The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning
Open accessAbstract
Theories of selective attention in associative learning posit that the salience of a cue will be high if the cue is the best available predictor of reinforcement (high predictiveness). In contrast, a different class of attentional theory stipulates that the salience of a cue will be high if the cue is an inaccurate predictor of reinforcement (high uncertainty). Evidence in support of these seemingly contradictory propositions has led to: (i) the development of hybrid attentional models that assume the coexistence of separate, predictiveness-driven and uncertainty-driven mechanisms of changes in cue salience; and (ii) a surge of interest in identifying the neural circuits underpinning these mechanisms. Here, we put forward a formal attentional model of learning that reconciles the roles of predictiveness and uncertainty in salience modification. The issues discussed are relevant to psychologists, behavioural neuroscientists and neuroeconomists investigating the roles of predictiveness and uncertainty in behaviour.
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
Pearce, J. M., & Hall, G. (1980) · Psychological Review · doi:10.1037/0033-295x.87.6.532 · Cited by 3,023
A theory of attention: Variations in the associability of stimuli with reinforcement
Mackintosh, N. J. (1975) · Psychological Review · doi:10.1037/h0076778 · Cited by 2,733
Habituation: a model phenomenon for the study of neuronal substrates of behavior
Thompson, R. F., & Spencer, W. A. (1966) · Psychological Review · doi:10.1037/h0022681 · Cited by 2,608
Discrete coding of reward probability and uncertainty by dopamine neurons
Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003) · Science · doi:10.1126/science.1077349 · Cited by 2,085
Neural differentiation of expected reward and risk in human subcortical structures
Preuschoff, K., Bossaerts, P., & Quartz, S. R. (2006) · Neuron · doi:10.1016/j.neuron.2006.06.024 · Cited by 692
Le Pelley, M. E. (2004) · The Quarterly Journal of Experimental Psychology Section B · doi:10.1080/02724990344000141 · Cited by 360
Explicit neural signals reflecting reward uncertainty
Schultz, W., Preuschoff, K., Camerer, C., Hsu, M., Fiorillo, C. D., Tobler, P. N., & Bossaerts, P. (2008) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2008.0152 · Cited by 239
Cited by
What the orbitofrontal cortex does not do
Stalnaker, T. A., Cooch, N. K., & Schoenbaum, G. (2015) · Nature Neuroscience · doi:10.1038/nn.3982 · Cited by 531
Attention and associative learning in humans: An integrative review.
Le Pelley, M. E., Mitchell, C. J., Beesley, T., George, D. N., & Wills, A. J. (2016) · Psychological Bulletin · doi:10.1037/bul0000064 · Cited by 329
Dopamine transients are sufficient and necessary for acquisition of model-based associations
Sharpe, M. J., Chang, C. Y., Liu, M. A., Batchelor, H. M., Mueller, L. E., Jones, J. L., Niv, Y., & Schoenbaum, G. (2017) · Nature Neuroscience · doi:10.1038/nn.4538 · Cited by 300
Blanchard, T. C., Hayden, B. Y., & Bromberg-Martin, E. S. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.050 · Cited by 295
Dopamine neurons respond to errors in the prediction of sensory features of expected rewards
Takahashi, Y. K., Batchelor, H. M., Liu, B., Khanna, A., Morales, M., & Schoenbaum, G. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.025 · Cited by 200
Metadata
- Authors
- Esber, G. R., & Haselgrove, M.
- Venue
- Proceedings of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2011-06-08
- Volume
- 278
- Issue
- 1718
- Pages
- 2553-2561
- ISSN
- 0080-4649 (ISSN-L)
- 0962-8452
- 1471-2954
- OpenAlex
- W2108256674
- Cited by
- 178
- References
- 7
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning. https://app.cochat.ai/papers/10.1098/rspb.2011.0836
- The paper
- Esber, G. R., & Haselgrove, M. (2011). Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning. Proceedings of the Royal Society B: Biological Sciences. https://doi.org/10.1098/rspb.2011.0836
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.
