The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Perceptual Learning Directs Auditory Cortical Map Reorganization through Top-Down Influences
Open accessAbstract
The primary sensory cortex is positioned at a confluence of bottom-up dedicated sensory inputs and top-down inputs related to higher-order sensory features, attentional state, and behavioral reinforcement. We tested whether topographic map plasticity in the adult primary auditory cortex and a secondary auditory area, the suprarhinal auditory field, was controlled by the statistics of bottom-up sensory inputs or by top-down task-dependent influences. Rats were trained to attend to independent parameters, either frequency or intensity, within an identical set of auditory stimuli, allowing us to vary task demands while holding the bottom-up sensory inputs constant. We observed a clear double-dissociation in map plasticity in both cortical fields. Rats trained to attend to frequency cues exhibited an expanded representation of the target frequency range within the tonotopic map but no change in sound intensity encoding compared with controls. Rats trained to attend to intensity cues expressed an increased proportion of nonmonotonic intensity response profiles preferentially tuned to the target intensity range but no change in tonotopic map organization relative to controls. The degree of topographic map plasticity within the task-relevant stimulus dimension was correlated with the degree of perceptual learning for rats in both tasks. These data suggest that enduring receptive field plasticity in the adult auditory cortex may be shaped by task-specific top-down inputs that interact with bottom-up sensory inputs and reinforcement-based neuromodulator release. Top-down inputs might confer the selectivity necessary to modify a single feature representation without affecting other spatially organized feature representations embedded within the same neural circuitry.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multiple reward signals in the brain
Schultz, W. (2000) · Nature Reviews Neuroscience · doi:10.1038/35044563 · Cited by 1,303
Recanzone, G., Schreiner, C., & Merzenich, M. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00087.1993 · Cited by 1,266
Self-citationMap Plasticity in Somatosensory Cortex
Feldman, D. E., & Brecht, M. (2005) · Science · doi:10.1126/science.1115807 · Cited by 587
Attentional control of early perceptual learning.
Ahissar, M., & Hochstein, S. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.12.5718 · Cited by 497
Learning to see: experience and attention in primary visual cortex
Crist, R. E., Li, W., & Gilbert, C. D. (2001) · Nature Neuroscience · doi:10.1038/87470 · Cited by 364
Temporal plasticity in the primary auditory cortex induced by operant perceptual learning
Bao, S., Chang, E. F., Woods, J., & Merzenich, M. M. (2004) · Nature Neuroscience · doi:10.1038/nn1293 · Cited by 265
Self-citationIncreases in cortical acetylcholine release during sustained attention performance in rats
Himmelheber, A. M., Sarter, M., & Bruno, J. P. (2000) · Cognitive Brain Research · doi:10.1016/s0926-6410(00)00012-4 · Cited by 261
Ohl, F. W., & Scheich, H. (1997) · Journal of Comparative Physiology A: Sensory, Neural, and Behavioral Physiology · doi:10.1007/s003590050150 · Cited by 85
Cited by
Why would Musical Training Benefit the Neural Encoding of Speech? The OPERA Hypothesis
Patel, A. D. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00142 · Cited by 703
Mindfulness meditation training alters cortical representations of interoceptive attention
Farb, N. A. S., Segal, Z. V., & Anderson, A. K. (2013) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nss066 · Cited by 566
Mismatch negativity (MMN), the deviance-elicited auditory deflection, explained
May, P. J. C., & Tiitinen, H. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2009.00856.x · Cited by 549
Brain plasticity through the life span: Learning to learn and action video games
Bavelier, D., Green, C. S., Pouget, A., & Schrater, P. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-152832 · Cited by 541
Rewards evoke learning of unconsciously processed visual stimuli in adult humans
Seitz, A. R., Kim, D., & Watanabe, T. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.016 · Cited by 337
Okamoto, H., Stracke, H., Stoll, W., & Pantev, C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911268107 · Cited by 311
Reber, P. J. (2013) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.06.019 · Cited by 296
A common framework for perceptual learning
Seitz, A. R., & Dinse, H. R. (2007) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2007.02.004 · Cited by 276
Perceptual learning of Cantonese lexical tones by tone and non-tone language speakers
Francis, A. L., Ciocca, V., Ma, L., & Fenn, K. (2008) · Journal of Phonetics · doi:10.1016/j.wocn.2007.06.005 · Cited by 256
Linking topography to tonotopy in the mouse auditory thalamocortical circuit
Hackett, T. A., Rinaldi Barkat, T., O'Brien, B. M. J., Hensch, T. K., & Polley, D. B. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.5333-10.2011 · Cited by 216
Self-citationParvalbumin-Expressing Inhibitory Interneurons in Auditory Cortex Are Well-Tuned for Frequency
Moore, A. K., & Wehr, M. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.0663-13.2013 · Cited by 225
Associative representational plasticity in the auditory cortex: a synthesis of two disciplines
Weinberger, N. M. (2007) · Learning & Memory · doi:10.1101/lm.421807 · Cited by 223
Blake, D. T., Heiser, M. A., Caywood, M., & Merzenich, M. M. (2006) · Neuron · doi:10.1016/j.neuron.2006.08.009 · Cited by 150
Self-citationCortical Plasticity, Excitatory–Inhibitory Balance, and Sensory Perception
Carcea, I., & Froemke, R. C. (2013) · Progress in Brain Research · doi:10.1016/b978-0-444-63327-9.00003-5 · Cited by 110
Learning Enhances Sensory Processing in Mouse V1 before Improving Behavior
Jurjut, O., Georgieva, P., Busse, L., & Katzner, S. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3485-16.2017 · Cited by 99
Beyeler, M., Rokem, A., Boynton, G. M., & Fine, I. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa795e · Cited by 102
Tracing the Trajectory of Sensory Plasticity across Different Stages of Speech Learning in Adulthood
Reetzke, R., Xie, Z., Llanos, F., & Chandrasekaran, B. (2018) · Current Biology · doi:10.1016/j.cub.2018.03.026 · Cited by 90
Functional characteristics of auditory cortex in the blind
Stevens, A. A., & Weaver, K. E. (2009) · Behavioural Brain Research · doi:10.1016/j.bbr.2008.07.041 · Cited by 77
Banerjee, S. B., & Liu, R. C. (2013) · Frontiers in Neuroendocrinology · doi:10.1016/j.yfrne.2013.07.008 · Cited by 58
Reynolds, S., Lane, S. J., & Richards, L. (2010) · Journal of Neurodevelopmental Disorders · doi:10.1007/s11689-010-9053-4 · Cited by 54
Initial Uncertainty Impacts Statistical Learning in Sound Sequence Processing
Todd, J., Provost, A., Whitson, L., & Mullens, D. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.05.011 · Cited by 10
Metadata
- Authors
- Polley, D. B., Steinberg, E. E., & Merzenich, M. M.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2006-05-03
- Volume
- 26
- Issue
- 18
- Pages
- 4970-4982
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2136431242
- Cited by
- 551
- References
- 8
- FWCI
- 14.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.