Classification images: A review

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According to signal detection theoretical analyses, visual signals occurring at a cued location are detected more accurately, whereas frequently occurring ones are reported more often but are not better distinguished from noise. However, conventional analyses that estimate sensitivity and bias by comparing true- and false-positive rates offer limited insights into the mechanisms responsible for these effects. Here, we reassessed the prior influences of signal probability and relevance on visual contrast detection using a reverse-correlation technique that quantifies how signal-like fluctuations in noise predict trial-to-trial variability in choice discarded by conventional analyses. This approach allowed us to estimate separately the sensitivity of true and false positives to parametric changes in signal energy. We found that signal probability and relevance both increased energy sensitivity, but in dissociable ways. Cues predicting the relevant location increased primarily the sensitivity of true positives by suppressing internal noise during signal processing, whereas cues predicting greater signal probability increased both the frequency and the sensitivity of false positives by biasing the baseline activity of signal-selective units. We interpret these findings in light of “predictive-coding” models of perception, which propose separable top-down influences of expectation (probability driven) and attention (relevance driven) on bottom-up sensory processing.

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confidence: 99%

Dissociable prior influences of signal probability and relevance on visual contrast sensitivity

Wyart

Nobre

Summerfield

2012

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“…Identifying these windows is far from trivial; it involves determining what information is being processed, from where, and at what point in time during an individual fixation. We have developed a dual-task noise classification approach (24)(25)(26) that allows us to identify what information is used by the observer for what "task" over the brief time scale of a single fixation. Using this method, we show that the uptake of information for foveal analysis and peripheral selection proceeds independently and in parallel.…”

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confidence: 99%

Foveal analysis and peripheral selection during active visual sampling

Ludwig

Davies

Eckstein

2014

Human vision is an active process in which information is sampled during brief periods of stable fixation in between gaze shifts. Foveal analysis serves to identify the currently fixated object and has to be coordinated with a peripheral selection process of the next fixation location. Models of visual search and scene perception typically focus on the latter, without considering foveal processing requirements. We developed a dual-task noise classification technique that enables identification of the information uptake for foveal analysis and peripheral selection within a single fixation. Human observers had to use foveal vision to extract visual feature information (orientation) from different locations for a psychophysical comparison. The selection of to-be-fixated locations was guided by a different feature (luminance contrast). We inserted noise in both visual features and identified the uptake of information by looking at correlations between the noise at different points in time and behavior. Our data show that foveal analysis and peripheral selection proceeded completely in parallel. Peripheral processing stopped some time before the onset of an eye movement, but foveal analysis continued during this period. Variations in the difficulty of foveal processing did not influence the uptake of peripheral information and the efficacy of peripheral selection, suggesting that foveal analysis and peripheral selection operated independently. These results provide important theoretical constraints on how to model target selection in conjunction with foveal object identification: in parallel and independently.A lmost all human visually guided behavior relies on the selective uptake of information, due to sensory and cognitive limitations. On the sensory side, the sampling of visual input by the retinal mosaic of photoreceptors becomes increasingly sparse and irregular away from central vision (1). In addition, fewer cortical neurons are devoted to the analysis of peripheral visual information (cortical magnification) (2, 3). Humans and other animals with so-called foveated visual systems have evolved gazeshifting mechanisms to overcome these limitations. Saccadic eye movements serve to rapidly and efficiently deploy gaze to objects and regions of interest in the visual field. Sampling the environment appropriately with gaze is the starting point of adaptive visual-motor behavior (4, 5).Studies have shown that saccadic eye movements are guided by analysis of information in the visual periphery up to 80-100 ms before saccade execution (6-8). However, active vision typically requires humans not only also to analyze information in the visual periphery to decide where to fixate next (peripheral selection), but also to analyze the information at the current fixation location (foveal analysis). Not much is known about how foveal analysis and peripheral selection are coordinated and interact. In this regard, we need to know (i) whether and to what extent foveal analysis and peripheral selection are constrained by a common bo...

“…Our approach relies on the linear template model that has been shown to account for performance in many simple discrimination tasks (2,3). In this model, the decision variable is the dot product of a template with the stimulus, plus a sample of normally distributed internal noise.…”

Section: Proxy Decision Variablesmentioning

confidence: 99%

“…This gives an imperfect estimate of the true decision variable for at least two reasons: The classification image is an imperfect estimate of the template (3)(4)(5)(6), and the observer has internal noise (1). In SI Text, Properties of the Proxy Decision Space, we show that both these factors imply that the proxy decision variable is equal to the true decision variable plus a normal random variable that represents measurement error.…”

Section: Proxy Decision Spacementioning

confidence: 99%

Classification images reveal decision variables and strategies in forced choice tasks

Pritchett

Murray

2015

Despite decades of research, there is still uncertainty about how people make simple decisions about perceptual stimuli. Most theories assume that perceptual decisions are based on decision variables, which are internal variables that encode task-relevant information. However, decision variables are usually considered to be theoretical constructs that cannot be measured directly, and this often makes it difficult to test theories of perceptual decision making. Here we show how to measure decision variables on individual trials, and we use these measurements to test theories of perceptual decision making more directly than has previously been possible. We measure classification images, which are estimates of templates that observers use to extract information from stimuli. We then calculate the dot product of these classification images with the stimuli to estimate observers' decision variables. Finally, we reconstruct each observer's "decision space," a map that shows the probability of the observer's responses for all values of the decision variables. We use this method to examine decision strategies in two-alternative forced choice (2AFC) tasks, for which there are several competing models. In one experiment, the resulting decision spaces support the difference model, a classic theory of 2AFC decisions. In a second experiment, we find unexpected decision spaces that are not predicted by standard models of 2AFC decisions, and that suggest intrinsic uncertainty or soft thresholding. These experiments give new evidence regarding observers' strategies in 2AFC tasks, and they show how measuring decision variables can answer long-standing questions about perceptual decision making.any current questions about human cognition are related to how people make decisions, including decisions based on perceptual information. For example, how do we decide whether a search target is present in a cluttered display? How do we decide when to respond in a task where both speed and accuracy are important? How do we judge which of two signals is present in a discrimination task?Most theories of perceptual decision making rely on the notion of a decision variable, a quantity that the observer calculates from the stimulus to summarize task-relevant information, e.g., the probability that a faint signal is present in a detection task (1). Some theories of decision making are very simple, e.g., the observer gives one response if the decision variable is greater than a fixed criterion, and another response if the decision variable is less than the criterion. Other theories use more complex decision rules. Testing theories of decision making would be much easier if we had access to observers' decision variables, but these are usually thought of as theoretical constructs that cannot be measured psychophysically. Here we show that in some tasks, it is possible to estimate decision variables on individual trials, and this provides a very direct way of testing theories of perceptual decision making. We use this method to examine the long-sta...