Publications by authors named "Michael S Pratte"

People vary in their performance on visual working memory tasks, and these individual differences covary with a wide range of higher-level cognitive processes including fluid intelligence. Performance also varies across study displays, purportedly driven by both low- and higher-level processes. Understanding what causes these sources of systematic variability has been crucial for developing theories of working memory.

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Perceiving the motion of an object is thought to involve two stages: Local motion energy is measured at each point in space, and these signals are then pooled across space to build coherent global motion. There are several theories of how local-to-global pooling occurs, but they all predict that global motion perception is a continuous process, such that increasing the strength of motion energy should gradually increase the precision of perceived motion directions. We test this prediction against the alternative that global motion perception is discrete: Motion is either perceived with high precision or fails to be perceived altogether.

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Several experimental paradigms are purported to measure response conflict, including the Stroop, Simon, and Eriksen flanker tasks. Although these tasks are often treated as being similar, delta plot analyses of response time distributions have revealed marked differences across them. Several theories have been proposed to explain these differences, however, assessing their veracity is difficult given the numerous differences across tasks.

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Visual working memory is often characterized as a discrete system, where an item is either stored in memory or it is lost completely. As this theory predicts, increasing memory load primarily affects the probability that an item is in memory. However, the precision of items successfully stored in memory also decreases with memory load.

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In typical visual working memory tasks, participants report the color of a previously studied item at some probed location. Alternatively, in some recent studies, a color is probed and participants must report the item's location. There is a surprising difference between these tasks: in location reports participants almost never guess randomly as they do when reporting color, but often incorrectly report the locations of non-probed items.

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Iconic memory is characterized by its large storage capacity and brief storage duration, whereas visual working memory is characterized by its small storage capacity. The limited information stored in working memory is often modeled as an all-or-none process in which studied information is either successfully stored or lost completely. This view raises a simple question: If almost all viewed information is stored in iconic memory, yet one second later most of it is completely absent from working memory, what happened to it? Here, I characterized how the precision and capacity of iconic memory changed over time and observed a clear dissociation: Iconic memory suffered from a complete loss of visual items, while the precision of items retained in memory was only marginally affected by the passage of time.

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The development of mathematical models to characterize perceptual and cognitive processes dates back almost to the inception of the field of psychology. Since the 1990s, human functional neuroimaging has provided for rapid empirical and theoretical advances across a variety of domains in cognitive neuroscience. In more recent work, formal modeling and neuroimaging approaches are being successfully combined, often producing models with a level of specificity and rigor that would not have been possible by studying behavior alone.

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If we view a visual scene that contains many objects, then momentarily close our eyes, some details persist while others seem to fade. Discrete models of visual working memory (VWM) assume that only a few items can be actively maintained in memory, beyond which pure guessing will emerge. Alternatively, continuous resource models assume that all items in a visual scene can be stored with some precision.

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Multivariate pattern analysis can be used to decode the orientation of a viewed grating from fMRI signals in early visual areas. Although some studies have reported identifying multiple sources of the orientation information that make decoding possible, a recent study argued that orientation decoding is only possible because of a single source: a coarse-scale retinotopically organized preference for radial orientations. Here we aim to resolve these discrepant findings.

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Bayesian theories of neural coding propose that sensory uncertainty is represented by a probability distribution encoded in neural population activity, but direct neural evidence supporting this hypothesis is currently lacking. Using fMRI in combination with a generative model-based analysis, we found that probability distributions reflecting sensory uncertainty could reliably be estimated from human visual cortex and, moreover, that observers appeared to use knowledge of this uncertainty in their perceptual decisions.

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Orientation selectivity is a cornerstone property of vision, commonly believed to emerge in the primary visual cortex. We found that reliable orientation information could be detected even earlier, in the human lateral geniculate nucleus, and that attentional feedback selectively altered these orientation responses. This attentional modulation may allow the visual system to modify incoming feature-specific signals at the earliest possible processing site.

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Recent fMRI decoding studies have demonstrated that early retinotopic visual areas exhibit similar patterns of activity during the perception of a stimulus and during the maintenance of that stimulus in working memory. These findings provide support for the sensory recruitment hypothesis that the mechanisms underlying perception serve as a foundation for visual working memory. However, a recent study by Ester, Serences, and Awh (2009) found that the orientation of a peripheral grating maintained in working memory could be classified from both the contralateral and ipsilateral regions of the primary visual cortex (V1), implying that, unlike perception, feature-specific information was maintained in a nonretinotopic manner.

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Considerable research has focused on how basic visual features are maintained in working memory, but little is currently known about the precision or capacity of visual working memory for complex objects. How precisely can an object be remembered, and to what extent might familiarity or perceptual expertise contribute to working memory performance? To address these questions, we developed a set of computer-generated face stimuli that varied continuously along the dimensions of age and gender, and we probed participants' memories using a method-of-adjustment reporting procedure. This paradigm allowed us to separately estimate the precision and capacity of working memory for individual faces, on the basis of the assumptions of a discrete capacity model, and to assess the impact of face inversion on memory performance.

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The visual system is remarkably proficient at extracting relevant object information from noisy, cluttered environments. Although attention is known to enhance sensory processing, the mechanisms by which attention extracts relevant information from noise are not well understood. According to the perceptual template model, attention may act to amplify responses to all visual input, or it may act as a noise filter, dampening responses to irrelevant visual noise.

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Recognition memory is often modeled as constituting 2 separate processes, recollection and familiarity, rather than as constituting a single process mediated by a generic latent strength. One way of stating evidence for the more complex 2-process model is to show dissociations with select manipulations, in which one manipulation affects recollection more than the second and the second affects familiarity more than the first. One of the best paradigms for assessing such dissociations is the confidence-ratings paradigm, because within it criterial and mnemonic effects may be separately estimated.

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Considerable information about mental states can be decoded from noninvasive measures of human brain activity. Analyses of brain activity patterns can reveal what a person is seeing, perceiving, attending to, or remembering. Moreover, multidimensional models can be used to investigate how the brain encodes complex visual scenes or abstract semantic information.

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Many popular models conceptualize working memory as consisting of three or four discrete slots or bins. This conceptualization, however, has been seemingly refuted by Bays and Husain (2009), who reported perfect performance on a working memory task with a large number of very simple items. We show, however, that this perfect-performance result likely reflects a design flaw rather than mnemonic structure.

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A dominant theme in modeling human perceptual judgments is that sensory neural activity is summed or integrated until a critical bound is reached. Such models predict that, in general, the shape of response time distributions change across conditions, although in practice, this shape change may be subtle. An alternative view is that response time distributions are shape invariant across conditions or groups.

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Stroop and Simon tasks are logically similar and are often used to investigate cognitive control and inhibition processes. We compare the distributional properties of Stroop and Simon effects with delta plots and find different although stable patterns. Stroop effects across a variety of conditions are smallest for fast responses and increase as responses slow.

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Mickes, Wixted, and Wais (2007) proposed a simple test of latent strength variability in recognition memory. They asked participants to rate their confidence using either a 20-point or a 99-point strength scale and plotted distributions of the resulting ratings. They found 25% more variability in ratings for studied than for new items, which they interpreted as providing evidence that latent mnemonic strength distributions are 25% more variable for studied than for new items.

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One of the most influential findings in the study of recognition memory is that receiver operating characteristic (ROC) curves are asymmetric about the negative diagonal. This result has led to the rejection of the equal-variance signal detection model of recognition memory and has provided motivation for more complex models, such as the unequal-variance signal detection and dual-process models. Here, the authors test the possibility that previous demonstrations of ROC asymmetry do not reflect mnemonic process but rather reflect distortions due to averaging data over items.

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Subliminal priming is said to occur when a subliminal prime influences the classification of a subsequent target. Most subliminal-priming claims are based on separate target- and prime-classification tasks. Because primes are intended to be subliminal, the prime-classification task is difficult, and the target-classification task is easy.

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Visual working memory is often modeled as having a fixed number of slots. We test this model by assessing the receiver operating characteristics (ROC) of participants in a visual-working-memory change-detection task. ROC plots yielded straight lines with a slope of 1.

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In many paradigms, the persuasiveness of subliminal priming relies on establishing that stimuli are undetectable. The standard significance test approach is ill-suited as null results may reflect either truly undetectable stimuli or a lack of power to resolve weakly detectable stimuli. We present a novel statistical model as an alternative.

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