, including the entorhinal cortex and the ventromedial prefrontal cortex, showed gridlike neural representation of conceptual space. Science , this issue p. 1464
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The location of the DMN as equidistant from all sensory/motor systems is aligned with its broad range of functions that require integration between multiple sensory systems, including episodic and semantic memory, social cognition, goal-directed working memory, and reward-guided decision making.
“Second, the location of the DMN as equidistant from all sensory/motor systems is aligned with its broad range of functions that require integration between multiple sensory systems, including episodic [51] and semantic memory [52-54], social cognition [55, 56], goal-directed working memory tasks [26, 32, 33, 35] and reward-guided decision making [57, 58].”
From Situating the default-mode network along a principal gradient of macroscale cortical organization (2016) · cites this paper as [54]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe general modeling framework is applicable to any circuit that supports navigation through physical space and even navigation of abstract cognitive spaces.
“We explored these topics in a general modeling framework applicable to any circuit that supports navigation through physical space and even navigation of abstract cognitive spaces”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Constantinescu et al., 2016
The paper’s Abstract reports grid-like signals during navigation of two-dimensional conceptual knowledge and relates them to signals observed during spatial navigation. It does not present a general modeling framework or establish applicability to any navigation-supporting circuit. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThere is preliminary evidence from fMRI that abstract concepts are represented according to grid-cell-like hexagonal grid structures in humans.
“Interestingly, there is preliminary evidence from fMRI that abstract concepts are also represented according to grid-cell-like hexagonal grid structures in humans (Constantinescu et al., 2016)”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Constantinescu et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works
Self-citationJenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888 · see citing works
Fast robust automated brain extraction
Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496 · see citing works
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700 · see citing works
Cognitive maps in rats and men
Tolman, E. C. (1948) · Psychological Review · doi:10.1037/h0061626 · Cited by 6,641 · see citing works
Microstructure of a spatial map in the entorhinal cortex
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., & Moser, E. I. (2005) · Nature · doi:10.1038/nature03721 · Cited by 4,068 · see citing works
Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp055 · Cited by 4,060 · see citing works
Invariant visual representation by single neurons in the human brain
Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005) · Nature · doi:10.1038/nature03687 · Cited by 1,988 · see citing works
Memory, navigation and theta rhythm in the hippocampal-entorhinal system
Buzsáki, G., & Moser, E. I. (2013) · Nature Neuroscience · doi:10.1038/nn.3304 · Cited by 1,847 · see citing works
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.046 · Cited by 1,770 · see citing works
The future of memory: remembering, imagining, and the brain
Schacter, D. L., Addis, D. R., Hassabis, D., Martin, V. C., Spreng, R. N., & Szpunar, K. K. (2012) · Neuron · doi:10.1016/j.neuron.2012.11.001 · Cited by 1,398 · see citing works
Griffanti, L., Salimi‐Khorshidi, G., Beckmann, C. F., Auerbach, E. J., Douaud, G., Sexton, C. E., Zsoldos, E., Ebmeier, K. P., Filippini, N., Mackay, C. E., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.034 · Cited by 1,234 · see citing works
Deconstructing episodic memory with construction
Hassabis, D., & Maguire, E. A. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.05.001 · Cited by 1,229 · see citing works
Evidence for grid cells in a human memory network
Doeller, C. F., Barry, C., & Burgess, N. (2010) · Nature · doi:10.1038/nature08704 · Cited by 828 · see citing works
The reorganization and reactivation of hippocampal maps predict spatial memory performance
Dupret, D., O'Neill, J., Pleydell-Bouverie, B., & Csicsvari, J. (2010) · Nature Neuroscience · doi:10.1038/nn.2599 · Cited by 769 · see citing works
Understanding Other Minds: Linking Developmental Psychology and Functional Neuroimaging
Saxe, R., Carey, S., & Kanwisher, N. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142044 · Cited by 726 · see citing works
The entorhinal grid map is discretized
Stensola, H., Stensola, T., Solstad, T., Frøland, K., Moser, M., & Moser, E. I. (2012) · Nature · doi:10.1038/nature11649 · Cited by 706 · see citing works
Informatic parcellation of the network involved in the computation of subjective value
Clithero, J. A., & Rangel, A. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nst106 · Cited by 706 · see citing works
Can We Reconcile the Declarative Memory and Spatial Navigation Views on Hippocampal Function?
Eichenbaum, H., & Cohen, N. J. (2014) · Neuron · doi:10.1016/j.neuron.2014.07.032 · Cited by 615 · see citing works
Direct recordings of grid-like neuronal activity in human spatial navigation
Jacobs, J., Weidemann, C. T., Miller, J. F., Solway, A., Burke, J. F., Wei, X.-X., Suthana, N., Sperling, M. R., Sharan, A. D., Fried, I., et al. (2013) · Nature Neuroscience · doi:10.1038/nn.3466 · Cited by 564 · see citing works
Experience-dependent rescaling of entorhinal grids
Barry, C., Hayman, R. M. A., Burgess, N., & Jeffery, K. J. (2007) · Nature Neuroscience · doi:10.1038/nn1905 · Cited by 532 · see citing works
Weiskopf, N., Hutton, C., Josephs, O., & Deichmann, R. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.07.029 · Cited by 488 · see citing works
Reduced grid-cell–like representations in adults at genetic risk for Alzheimer’s disease
Kunz, L., Schröder, T. N., Lee, H. L., Montag, C., Lachmann, B., Sariyska, R., Reuter, M., Stirnberg, R., Stöcker, T., Messing-Floeter, P. C., et al. (2015) · Science · doi:10.1126/science.aac8128 · Cited by 345 · see citing works
During Running in Place, Grid Cells Integrate Elapsed Time and Distance Run
Kraus, B. J., Brandon, M. P., Robinson, R. J., Connerney, M. A., Hasselmo, M. E., & Eichenbaum, H. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.031 · Cited by 293 · see citing works
Online evaluation of novel choices by simultaneous representation of multiple memories
Barron, H. C., Dolan, R. J., & Behrens, T. E. J. (2013) · Nature Neuroscience · doi:10.1038/nn.3515 · Cited by 266 · see citing works
Self-citationTracking the emergence of conceptual knowledge during human decision making
Kumaran, D., Summerfield, J. J., Hassabis, D., & Maguire, E. A. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.030 · Cited by 260 · see citing works
Shearing-induced asymmetry in entorhinal grid cells
Stensola, T., Stensola, H., Moser, M., & Moser, E. (2015) · Nature · doi:10.1038/nature14151 · Cited by 231 · see citing works
Benoit, R. G., Szpunar, K. K., & Schacter, D. L. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419274111 · Cited by 223 · see citing works
Dordek, Y., Soudry, D., Meir, R., & Derdikman, D. (2016) · eLife · doi:10.7554/elife.10094 · Cited by 175 · see citing works
Grid-like processing of imagined navigation
Horner, A. J., Bisby, J. A., Zotow, E., Bush, D., & Burgess, N. (2016) · Current Biology · doi:10.1016/j.cub.2016.01.042 · Cited by 173 · see citing works
Quantifying the Internal Structure of Categories Using a Neural Typicality Measure
Davis, T. H., & Poldrack, R. A. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht014 · Cited by 57 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [4]O’Keefe J Nadel L The Hippocampus as a Cognitive Map Oxford Univ. Press 1978
- [23]Aronov D Nevers R Tank DW CA1 firing fields represent an abstract coordinate during non-spatial navigation Proceedings of the Meeting of Computational and Systems Neuroscience 2016 T-29 41
- [28]Stachenfeld KL Botvinick MM Gershman SJ Design Principles of the Hippocampal Cognitive Map NIPS Proceedings, Advances in Neural Information Processing Systems 2014 2528 2536
Cited by
Sorted by citations
Situating the default-mode network along a principal gradient of macroscale cortical organization
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608282113 · Cited by 2,321 · see citing works
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works
What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior
Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.002 · Cited by 1,055 · see citing works
Self-citationThe default mode network in cognition: a topographical perspective
Smallwood, J., Bernhardt, B. C., Leech, R., Bzdok, D., Jefferies, E., & Margulies, D. S. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00474-4 · Cited by 935 · see citing works
The cognitive map in humans: spatial navigation and beyond
Epstein, R. A., Patai, E. Z., Julian, J. B., & Spiers, H. J. (2017) · Nature Neuroscience · doi:10.1038/nn.4656 · Cited by 921 · see citing works
Viewpoints: how the hippocampus contributes to memory, navigation and cognition
Lisman, J., Buzsáki, G., Eichenbaum, H., Nadel, L., Ranganath, C., & Redish, A. D. (2017) · Nature Neuroscience · doi:10.1038/nn.4661 · Cited by 851 · see citing works
Mapping of a non-spatial dimension by the hippocampal-entorhinal circuit
Aronov, D., Nevers, R., & Tank, D. W. (2017) · Nature · doi:10.1038/nature21692 · Cited by 773 · see citing works
Navigating cognition: Spatial codes for human thinking
Bellmund, J. L. S., Gärdenfors, P., Moser, E. I., & Doeller, C. F. (2018) · Science · doi:10.1126/science.aat6766 · Cited by 643 · see citing works
Spatial representation in the hippocampal formation: a history
Moser, E. I., Moser, M.-B., & McNaughton, B. L. (2017) · Nature Neuroscience · doi:10.1038/nn.4653 · Cited by 560 · see citing works
Human replay spontaneously reorganizes experience
Liu, Y., Dolan, R. J., Kurth-Nelson, Z., & Behrens, T. E. J. (2019) · Cell · doi:10.1016/j.cell.2019.06.012 · Cited by 497 · see citing works
Self-citationThe role of the hippocampus in navigation is memory
Eichenbaum, H. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00005.2017 · Cited by 430 · see citing works
A map of abstract relational knowledge in the human hippocampal-entorhinal cortex
Garvert, M. M., Dolan, R. J., & Behrens, T. E. (2017) · eLife · doi:10.7554/elife.17086 · Cited by 379 · see citing works
Self-citationIf deep learning is the answer, what is the question?
Saxe, A., Nelli, S., & Summerfield, C. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-00395-8 · Cited by 397 · see citing works
The Border Between Seeing and Thinking
Block, N. (2023) · doi:10.1093/oso/9780197622223.001.0001 · Cited by 367 · see citing works
Sequential replay of nonspatial task states in the human hippocampus
Schuck, N. W., & Niv, Y. (2019) · Science · doi:10.1126/science.aaw5181 · Cited by 304 · see citing works
Structuring knowledge with cognitive maps and cognitive graphs
Peer, M., Brunec, I. K., Newcombe, N. S., & Epstein, R. A. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.10.004 · Cited by 283 · see citing works
Deep reinforcement learning and its neuroscientific implications
Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269 · see citing works
Medial and orbital frontal cortex in decision-making and flexible behavior
Klein-Flügge, M. C., Bongioanni, A., & Rushworth, M. F. S. (2022) · Neuron · doi:10.1016/j.neuron.2022.05.022 · Cited by 244 · see citing works
Dynamic updating of hippocampal object representations reflects new conceptual knowledge
Mack, M. L., Love, B. C., & Preston, A. R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1614048113 · Cited by 223 · see citing works
Neuronal vector coding in spatial cognition
Bicanski, A., & Burgess, N. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0336-9 · Cited by 172 · see citing works
Hexadirectional coding of visual space in human entorhinal cortex
Nau, M., Navarro Schröder, T., Bellmund, J. L. S., & Doeller, C. F. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0050-8 · Cited by 182 · see citing works
Learning structures: Predictive representations, replay, and generalization
Momennejad, I. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.02.017 · Cited by 159 · see citing works
Meta-learning in natural and artificial intelligence
Wang, J. X. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.01.002 · Cited by 171 · see citing works
Egocentric and allocentric representations of space in the rodent brain
Wang, C., Chen, X., & Knierim, J. J. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.11.005 · Cited by 141 · see citing works
A new look at domain specificity: Insights from social neuroscience
Spunt, R. P., & Adolphs, R. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.76 · Cited by 149 · see citing works
Dark control: The default mode network as a reinforcement learning agent
Dohmatob, E., Dumas, G., & Bzdok, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25019 · Cited by 121 · see citing works
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works
Structure learning and the posterior parietal cortex
Summerfield, C., Luyckx, F., & Sheahan, H. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2019.101717 · Cited by 118 · see citing works
Laminar organization of encoding and memory reactivation in the parietal cortex
Wilber, A. A., Skelin, I., Wu, W., & McNaughton, B. L. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.033 · Cited by 95 · see citing works
Experience-dependent contextual codes in the hippocampus
Plitt, M. H., & Giocomo, L. M. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00816-6 · Cited by 101 · see citing works
Oxytocin modulates social value representations in the amygdala
Liu, Y., Li, S., Lin, W., Li, W., Yan, X., Wang, X., Pan, X., Rutledge, R. B., & Ma, Y. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0351-1 · Cited by 89 · see citing works
Reagh, Z. M., & Ranganath, C. (2018) · Neuroscience Letters · doi:10.1016/j.neulet.2018.04.050 · Cited by 83 · see citing works
Zheng, L., Gao, Z., McAvan, A. S., Isham, E. A., & Ekstrom, A. D. (2021) · Nature Communications · doi:10.1038/s41467-021-26560-w · Cited by 54 · see citing works
From remembering to reconstruction: The transformative neural representation of episodic memory.
Xue, G. (2022) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2022.102351 · Cited by 49 · see citing works
Mental navigation and the neural mechanisms of insight
Aru, J., Drüke, M., Pikamäe, J., & Larkum, M. E. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2022.11.002 · Cited by 38 · see citing works
Generalisation of structural knowledge in the hippocampal-entorhinal system
Whittington, J. C. R., Muller, T. H., Mark, S., Barry, C., & Behrens, T. E. J. (2018) · arXiv · doi:10.48550/arxiv.1805.09042 · Cited by 36 · see citing works
Self-citationBrain network dynamics during spontaneous strategy shifts and incremental task optimization
Allegra, M., Seyed-Allaei, S., Schuck, N. W., Amati, D., Laio, A., & Reverberi, C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116854 · Cited by 27 · see citing works
Mahmoodi, A., Nili, H., Harbison, C., Hamilton, S., Trudel, N., Bang, D., & Rushworth, M. F. S. (2023) · Neuron · doi:10.1016/j.neuron.2022.12.030 · Cited by 20 · see citing works
Remapping in a recurrent neural network model of navigation and context inference
Low, I. I., Giocomo, L. M., & Williams, A. H. (2023) · eLife · doi:10.7554/elife.86943 · Cited by 15 · see citing works
“We explored these topics in a general modeling framework applicable to any circuit that supports navigation through physical space and even navigation of abstract cognitive spaces (Aronov et al., 2017; Constantinescu et al., 2016; Whittington et al., 2020).”
BackgroundPredictive Representations: Building Blocks of Intelligence
Carvalho, W., Tomov, M. S., de Cothi, W., Barry, C., & Gershman, S. J. (2024) · Neural Computation · doi:10.1162/neco_a_01705 · Cited by 15 · see citing works
Evaluating Spatial Understanding of Large Language Models
Yamada, Y., Bao, Y., Lampinen, A. K., Kasai, J., & Yildirim, I. (2023) · arXiv · doi:10.48550/arxiv.2310.14540 · Cited by 8 · see citing works
Remapping in a recurrent neural network model of navigation and context inference
Low, I. I. C., Giocomo, L. M., & Williams, A. H. (2023) · doi:10.1101/2023.01.25.525596 · Cited by 3 · see citing works
DeKraker, J., Eichert, N., Hong, S.-J., Evans, A. C., & Bernhardt, B. C. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106890 · Cited by 1 · see citing works
Remapping in a recurrent neural network model of navigation and context inference
Low, I. I., Giocomo, L. M., & Williams, A. H. (2023) · eLife · doi:10.7554/elife.86943.3 · Cited by 1 · see citing works
- Authors
- Constantinescu, A. O., O’Reilly, J. X., & Behrens, T. E. J.
- Affiliations
- John Radcliffe Hospital (GB)
- Radboud University Nijmegen (NL)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Constantinescu, Alexandra O.
- O’Reilly, Jill X.
- Behrens, Timothy E. J.
- Venue
- Science
- Published
- 2016-06-17
- Volume
- 352
- Issue
- 6292
- Pages
- 1464-1468
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC5248972
- OpenAlex
- W2434898965
- Cited by
- 937
- References
- 31
- Citations by year
- 2026: 93 · 2025: 112 · 2024: 115 · 2023: 138 · 2022: 92 · 2021: 110 · 2020: 67 · 2019: 85 · 2018: 61 · 2017: 50 · 2016: 13 · 2015: 1
- FWCI
- 43.90
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Wellcome Trust (099715/Z/12/Z)
- Medical Research Council Career Development Award (MR/L019639/1)
- Wellcome Trust Senior Research Fellowship (WT104765MA)
- James S. McDonnell Foundation (JSMF220020372)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- grid cells, conceptual knowledge, cognitive maps, conceptual space, spatial navigation, brain regions, conceptual relations
- MeSH terms
- Adult, Brain, Female, Habits, Humans, Magnetic Resonance Imaging, Male, Neurons, Knowledge, Young Adult, Functional Neuroimaging, Spatial Navigation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review