Emergent human-like covert attention in feedforward convolutional neural networks.

Covert attention allows the selection of locations or features of the visual scene without moving the eyes. Cues and contexts predictive of a target's location orient covert attention and improve perceptual performance. The performance benefits are widely attributed to theories of covert attention as a limited resource, zoom, spotlight, or weighting of visual information. However, such concepts are difficult to map to neuronal populations. We show that a feedforward convolutional neural network (CNN) trained on images to optimize target detection accuracy and with no explicit incorporation of an attention mechanism, a limited resource, or feedback connections learns to utilize cues and contexts in the three most prominent covert attention tasks (Posner cueing, set size effects in search, and contextual cueing) and predicts the cue/context influences on human accuracy. The CNN's cueing/context effects generalize across network training schemes, to peripheral and central pre-cues, discrimination tasks, and reaction time measures, and critically do not vary with reductions in network resources (size). The CNN shows comparable cueing/context effects to a model that optimally uses image information to make decisions (Bayesian ideal observer) but generalizes these effects to cue instances unseen during training. Together, the findings suggest that human-like behavioral signatures of covert attention in the three landmark paradigms might be an emergent property of task accuracy optimization in neuronal populations without positing limited attentional resources. The findings might explain recent behavioral results showing cueing and context effects across a variety of simple organisms with no neocortex, from archerfish to fruit flies.

How experts and novices judge other people's knowledgeability from language use.

How accurate are people in judging someone else's knowledge based on their language use, and do more knowledgeable people use different cues to make these judgments? We address this by recruiting a group of participants ("informants") to answer general knowledge questions and describe various images belonging to different categories (e.g., cartoons, basketball). A second group of participants ("evaluators") also answer general knowledge questions and decide who is more knowledgeable within pairs of informants, based on these descriptions. Evaluators perform above chance at identifying the most knowledgeable informants (65% with only one description available). The less knowledgeable evaluators base their decisions on the number of specific statements, regardless of whether the statements are true or false. The more knowledgeable evaluators treat true and false statements differently and penalize the knowledge they attribute to informants who produce specific yet false statements. Our findings demonstrate the power of a few words when assessing others' knowledge and have implications for how misinformation is processed differently between experts and novices.

A preference to look closer to the eyes is associated with a position-invariant face neural code.

When looking at faces, humans invariably move their eyes to a consistent preferred first fixation location on the face. While most people have the preferred fixation location just below the eyes, a minority have it between the nose-tip and mouth. Not much is known about whether these long-term differences in the preferred fixation location are associated with distinct neural representations of faces. To study this, we used a gaze-contingent face adaptation aftereffect paradigm to test in two groups of observers, one with their mean preferred fixation location closer to the eyes (upper lookers) and the other closer to the mouth (lower lookers). In this task, participants were required to maintain their gaze at either their own group's mean preferred fixation location or that of the other group during adaptation and testing. The two possible fixation locations were 3.6° apart on the face. We measured the face adaptation aftereffects when the adaptation and testing happened while participants maintained fixation at either the same or different locations on the face. Both groups showed equally strong adaptation effects when the adaptation and testing happened at the same fixation location. Crucially, only the upper lookers showed a partial transfer of the FAE across the two fixation locations, when adaptation occurred at the eyes. Lower lookers showed no spatial transfer of the FAE irrespective of the adaptation position. Given the classic finding that neural tuning is increasingly position invariant as one moves higher in the visual hierarchy, this result suggests that differences in the preferred fixation location are associated with distinct neural representations of faces.

Widespread frontoparietal fMRI activity is greatly affected by changes in criterion placement, not discriminability, during recognition memory and visual detection tests.

Widespread frontoparietal activity is consistently observed in recognition memory tests that compare studied ("target") versus unstudied ("nontarget") responses. However, there are conflicting accounts that ascribe various aspects of frontoparietal activity to mnemonic evidence versus decisional processes. According to Signal Detection Theory, recognition judgments require individuals to decide whether the memory strength of an item exceeds an evidence threshold-the decision criterion-for reporting previously studied items. Yet, most fMRI studies fail to manipulate both memory strength and decision criteria, making it difficult to appropriately identify frontoparietal activity associated with each process. In the current experiment, we manipulated both discriminability and decision criteria across recognition memory and visual detection tests during fMRI scanning to assess how frontoparietal activity is affected by each manipulation. Our findings revealed that maintaining a conservative versus liberal decision criterion drastically affects frontoparietal activity in target versus nontarget response contrasts for both recognition memory and visual detection tests. However, manipulations of discriminability showed virtually no differences in frontoparietal activity in target versus nontarget response or item contrasts. Comparing across task domains, we observed similar modulations of frontoparietal activity across criterion conditions, though the recognition memory task revealed larger activations in both magnitude and spatial extent in these contrasts. Nonetheless, there appears to be some domain specificity in frontoparietal activity associated with the maintenance of a conservative versus liberal criterion. We propose that widespread frontoparietal activity observed in target versus nontarget contrasts is largely attributable to response bias where increased activity may reflect inhibition of a prepotent response, which differs depending on whether a person maintains a conservative versus liberal decision criterion.

Head and body cues guide eye movements and facilitate target search in real-world videos.

Static gaze cues presented in central vision result in observer shifts of covert attention and eye movements, and benefits in perceptual performance in the detection of simple targets. Less is known about how dynamic gazer behaviors with head and body motion influence search eye movements and performance in perceptual tasks in real-world scenes. Participants searched for a target person (yes/no task, 50% presence), whereas watching videos of one to three gazers looking at a designated person (50% valid gaze cue, looking at the target). To assess the contributions of different body parts, we digitally erase parts of the gazers in the videos to create three different body parts/whole conditions for gazers: floating heads (only head movements), headless bodies (only lower body movements), and the baseline condition with intact head and body. We show that valid dynamic gaze cues guided participants' eye movements (up to 3 fixations) closer to the target, speeded the time to foveate the target, reduced fixations to the gazers, and improved target detection. The effect of gaze cues in guiding eye movements to the search target was the smallest when the gazer's head was removed from the videos. To assess the inherent information about gaze goal location for each body parts/whole condition, we collected perceptual judgments estimating gaze goals by a separate group of observers with unlimited time. Observers' perceptual judgments showed larger estimate errors when the gazer's head was removed. This suggests that the reduced eye movement guidance from lower body cueing is related to observers' difficulty extracting gaze information without the presence of the head. Together, the study extends previous work by evaluating the impact of dynamic gazer behaviors on search with videos of real-world cluttered scenes.

A 2D Synthesized Image Improves the 3D Search for Foveated Visual Systems.

Current medical imaging increasingly relies on 3D volumetric data making it difficult for radiologists to thoroughly search all regions of the volume. In some applications (e.g., Digital Breast Tomosynthesis), the volumetric data is typically paired with a synthesized 2D image (2D-S) generated from the corresponding 3D volume. We investigate how this image pairing affects the search for spatially large and small signals. Observers searched for these signals in 3D volumes, 2D-S images, and while viewing both. We hypothesize that lower spatial acuity in the observers' visual periphery hinders the search for the small signals in the 3D images. However, the inclusion of the 2D-S guides eye movements to suspicious locations, improving the observer's ability to find the signals in 3D. Behavioral results show that the 2D-S, used as an adjunct to the volumetric data, improves the localization and detection of the small (but not large) signal compared to 3D alone. There is a concomitant reduction in search errors as well. To understand this process at a computational level, we implement a Foveated Search Model (FSM) that executes human eye movements and then processes points in the image with varying spatial detail based on their eccentricity from fixations. The FSM predicts human performance for both signals and captures the reduction in search errors when the 2D-S supplements the 3D search. Our experimental and modeling results delineate the utility of 2D-S in 3D search-reduce the detrimental impact of low-resolution peripheral processing by guiding attention to regions of interest, effectively reducing errors.

Discrimination tasks in simulated low-dose CT noise.

BACKGROUND: This study reports the results of a set of discrimination experiments using simulated images that represent the appearance of subtle lesions in low-dose computed tomography (CT) of the lungs. Noise in these images has a characteristic ramp-spectrum before apodization by noise control filters. We consider three specific diagnostic features that determine whether a lesion is considered malignant or benign, two system-resolution levels, and four apodization levels for a total of 24 experimental conditions. PURPOSE: The goal of the investigation is to better understand how well human observers perform subtle discrimination tasks like these, and the mechanisms of that performance. We use a forced-choice psychophysical paradigm to estimate observer efficiency and classification images. These measures quantify how effectively subjects can read the images, and how they use images to perform discrimination tasks across the different imaging conditions. MATERIALS AND METHODS: The simulated CT images used as stimuli in the psychophysical experiments are generated from high-resolution objects passed through a modulation transfer function (MTF) before down-sampling to the image-pixel grid. Acquisition noise is then added with a ramp noise-power spectrum (NPS), with subsequent smoothing through apodization filters. The features considered are lesion size, indistinct lesion boundary, and a nonuniform lesion interior. System resolution is implemented by an MTF with resolution (10% max.) of 0.47 or 0.58 cyc/mm. Apodization is implemented by a Shepp-Logan filter (Sinc profile) with various cutoffs. Six medically naïve subjects participated in the psychophysical studies, entailing training and testing components for each condition. Training consisted of staircase procedures to find the 80% correct threshold for each subject, and testing involved 2000 psychophysical trials at the threshold value for each subject. Human-observer performance is compared to the Ideal Observer to generate estimates of task efficiency. The significance of imaging factors is assessed using ANOVA. Classification images are used to estimate the linear template weights used by subjects to perform these tasks. Classification-image spectra are used to analyze subject weights in the spatial-frequency domain. RESULTS: Overall, average observer efficiency is relatively low in these experiments (10%-40%) relative to detection and localization studies reported previously. We find significant effects for feature type and apodization level on observer efficiency. Somewhat surprisingly, system resolution is not a significant factor. Efficiency effects of the different features appear to be well explained by the profile of the linear templates in the classification images. Increasingly strong apodization is found to both increase the classification-image weights and to increase the mean-frequency of the classification-image spectra. A secondary analysis of "Unapodized" classification images shows that this is largely due to observers undoing (inverting) the effects of apodization filters. CONCLUSIONS: These studies demonstrate that human observers can be relatively inefficient at feature-discrimination tasks in ramp-spectrum noise. Observers appear to be adapting to frequency suppression implemented in apodization filters, but there are residual effects that are not explained by spatial weighting patterns. The studies also suggest that the mechanisms for improving performance through the application of noise-control filters may require further investigation.

Gaze-cued shifts of attention and microsaccades are sustained for whole bodies but are transient for body parts.

Gaze direction is an evolutionarily important mechanism in daily social interactions. It reflects a person's internal cognitive state, spatial locus of interest, and predicts future actions. Studies have used static head images presented foveally and simple synthetic tasks to find that gaze orients attention and facilitates target detection at the cued location in a sustained manner. Little is known about how people's natural gaze behavior, including eyes, head, and body movements, jointly orient covert attention, microsaccades, and facilitate performance in more ecological dynamic scenes. Participants completed a target person detection task with videos of real scenes. The videos showed people looking toward (valid cue) or away from a target (invalid cue) location. We digitally manipulated the individuals in the videos directing gaze to create three conditions: whole-intact (head and body movements), floating heads (only head movements), and headless bodies (only body movements). We assessed their impact on participants' behavioral performance and microsaccades during the task. We show that, in isolation, an individual's head or body orienting toward the target-person direction led to facilitation in detection that is transient in time (200 ms). In contrast, only the whole-intact condition led to sustained facilitation (500 ms). Furthermore, observers executed microsaccades more frequently towards the cued direction for valid trials, but this bias was sustained in time only with the joint presence of head and body parts. Together, the results differ from previous findings with foveally presented static heads. In more real-world scenarios and tasks, sustained attention requires the presence of the whole-intact body of the individuals dynamically directing their gaze.

Peripheral facial features guiding eye movements and reducing fixational variability.

Face processing is a fast and efficient process due to its evolutionary and social importance. A majority of people direct their first eye movement to a featureless point just below the eyes that maximizes accuracy in recognizing a person's identity and gender. Yet, the exact properties or features of the face that guide the first eye movements and reduce fixational variability are unknown. Here, we manipulated the presence of the facial features and the spatial configuration of features to investigate their effect on the location and variability of first and second fixations to peripherally presented faces. Our results showed that observers can utilize the face outline, individual facial features, and feature spatial configuration to guide the first eye movements to their preferred point of fixation. The eyes have a preferential role in guiding the first eye movements and reducing fixation variability. Eliminating the eyes or altering their position had the greatest influence on the location and variability of fixations and resulted in the largest detriment to face identification performance. The other internal features (nose and mouth) also contribute to reducing fixation variability. A subsequent experiment measuring detection of single features showed that the eyes have the highest detectability (relative to other features) in the visual periphery providing a strong sensory signal to guide the oculomotor system. Together, the results suggest a flexible multiple-cue approach that might be a robust solution to cope with how the varying eccentricities in the real world influence the ability to resolve individual feature properties and the preferential role of the eyes.

Medical image quality metrics for foveated model observers.

Purpose: A recently proposed model observer mimics the foveated nature of the human visual system by processing the entire image with varying spatial detail, executing eye movements, and scrolling through slices. The model can predict how human search performance changes with signal type and modality (2D versus 3D), yet its implementation is computationally expensive and time-consuming. Here, we evaluate various image quality metrics using extensions of the classic index of detectability expression and assess foveated model observers for search tasks. Approach: We evaluated foveated extensions of a channelized Hotelling and nonprewhitening matched filter model with an eye filter. The proposed methods involve calculating a model index of detectability ( d ' ) for each retinal eccentricity and combining these with a weighting function into a single detectability metric. We assessed different versions of the weighting function that varied in the required measurements of the human observers' search (no measurements, eye movement patterns, size of the image, and median search times). Results: We show that the index of detectability across eccentricities weighted using the eye movement patterns of observers best predicted human performance in 2D versus 3D search performance for a small microcalcification-like signal and a larger mass-like. The metric with a weighting function based on median search times was the second best predicting human results. Conclusions: The findings provide a set of model observer tools to evaluate image quality in the early stages of imaging system evaluation or design without implementing the more computationally complex foveated search model.

The transverse occipital sulcus and intraparietal sulcus show neural selectivity to object-scene size relationships.

To optimize visual search, humans attend to objects with the expected size of the sought target relative to its surrounding scene (object-scene scale consistency). We investigate how the human brain responds to variations in object-scene scale consistency. We use functional magnetic resonance imaging and a voxel-wise feature encoding model to estimate tuning to different object/scene properties. We find that regions involved in scene processing (transverse occipital sulcus) and spatial attention (intraparietal sulcus) have the strongest responsiveness and selectivity to object-scene scale consistency: reduced activity to mis-scaled objects (either unusually smaller or larger). The findings show how and where the brain incorporates object-scene size relationships in the processing of scenes. The response properties of these brain areas might explain why during visual search humans often miss objects that are salient but at atypical sizes relative to the surrounding scene.

Eye movement strategies in face ethnicity categorization vs. face identification tasks.

A quick look at a face allows us to identify the person, their gender, and emotion. Humans direct their first eye movement towards points on the face that vary moderately across these common tasks and maximize performance. However, not known is the extent to which humans alter their oculomotor strategies to maximize accuracy in more specialized face categorization tasks. We studied the eye movements of Indian observers during a North vs. South Indian face categorization task and compared them to those in a person-identification task. We found that observers did not alter their first eye movement strategy for the ethnic categorization task, i.e., they directed their first fixations to a similar preferred point as in the person-identification task. To assess whether using a similar preferred point of fixation for both tasks resulted in a performance cost for the categorization task, we measured performance as a function of fixation position along the face. Fixating away from the preferred point of fixation reduced observer performance in the person identification task, but not in the ethnicity categorization task. We used computational modeling to assess whether the results could be explained by an interaction between the distribution of task information across the face and the foveated properties of the visual system. A foveated ideal observer analysis revealed a spatially more distributed task information and lower dependence of performance on the point of fixation for the ethnicity categorization task relative to the person identification. We conclude that, unlike the person identification task, humans can access the information for the ethnicity categorization task from various points of fixation. Thus, the observer strategy to utilize the typical person identification first eye movement for the ethnicity categorization task is a simple solution that incurs little or no performance cost.

Comparative observer effects in 2D and 3D localization tasks.

Purpose: Three-dimensional "volumetric" imaging methods are now a common component of medical imaging across many imaging modalities. Relatively little is known about how human observers localize targets masked by noise and clutter as they scroll through a 3D image and how it compares to a similar task confined to a single 2D slice. Approach: Gaussian random textures were used to represent noisy volumetric medical images. Subjects were able to freely inspect the images, including scrolling through 3D images as part of their search process. A total of eight experimental conditions were evaluated (2D versus 3D images, large versus small targets, power-law versus white noise). We analyze performance in these experiments using task efficiency and the classification image technique. Results: In 3D tasks, median response times were roughly nine times longer than 2D, with larger relative differences for incorrect trials. The efficiency data show a dissociation in which subjects perform with higher statistical efficiency in 2D tasks for large targets and higher efficiency in 3D tasks with small targets. The classification images suggest that a critical mechanism behind this dissociation is an inability to integrate across multiple slices to form a 3D localization response. The central slices of 3D classification images are remarkably similar to the corresponding 2D classification images. Conclusions: 2D and 3D tasks show similar weighting patterns between 2D images and the central slice of 3D images. There is relatively little weighting across slices in the 3D tasks, leading to lower task efficiency with respect to the ideal observer.

Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets.

Advances in 3D imaging technology are transforming how radiologists search for cancer1,2 and how security officers scrutinize baggage for dangerous objects.3 These new 3D technologies often improve search over 2D images4,5 but vastly increase the image data. Here, we investigate 3D search for targets of various sizes in filtered noise and digital breast phantoms. For a Bayesian ideal observer optimally processing the filtered noise and a convolutional neural network processing the digital breast phantoms, search with 3D image stacks increases target information and improves accuracy over search with 2D images. In contrast, 3D search by humans leads to high miss rates for small targets easily detected in 2D search, but not for larger targets more visible in the visual periphery. Analyses of human eye movements, perceptual judgments, and a computational model with a foveated visual system suggest that human errors can be explained by interaction among a target's peripheral visibility, eye movement under-exploration of the 3D images, and a perceived overestimation of the explored area. Instructing observers to extend the search reduces 75% of the small target misses without increasing false positives. Results with twelve radiologists confirm that even medical professionals reading realistic breast phantoms have high miss rates for small targets in 3D search. Thus, under-exploration represents a fundamental limitation to the efficacy with which humans search in 3D image stacks and miss targets with these prevalent image technologies.

EEG signatures of contextual influences on visual search with real scenes.

The use of scene context is a powerful way by which biological organisms guide and facilitate visual search. Although many studies have shown enhancements of target-related electroencephalographic activity (EEG) with synthetic cues, there have been fewer studies demonstrating such enhancements during search with scene context and objects in real world scenes. Here, observers covertly searched for a target in images of real scenes while we used EEG to measure the steady state visual evoked response to objects flickering at different frequencies. The target appeared in its typical contextual location or out of context while we controlled for low-level properties of the image including target saliency against the background and retinal eccentricity. A pattern classifier using EEG activity at the relevant modulated frequencies showed target detection accuracy increased when the target was in a contextually appropriate location. A control condition for which observers searched the same images for a different target orthogonal to the contextual manipulation, resulted in no effects of scene context on classifier performance, confirming that image properties cannot explain the contextual modulations of neural activity. Pattern classifier decisions for individual images were also related to the aggregated observer behavioral decisions for individual images. Together, these findings demonstrate target-related neural responses are modulated by scene context during visual search with real world scenes and can be related to behavioral search decisions.

Foveated Model Observers for Visual Search in 3D Medical Images.

Model observers have a long history of success in predicting human observer performance in clinically-relevant detection tasks. New 3D image modalities provide more signal information but vastly increase the search space to be scrutinized. Here, we compared standard linear model observers (ideal observers, non-pre-whitening matched filter with eye filter, and various versions of Channelized Hotelling models) to human performance searching in 3D 1/f2.8 filtered noise images and assessed its relationship to the more traditional location known exactly detection tasks and 2D search. We investigated two different signal types that vary in their detectability away from the point of fixation (visual periphery). We show that the influence of 3D search on human performance interacts with the signal's detectability in the visual periphery. Detection performance for signals difficult to detect in the visual periphery deteriorates greatly in 3D search but not in 3D location known exactly and 2D search. Standard model observers do not predict the interaction between 3D search and signal type. A proposed extension of the Channelized Hotelling model (foveated search model) that processes the image with reduced spatial detail away from the point of fixation, explores the image through eye movements, and scrolls across slices can successfully predict the interaction observed in humans and also the types of errors in 3D search. Together, the findings highlight the need for foveated model observers for image quality evaluation with 3D search.

Search of low-contrast liver lesions in abdominal CT: the importance of scrolling behavior.

Purpose: Visual search using volumetric images is becoming the standard in medical imaging. However, we do not fully understand how eye movement strategies mediate diagnostic performance. A recent study on computed tomography (CT) images showed that the search strategies of radiologists could be classified based on saccade amplitudes and cross-quadrant eye movements [eye movement index (EMI)] into two categories: drillers and scanners. Approach: We investigate how the number of times a radiologist scrolls in a given direction during analysis of the images (number of courses) could add a supplementary variable to use to characterize search strategies. We used a set of 15 normal liver CT images in which we inserted 1 to 5 hypodense metastases of two different signal contrast amplitudes. Twenty radiologists were asked to search for the metastases while their eye-gaze was recorded by an eye-tracker device (EyeLink1000, SR Research Ltd., Mississauga, Ontario, Canada). Results: We found that categorizing radiologists based on the number of courses (rather than EMI) could better predict differences in decision times, percentage of image covered, and search error rates. Radiologists with a larger number of courses covered more volume in more time, found more metastases, and made fewer search errors than those with a lower number of courses. Our results suggest that the traditional definition of drillers and scanners could be expanded to include scrolling behavior. Drillers could be defined as scrolling back and forth through the image stack, each time exploring a different area on each image (low EMI and high number of courses). Scanners could be defined as scrolling progressively through the stack of images and focusing on different areas within each image slice (high EMI and low number of courses). Conclusions: Together, our results further enhance the understanding of how radiologists investigate three-dimensional volumes and may improve how to teach effective reading strategies to radiology residents.

Evaluation of Convolutional Neural Networks for Search in 1/f 2.8 Filtered Noise and Digital Breast Tomosynthesis Phantoms.

With the advent of powerful convolutional neural networks (CNNs), recent studies have extended early applications of neural networks to imaging tasks thus making CNNs a potential new tool for assessing medical image quality. Here, we compare a CNN to model observers in a search task for two possible signals (a simulated mass and a smaller simulated micro-calcification) embedded in filtered noise and single slices of Digital Breast Tomosynthesis (DBT) virtual phantoms. For the case of the filtered noise, we show how a CNN can approximate the ideal observer for a search task, achieving a statistical efficiency of 0.77 for the microcalcification and 0.78 for the mass. For search in single slices of DBT phantoms, we show that a Channelized Hotelling Observer (CHO) performance is affected detrimentally by false positives related to anatomic variations and results in detection accuracy below human observer performance. In contrast, the CNN learns to identify and discount the backgrounds, and achieves performance comparable to that of human observer and superior to model observers (Proportion Correct for the microcalcification: CNN = 0.96; Humans = 0.98; CHO = 0.84; Proportion Correct for the mass: CNN = 0.98; Humans = 0.83; CHO = 0.51). Together, our results provide an important evaluation of CNN methods by benchmarking their performance against human and model observers in complex search tasks.

Measurement of the useful field of view for single slices of different imaging modalities and targets.

Purpose: With three-dimensional (3-D) images displayed as stacks of 2-D images, radiologists rely more heavily on vision away from their fixation point to visually process information, guide eye movements, and detect abnormalities. Thus the ability to detect targets away from the fixation point, commonly characterized as the useful field of view (UFOV), becomes critical for these 3-D imaging modalities. We investigate how the UFOV, defined as the eccentricity, in which detection performance degrades to a given probability, varies across imaging modalities and targets. Approach: We measure the detectability of different targets at various distances from gaze locations for single slices of liver computed tomography (CT), 2-D digital mammograms (DM), and single slices of digital breast tomosynthesis (DBT) cases. Observers with varying expertise were instructed to maintain their gaze at a point while a short display of the image was flashed and an eye tracker verified observer's steady fixation. Display times were 200 and 1000 ms for CT images and 500 ms for DM and DBT images. Results: We find variations in the UFOV from 9 to 12 deg for liver CT to as small as 2.5 to 5 deg for calcification clusters in breast images (DM and DBT). We compare our results to those reported in the literature for lung nodules and discuss the differences across methods used to measure the UFOV, their dependence on case selection/task difficulty, viewing conditions, and observer expertise. We propose a complementary measure defined in terms of performance degradation relative to the peak foveal performance (relative-UFOV) to circumvent UFOV's variations with case selection/task difficulty. Conclusion: Our results highlight the variations in the UFOV across imaging modalities, target types, observer expertise, and measurement methods and suggest an additional relative-UFOV measure to more thoroughly characterize the detection performance away from point of fixation.

Human observer templates for lesion discrimination tasks.

We investigate a series of two-alternative forced-choice (2AFC) discrimination tasks based on malignant features of abnormalities in low-dose lung CT scans. A total of 3 tasks are evaluated, and these consist of a size-discrimination task, a boundary-sharpness task, and an irregular-interior task. Target and alternative signal profiles for these tasks are modulated by one of two system transfer functions and embedded in ramp-spectrum noise that has been apodized for noise control in one of 4 different ways. This gives the resulting images statistical properties that are related to weak ground-glass lesions in axial slices of low-dose lung CT images. We investigate observer performance in these tasks using a combination of statistical efficiency and classification images. We report results of 24 2AFC experiments involving the three tasks. A staircase procedure is used to find the approximate 80% correct discrimination threshold in each task, with a subsequent set of 2,000 trials at this threshold. These data are used to estimate statistical efficiency with respect to the ideal observer for each task, and to estimate the observer template using the classification-image methodology. We find efficiency varies between the different tasks with lowest efficiency in the boundary-sharpness task, and highest efficiency in the non-uniform interior task. All three tasks produce clearly visible patterns of positive and negative weighting in the classification images. The spatial frequency plots of classification images show how apodization results in larger weights at higher spatial frequencies.