A detection theory account of visual short-term memory for color

Wilken, Patrick; Ji, Wei

doi:10.1167/4.8.150

Cited by 6 publications

(17 citation statements)

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“…To test this hypothesis, we conducted a VWM experiment using a delayed estimation task based on the continuous report paradigm. Participants successfully remembered and reconstructed scene images from the scene wheels in their WM, and the observed error pattens are consistent with typical error distributions observed from similar experiments with simple stimuli (Bays & Husain, 2008;Wilken & Ma, 2004;Zhang & Luck, 2008). Critically, the radius of the scene wheels was associated with memory precision.…”

supporting

confidence: 80%

“…The fine control of stimulus properties is essential for adapting new experimental paradigms. Continuous report is a recently developed paradigm that makes it possible to more directly observe a representational structure of the visual system (Bays & Husain, 2008;Wilken & Ma, 2004;Zhang & Luck, 2008). In this paradigm, participants select the stimulus they experienced using a highly controlled response scale that contains all possible stimulus options used in the study and is organized such that visually similar items are adjacent in a continuous 5 visual feature space (e.g., a colour space).…”

mentioning

confidence: 99%

“…Since it provides direct reconstructions of visual representation, the continuous report paradigm has broadened our understanding of visual perception and memory. For example, researchers have quantified more detailed aspects of visual representations like memory precision or bias by quantifying how densely errors are distributed near the target or how the central tendency of a response distribution is systematically shifted (Oh et al, 2019;Son et al, 2020;Wilken & Ma, 2004). The VWM literature has leveraged a mixture model approach that teases apart different components of the error distribution that are theorized to reflect fundamental retention mechanism of VWM (Bays et al, 2009;Fougnie et al, 2010;van den Berg et al, 2012;Zhang & Luck, 2008; but see also Schurgin et al, 2020).…”

mentioning

confidence: 99%

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Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space

Son

Walther

Mack

2020

Preprint

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Precisely characterizing mental representations of visual experiences requires careful control of experimental stimuli. Recent work leveraging such stimulus control in continuous report paradigms have led to important insights; however, these findings are constrained to simple visual properties like colour and line orientation. There remains a critical methodological barrier to characterizing perceptual and mnemonic representations of realistic visual experiences. Here, we introduce a novel method to systematically control visual properties of natural scene stimuli. Using generative adversarial networks (GAN), a state-of-art deep learning technique for creating highly realistic synthetic images, we generated scene wheels in which continuously changing visual properties smoothly transition between meaningful realistic scenes. To validate the efficacy of scene wheels, we conducted a memory experiment in which participants reconstructed to-be-remembered scenes from the scene wheels. Reconstruction errors for these scenes resemble error distributions observed in prior studies using simple stimulus properties. Importantly, memory precision varied systematically with scene wheel radius. These findings suggest our novel approach offers a window into the mental representations of naturalistic visual experiences.

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confidence: 80%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space

Son

Walther

Mack

2020

Preprint

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“… 2 It should be noted that the current proposal could be applied to a fixed slot/item-limited model (e.g., Luck and Vogel, 1997; Cowan, 2001; Zhang and Luck, 2008) or resource/information-limited model of working memory (e.g., Alvarez and Cavanagh, 2004; Wilken and Ma, 2004). This debate is intentionally avoided here because it seems to be unresolved in the literature more generally.…”

mentioning

confidence: 99%

Two Systems of Non-Symbolic Numerical Cognition

Hyde¹

2011

Front. Hum. Neurosci.

153

161

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Studies of human adults, infants, and non-human animals demonstrate that non-symbolic numerical cognition is supported by at least two distinct cognitive systems: a “parallel individuation system” that encodes the numerical identity of individual items and an “approximate number system” that encodes the approximate numerical magnitude, or numerosity, of a set. The exact nature and role of these systems, however, have been debated for over a 100-years. Some argue that the non-symbolic representation of small numbers (<4) is carried out solely by the parallel individuation system and the non-symbolic representation of large numbers (>4) is carried out solely by the approximate number system. Others argue that all numbers are represented by the approximate number system. This debate has been fueled largely by some studies showing dissociations between small and large number processing and other studies showing similar processing of small and large numbers. Recent work has addressed this debate by showing that the two systems are present and distinct from early infancy, persist despite the acquisition of a symbolic number system, activate distinct cortical networks, and engage differentially based attentional constraints. Based on the recent discoveries, I provide a hypothesis that may explain the puzzling findings and makes testable predictions as to when each system will be engaged. In particular, when items are presented under conditions that allow selection of individuals, they will be represented as distinct mental items through parallel individuation and not as a numerical magnitude. In contrast, when items are presented outside attentional limits (e.g., too many, too close together, under high attentional load), they will be represented as a single mental numerical magnitude and not as distinct mental items. These predictions provide a basis on which researchers can further investigate the role of each system in the development of uniquely human numerical thought.

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“…Human vSTM can contain four 19 to fourteen 20 items. The number of clusters made by humans needs to be within this range.…”

Section: Methodsmentioning

confidence: 99%

Mimicking human texture classification

2005

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In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was applied with three feature vectors, based on color/gray values, four texture features, and their combination. Second, 18 participants clustered the images using a newly developed card sorting program. The mutual agreement between the participants was 57% and 56% and between the algorithm and the participants it was 47% and 45%, for respectively color and gray-scale texture images. Third, in a benchmark, 30 participants judged the algorithms' clusters with gray-scale textures as more homogeneous then those with colored textures. However, a high interpersonal variability was present for both the color and the gray-scale clusters. So, despite the promising results, it is questionable whether average human texture classification can be mimicked (if it exists at all).

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A detection theory account of visual short-term memory for color

Cited by 6 publications

References 0 publications

Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space

Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space

Two Systems of Non-Symbolic Numerical Cognition

Mimicking human texture classification

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