The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual Decision-Making
Open accessAbstract
For nearly 50 years, the dominant account of decision-making holds that noisy information is accumulated until a fixed threshold is crossed. This account has been tested extensively against behavioral and neurophysiological data for decisions about consumer goods, perceptual stimuli, eyewitness testimony, memories, and dozens of other paradigms, with no systematic misfit between model and data. Recently, the standard model has been challenged by alternative accounts that assume that less evidence is required to trigger a decision as time passes. Such “collapsing boundaries” or “urgency signals” have gained popularity in some theoretical accounts of neurophysiology. Nevertheless, evidence in favor of these models is mixed, with support coming from only a narrow range of decision paradigms compared with a long history of support from dozens of paradigms for the standard theory. We conducted the first large-scale analysis of data from humans and nonhuman primates across three distinct paradigms using powerful model-selection methods to compare evidence for fixed versus collapsing bounds. Overall, we identified evidence in favor of the standard model with fixed decision boundaries. We further found that evidence for static or dynamic response boundaries may depend on specific paradigms or procedures, such as the extent of task practice. We conclude that the difficulty of selecting between collapsing and fixed bounds models has received insufficient attention in previous research, calling into question some previous results.
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
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
An introduction to probability theory and its applications
(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,269
Ratcliff, R. (1978) · Psychological Review · doi:10.1037/0033-295x.85.2.59 · Cited by 4,513
Self-citationANew Look at the Statistical Model Identification
AKAlKE, H. (2011) · Cited by 4,177
The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks
Ratcliff, R., & McKoon, G. (2008) · Neural Computation · doi:10.1162/neco.2008.12-06-420 · Cited by 3,059
Self-citationRoitman, J. D., & Shadlen, M. N. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-21-09475.2002 · Cited by 1,589 · cited 9×
“Roitman and Shadlen (2002), Palmer et al. (2005), and Experiment 1 had RDM stimuli with a 0% coherence condition, for which we fixed the drift rate to zero.”
BackgroundMethodResultInfluentialAn Introduction to Probability Theory and Its Applications, Vol. 1
Feller, W. (1968) · Cited by 1,446
Visual fixations and the computation and comparison of value in simple choice
Krajbich, I., Armel, C., & Rangel, A. (2010) · Nature Neuroscience · doi:10.1038/nn.2635 · Cited by 1,408
Neural Control of Voluntary Movement Initiation
Hanes, D. P., & Schall, J. D. (1996) · Science · doi:10.1126/science.274.5286.427 · Cited by 1,145
Cited by
Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions
Forstmann, B. U., Ratcliff, R., & Wagenmakers, E.-J. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033645 · Cited by 620
Self-citationSuboptimality in perceptual decision making
Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342
A flexible framework for simulating and fitting generalized drift-diffusion models
Shinn, M., Lam, N. H., & Murray, J. D. (2020) · eLife · doi:10.7554/elife.56938 · Cited by 133
Striatal activation reflects urgency in perceptual decision making
van Maanen, L., Fontanesi, L., Hawkins, G. E., & Forstmann, B. U. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.045 · Cited by 36
Self-citationProcess and Content in Decisions From Memory
Zhao, W. J., Richie, R., & Bhatia, S. (2022) · Psychological Review · doi:10.1037/rev0000318 · Cited by 28
Metadata
- Authors
- Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2015-02-11
- Volume
- 35
- Issue
- 6
- Pages
- 2476-2484
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2027728102
- Cited by
- 279
- References
- 76
- FWCI
- 15.67
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual Decision-Making. https://app.cochat.ai/papers/10.1523/jneurosci.2410-14.2015
- The paper
- Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015). Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual Decision-Making. The Journal of Neuroscience. https://doi.org/10.1523/jneurosci.2410-14.2015
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.