Local motion pooling is continuous, global motion perception is discrete.

Green, Marshall L.; Pratte, Michael S.

doi:10.1037/xhp0000971

Cited by 4 publications

(5 citation statements)

References 51 publications

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“…In other words, this flat performance suggests that the high amount of visual noise (90%) used in the coarse discrimination task may have functioned as a threshold on identifying the orientation of the Gabor whereby the observer either perceives the orientation signal and can easily categorize whether it is tilted left or right, or the observer fails to perceive the orientation signal and cannot identify the orientation at all regardless of the magnitude of the orientation offset. This explanation is in line with recent work demonstrating that different mechanisms can result in either graded or all-or-none perception even for highly similar visual stimuli (M. L. Green & Pratte, 2022).…”

Section: Dissociation Between Tasks Using Fine-and Coarse-scale Stimulisupporting

confidence: 92%

“…Our goal was to examine the predictions of an off-the-shelf probabilistic population coding model for the fine-and coarse-scale orientation discrimination tasks. Data were simulated from a model based on an encoder-decoder framework (M. L. Green & Pratte, 2022;Jazayeri & Movshon, 2006;Webb et al, 2007) in which evidence for the orientation category of a given stimulus is represented across a bank of orientation selective channels. The orientation sensitivity function of each detector followed a von Mises distribution to ensure that response profiles respected the circular nature of orientation space.…”

Section: Data and Codementioning

confidence: 99%

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Using artificial neural networks to relate external sensory features to internal decisional evidence

Green,

Hu,

Denison

et al. 2023

Preprint

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All theories of perceptual decision-making postulate that external sensory information is transformed into the internal evidence that is used to guide behavior. However, the nature of this external-to-internal transformation is generally unknown. In two experiments, we examined how a particular stimulus feature – orientation – is transformed into internal evidence. Subjects judged whether Gabor patches were tilted clockwise or counterclockwise. The results of Experiment 1 demonstrated that increasing orientation offset in fine-scale increments resulted in a linear increase in sensitivity (d´), suggesting a linear external-to-internal transformation. However, the results of Experiment 2 demonstrated that increasing orientation offset in coarse-scale increments had little effect on sensitivity, suggesting a highly non-linear transformation. These behavioral results imply that a given sensory feature may not have a one-to-one mapping with the internal representation of evidence across different tasks. Further, we evaluated whether an artificial neural network (ANN) trained on orientation categorization can reproduce the observed external-to-internal transformations. The ANN mirrored the empirical results – fine-scale increments in orientation offset were linearly transformed into internal evidence, but coarse-scale increments in orientation offset had little influence on internal evidence. These results begin to reveal how external sensory information is transformed into internal decisional evidence and suggest that ANNs could serve as a hypothesis-generation platform for this critical transformation.

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Section: Dissociation Between Tasks Using Fine-and Coarse-scale Stimulisupporting

confidence: 92%

Section: Data and Codementioning

confidence: 99%

Using artificial neural networks to relate external sensory features to internal decisional evidence

Green,

Hu,

Denison

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Some important applications of circular experimental tasks are in situations where the paradigms are designed in a way that a considerable deal of data is the result of guessing alongside the main cognitive process (Zhang and Luck, 2008;Green and Pratte, 2022). For example in the context of visual working memory, such guess data are claimed to be produced when the stimulus is not in the memory and this could give information about the capacity of the working memory.…”

Section: Experimental Data Containing Guessmentioning

confidence: 99%

“…Although the two-choice tasks are repeatedly validated, easily implemented, simple enough for participants, have tractable cognitive models with interpretable parameters, sometimes they seem to lack naturality for the specified cognitive process in research, or miss the desired information because of being limited to binary (or even trinary) choices. Recent applications of continuous report tasks represent interesting results that would be very hard or maybe even impossible to deduce by the means of two-choice tasks (Zhang and Luck, 2008;Bays et al, 2009;Fougnie and Alvarez, 2011;Bays et al, 2011;Fougnie et al, 2012;van den Berg et al, 2012;Marshall and Bays, 2013;Fan and Turk-Brown, 2013;Kool et al, 2014;Swan and Wyble, 2014;van den Berg et al, 2014;Ma et al, 2014;Gunseli et al, 2015;Green and Pratte, 2022).…”

Section: Introductionmentioning

confidence: 99%

EZ-CDM for modeling continuous decisions by everyone! Fast, simple, robust, and accurate estimation of circular diffusion model parameters

Qarehdaghi¹,

Rad²

2022

Preprint

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In recent years, the probing of cognitive processes underpinning choice in continuous-outcome space paradigms has attracted the attention of modeling researchers to provide a dynamic decision theory of speed and accuracy. One of the most important of these continuous models is the circular diffusion model (CDM, Smith, 2016, Psychological Review) which assumes a noisy accumulation process, described mathematically as a stochastic two-dimensional Wiener process on the interior of a disk. Despite the significant advantages of this powerful model, however, due to its mathematical complexity, very few researchers have used the CDM in their work. Here, we propose a simple and easy-to-use method for estimating the CDM parameters and fitting the model on continuous scale data by simple formulas that can be computed easily and needs neither theoretical knowledge of model fitting nor much programming skills. Despite being simple and easy, we show that the method surprisingly works with precision near the maximum likelihood estimation method. Also, we introduce a robust version of the method that is still simple but is almost immune to the contaminant responses. Finally, the actual use of the method for experimental data is given with an example of using the method, alongside estimation of guess probability in data. It is hoped that this simple approach introduced here can, on the one hand, take a step towards bridging the gap between theory and experimental data falling along a continuum, on the other hand, can encourage cognitive psychology researchers to move their laboratory research toward continuous response paradigms.

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“…For instance, it relates to questions of whether representations are more discrete and symbolic versus distributed and continuous, which have been a core aspect of cognitive science since the 1950s (e.g., Kolata, 1982;Garnelo & Shanahan, 2019;Rosenblatt, 1958;Rumelhart & McClelland, 1988;Marcus, 1998). Both Rouder et al, (2008) and Donkin et al, (2014) reported evidence for the all-or-none theory, and these results are still routinely cited as evidence for discrete-slot theories and metrics of working-memory (e.g., Cowan, 2014;Green & Pratte, 2022;Luck & Vogel, 2013;Ngiam, Foster, Adam, & Awh, 2022). They also play a major role in theories that working-memory is best considered to consist of approximately 'three to four' fixed representations (e.g., Cowan, 2001), which pervades nearly all popular understanding of individual differences in working-memory capacity.…”

mentioning

confidence: 99%

Zooming in on what counts as core and auxiliary: A case study on recognition models of visual working-memory

Robinson¹,

Williams²,

Brady³

2022

Preprint

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In response to the reported replication crisis in psychology, much recent work focused on increasing the rigor of theory assessment in the social sciences. This research highlights that testing theories is challenging because they inherit a new set of auxiliary assumptions as soon as they are linked to a specific methodology. In this article, we integrate and build on this line of work by demonstrating the breadth of these challenges. In particular, we show that tracking auxiliary assumptions is extremely difficult because they are made at different stages of theory testing and at multiple levels of a theory. We focus on these issues in a reanalysis of a seminal study and its replications, both of which use a simple working-memory paradigm and a mainstream computational modeling approach. These studies provide the main evidence for ‘discrete-slot’ recognition models of visual working-memory, and are still used as the basis for how to measure performance in popular visual working-memory tasks. In our reanalysis, we findthat core practical auxiliary assumptions were unchecked and violated; the original model comparison metrics and data were not diagnostic in several experiments. Furthermore, we find that models were not matched on their ‘theory-general’ auxiliary assumptions, indicating that the tested models were restricted, and not matched in theoretical scope. After testing these auxiliaryassumptions and identifying diagnostic testing conditions, we find evidence for the opposite conclusion. That is, continuous resource models outperform discrete-slot models. Together, our work demonstrates why tracking and testing auxiliary assumptions is a fundamental challenge, even in prominent studies led by careful, computationally-minded researchers. Our work also serves as a framework and conceptual guide for scientists and research consumers on how toidentify and test the gamut of auxiliary assumptions in psychological theory assessment.

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Local motion pooling is continuous, global motion perception is discrete.

Cited by 4 publications

References 51 publications

Using artificial neural networks to relate external sensory features to internal decisional evidence

Using artificial neural networks to relate external sensory features to internal decisional evidence

EZ-CDM for modeling continuous decisions by everyone! Fast, simple, robust, and accurate estimation of circular diffusion model parameters

Zooming in on what counts as core and auxiliary: A case study on recognition models of visual working-memory

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