Separating decision and encoding noise in signal detection tasks.

Cabrera, Carlos; Lü, Zhong-Lin; Dosher, Barbara Anne

doi:10.1037/a0039348

Cited by 19 publications

(18 citation statements)

References 77 publications

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“…Early noise has typically been considered at very early stages, including photoreceptor noise in the retina (Barlow, 1962), which can be considered as external noise (albeit in a different sense from experimentally added external noise, as it is not under the direct control of the experimenter). Late additive noise is often assumed (either implicitly or explicitly) to be added at the decision stage, long after the nonlinearities of early visual processing (Cabrera, Lu, & Dosher, 2015;Mueller & Weidemann, 2008). The results here point to a perceptuallyrelevant source of noise that is present in the early evoked response, at around 100ms or earlier.…”

Section: Resolving Early Noise and Birdsall's Theoremmentioning

confidence: 76%

Internal noise in contrast discrimination propagates forwards from early visual cortex

2019

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Human contrast discrimination performance is limited by transduction nonlinearities and variability of the neural representation (noise). Whereas the nonlinearities have been wellcharacterised, there is less agreement about the specifics of internal noise. Psychophysical models assume that it impacts late in sensory processing, whereas neuroimaging and intracranial electrophysiology studies suggest that the noise is much earlier. We investigated whether perceptually-relevant internal noise arises in early visual areas or later decision making areas. We recorded EEG and MEG during a two-interval-forced-choice contrast discrimination task and used multivariate pattern analysis to decode target/nontarget and selected/non-selected intervals from evoked responses. We found that perceptual decisions could be decoded from both EEG and MEG signals, even when the stimuli in both intervals were physically identical. Above-chance decision classification started <100ms after stimulus onset, suggesting that neural noise affects sensory signals early in the visual pathway. Classification accuracy increased over time, peaking at >500ms. Applying multivariate analysis to separate anatomically-defined brain regions in MEG source space, we found that occipital regions were informative early on but then information spreads forwards across parietal and frontal regions. This is consistent with neural noise affecting sensory processing at multiple stages of perceptual decision making. We suggest how early sensory noise might be resolved with Birdsall's linearisation, in which a dominant noise source obscures subsequent nonlinearities, to allow the visual system to preserve the wide dynamic range of early areas whilst still benefitting from contrast-invariance at later stages.

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Section: Resolving Early Noise and Birdsall's Theoremmentioning

confidence: 76%

Internal noise in contrast discrimination propagates forwards from early visual cortex

2019

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“…However, as demonstrated by the simulation results presented in Fig 2, different types of suboptimalities have near-identical effects on the response data, due to which we were unable to reliably distinguish between them using model comparison. Future studies may try to solve this model-identifiability problem by using experimental paradigms that provide a richer kind of behavioral data to further constrain the models (e.g., by collecting confidence ratings [55,56]). Moreover, we believe that it may be fruitful to further investigate the Bayesian sampling hypothesis as a possible source of suboptimality in our task.…”

Section: Discussionmentioning

confidence: 99%

Imperfect Bayesian inference in visual perception

Stengård

Berg

2019

PLoS Comput Biol

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Optimal Bayesian models have been highly successful in describing human performance on perceptual decision-making tasks, such as cue combination and visual search. However, recent studies have argued that these models are often overly flexible and therefore lack explanatory power. Moreover, there are indications that neural computation is inherently imprecise, which makes it implausible that humans would perform optimally on any non-trivial task. Here, we reconsider human performance on a visual-search task by using an approach that constrains model flexibility and tests for computational imperfections. Subjects performed a target detection task in which targets and distractors were tilted ellipses with orientations drawn from Gaussian distributions with different means. We varied the amount of overlap between these distributions to create multiple levels of external uncertainty. We also varied the level of sensory noise, by testing subjects under both short and unlimited display times. On average, empirical performance—measured as d ’—fell 18.1% short of optimal performance. We found no evidence that the magnitude of this suboptimality was affected by the level of internal or external uncertainty. The data were well accounted for by a Bayesian model with imperfections in its computations. This “imperfect Bayesian” model convincingly outperformed the “flawless Bayesian” model as well as all ten heuristic models that we tested. These results suggest that perception is founded on Bayesian principles, but with suboptimalities in the implementation of these principles. The view of perception as imperfect Bayesian inference can provide a middle ground between traditional Bayesian and anti-Bayesian views.

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“…We opted not to use this approach, however, because of the amount of data that we would have excluded to accommodate such an analysis. Our full model undoubtedly omitted other potentially consequential variables as well, such as interactions among IPIs, trial-to-trial variation in attention (e.g., Chambers and Pressnitzer, 2014;Parise and Ernst, 2017;Schwiedrzik et al, 2014), and variability in the decision criterion (Cabrera et al, 2015;Mueller and Weidemann, 2008), as discussed above.…”

Section: Investigating the Decision-making Processmentioning

confidence: 99%

Feeling the Beat (and Seeing It, Too): Vibrotactile, Visual, and Bimodal Rate Discrimination

2020

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Abstract Beats are among the basic units of perceptual experience. Produced by regular, intermittent stimulation, beats are most commonly associated with audition, but the experience of a beat can result from stimulation in other modalities as well. We studied the robustness of visual, vibrotactile, and bimodal signals as sources of beat perception. Subjects attempted to discriminate between pulse trains delivered at 3 Hz or at 6 Hz. To investigate signal robustness, we intentionally degraded signals on two-thirds of the trials using temporal-domain noise. On these trials, inter-pulse intervals (IPIs) were stochastic, perturbed independently from the nominal IPI by random samples from zero-mean Gaussian distributions with different variances. These perturbations produced directional changes in the IPIs, which either increased or decreased the likelihood of confusing the two pulse rates. In addition to affording an assay of signal robustness, this paradigm made it possible to gauge how subjects’ judgments were influenced by successive IPIs. Logistic regression revealed a strong primacy effect: subjects’ decisions were disproportionately influenced by a trial’s initial IPIs. Response times and parameter estimates from drift-diffusion modeling showed that information accumulates more rapidly with bimodal stimulation than with either unimodal stimulus alone. Analysis of error rates within each condition suggested consistently optimal decision making, even with increased IPI variability. Finally, beat information delivered by vibrotactile signals proved just as robust as information conveyed by visual signals, confirming vibrotactile stimulation’s potential as a communication channel.

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Separating decision and encoding noise in signal detection tasks.

Cited by 19 publications

References 77 publications

Internal noise in contrast discrimination propagates forwards from early visual cortex

Internal noise in contrast discrimination propagates forwards from early visual cortex

Imperfect Bayesian inference in visual perception

Feeling the Beat (and Seeing It, Too): Vibrotactile, Visual, and Bimodal Rate Discrimination

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