Internal and external sources of variability in perceptual decision-making.

Ratcliff, Roger; Voskuilen, Chelsea; McKoon, Gail

doi:10.1037/rev0000080

Cited by 55 publications

(84 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First and foremost, we agree that Ratcliff et al (2018) provided evidence that drift rate varies in some manner between trials in an experiment, which stands in contrast to recent neuroscientific proposals that drift rate remains identical across decisions in an experiment (e.g., O'Connell, Shadlen, Wong-Lin, & Kelly, 2018;Ditterich, 2006aDitterich, , 2006bDrugowitsch, Moreno-Bote, Churchland, Shadlen, & Pouget, 2012;Churchland, Kiani, & Shadlen, 2008).…”

supporting

confidence: 76%

“…Here internal noise refers to random within-trial variability in drift rate and external noise refers to random between-trial variability in drift rate, where "random variability" is variability that cannot be modelled deterministically and instead must be modelled as a random variable from a probability distribution. Ratcliff et al (2018) conducted five different double-pass experiments, where the exact same stimulus was presented on two different trials, assessed whether a diffusion model (Ratcliff, 1978) could explain the level of agreement between these identical trials.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Systematic and random sources of variability in perceptual decision-making: Comment on Ratcliff, Voskuilen, and McKoon (2018).

Evans¹,

Tillman²,

Wagenmakers³

2020

Psychological Review

View full text Add to dashboard Cite

A key assumption of models of human cognition is that there is variability in information processing. Evidence accumulation models (EAMs) commonly assume two broad variabilities in information processing: within-trial variability, which is thought to reflect moment-to-moment fluctuations in perceptual processes, and between-trial variability, which is thought to reflect variability in slower-changing processes like attention, or systematic variability between the stimuli on different trials. Recently, Ratcliff, Voskuilen, and McKoon (2018) claimed to "provide direct evidence that external noise is, in fact, required to explain the data from five simple two-choice decision tasks" (p. 33), suggesting that at least some portion of the between-trial variability in information processing is due to "noise". However, we argue that Ratcliff et al. (2018) failed to distinguish between two different potential sources of between-trial variability: random (i.e., "external noise") and systematic (e.g., item effects). Contrary to the claims of Ratcliff et al. (2018), we show that "external noise" is not required to explain their findings, as the same trends of data can be produced when only item effects are present. Furthermore, we contend that the concept of "noise" within cognitive models merely serves as a convenience parameter for sources of variability that we know exist, but are unable to account for. Therefore, we question the usefulness of experiments aimed at testing the general existence of "random" variability, and instead suggest that future research should attempt to replace the random variability terms within cognitive models with actual explanations of the process.

show abstract

supporting

confidence: 76%

mentioning

confidence: 99%

Systematic and random sources of variability in perceptual decision-making: Comment on Ratcliff, Voskuilen, and McKoon (2018).

Evans¹,

Tillman²,

Wagenmakers³

2020

Psychological Review

View full text Add to dashboard Cite

show abstract

“…In contrast, when the stimulus has no impact on the choice, the consistency is 0.5. We used the double-pass method, which presents each stimulus twice 13,38,39 , to explore how consistency in the DWM depended on σ S and σ I (Figure 4). We only used μ =0 stimuli with exactly zero integrated evidence in order to avoid the parsimonious increase of consistency due to larger deviations of the accumulated evidence from the mean (see Methods).…”

Section: Consistency In Models Of Evidence Integrationmentioning

confidence: 99%

Flexible categorization in perceptual decision making

Prat-Ortega

Wimmer

Roxin

et al. 2020

Preprint

View full text Add to dashboard Cite

Perceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization , in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making. 5/23/2020 Copy of Theoretical paper_post_thesis_biorxiv_nolinks -Google Docs https://docs.google.com/document/d/1GzJnoKh80LGPHNONiRWI8FqU4SV5qtRlgl-Cr8hPWBs/edit# 2/47stimulus evidence linearly until one of the bounds is reached 1 . The DDMA and its different variations have been successfully used to fit psychometric and chronometric curves 2 , to capture the speed accuracy trade off 1,3,4 , to account for history dependent choice biases 5 , changes of mind 6 , confidence reports 7 or the Weber's law 8 . Although the absorbing bounds were originally thought of as a mechanism to terminate the integration process, the DDMA has also been applied to fixed duration tasks 9,10 . In motion discrimination tasks, for instance, it can reproduce the subjects' tendency to give more weight to early rather than late stimulus information, which is called a primacy effect 9,11-15 . However, depending on the details of the task and the stimulus, subjects can also give more weight to late rather than to early evidence (i.e. a recency effect) 16,17 or weigh the whole stimulus uniformly 18 . In order to account for these differences, the DDMA needs to be modified by using reflecting instead of absorbing bounds or by removing the bounds altogether 19 . Despite their considerable success in fitting experimental data, the DDMA and its many variants remain purely phenomenological descriptions of sensory integration. This makes it difficult to link the DDMA to the actual neural circuit mechanisms underlying perceptual decision making.These neural circuit mechanisms have been studied with biophysical attractor network models that ca...

show abstract

“…For example, some participants may be more prone to changing their mind than others. Additionally, responses to physically identical stimuli are also often correlated (Ratcliff, Voskuilen, & McKoon, 2018). For example, some stimuli may be more difficult to judge than others, due to random fluctuations in the noise added in each trial.…”

Section: Random Effects Structurementioning

confidence: 99%

Changes of mind and absolute evidence

Turner¹,

Feuerriegel²,

Andrejević³

et al. 2019

Preprint

View full text Add to dashboard Cite

To navigate the world safely, we often need to rapidly ‘change our mind’ about decisions. Current models assume that initial decisions and change-of-mind decisions draw upon common sources of sensory evidence. In two-choice scenarios, this evidence may be ‘relative’ or ‘absolute’. For example, when judging which of two objects is the brightest, the luminance difference and luminance ratio between the two objects are sources of ‘relative’ evidence, which are invariant across additive and multiplicative luminance changes. Conversely, the overall luminance of the two objects combined is a source of ‘absolute’ evidence, which necessarily varies across symmetric luminance manipulations. Previous studies have shown that initial decisions are sensitive to both relative and absolute evidence; however, it is unknown whether change-of-mind decisions are sensitive to absolute evidence. Here, we investigated this question across two experiments. In each experiment participants indicated which of two flickering greyscale squares was brightest. Following an initial decision, the stimuli remained on screen for a brief period and participants could change their response. To investigate the effect of absolute evidence, the overall luminance of the two squares was varied whilst either the luminance difference (Experiment 1) or luminance ratio (Experiment 2) was held constant. In both experiments we found that increases in absolute evidence led to faster, less accurate initial responses and slower changes of mind. Change-of-mind accuracy decreased when the luminance difference was held constant, but remained unchanged when the luminance ratio was fixed. The initial response effects could be explained by the presence of input-dependent noise within the decision process, varying either within or across trials. However, the change-of-mind effects could not be captured by existing models, nor by two modified models which included input-dependent noise sources. This suggests that that the continued integration of sensory evidence following an initial decision operates differently to that described in existing theoretical accounts.

show abstract

Internal and external sources of variability in perceptual decision-making.

Cited by 55 publications

References 67 publications

Systematic and random sources of variability in perceptual decision-making: Comment on Ratcliff, Voskuilen, and McKoon (2018).

Systematic and random sources of variability in perceptual decision-making: Comment on Ratcliff, Voskuilen, and McKoon (2018).

Flexible categorization in perceptual decision making

Changes of mind and absolute evidence

Contact Info

Product

Resources

About