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.

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.

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.

The wisdom of crowds for visual search.

Decision-making accuracy typically increases through collective integration of people's judgments into group decisions, a phenomenon known as the wisdom of crowds. For simple perceptual laboratory tasks, classic signal detection theory specifies the upper limit for collective integration benefits obtained by weighted averaging of people's confidences, and simple majority voting can often approximate that limit. Life-critical perceptual decisions often involve searching large image data (e.g., medical, security, and aerial imagery), but the expected benefits and merits of using different pooling algorithms are unknown for such tasks. Here, we show that expected pooling benefits are significantly greater for visual search than for single-location perceptual tasks and the prediction given by classic signal detection theory. In addition, we show that simple majority voting obtains inferior accuracy benefits for visual search relative to averaging and weighted averaging of observers' confidences. Analysis of gaze behavior across observers suggests that the greater collective integration benefits for visual search arise from an interaction between the foveated properties of the human visual system (high foveal acuity and low peripheral acuity) and observers' nonexhaustive search patterns, and can be predicted by an extended signal detection theory framework with trial to trial sampling from a varying mixture of high and low target detectabilities across observers (SDT-MIX). These findings advance our theoretical understanding of how to predict and enhance the wisdom of crowds for real world search tasks and could apply more generally to any decision-making task for which the minority of group members with high expertise varies from decision to decision.

Domain Specificity of Oculomotor Learning after Changes in Sensory Processing.

Humans visually process the world with varying spatial resolution and can program their eye movements optimally to maximize information acquisition for a variety of everyday tasks. Diseases such as macular degeneration can change visual sensory processing, introducing central vision loss (a scotoma). However, humans can learn to direct a new preferred retinal location to regions of interest for simple visual tasks. Whether such learned compensatory saccades are optimal and generalize to more complex tasks, which require integrating information across a large area of the visual field, is not well understood. Here, we explore the possible effects of central vision loss on the optimal saccades during a face identification task, using a gaze-contingent simulated scotoma. We show that a new foveated ideal observer with a central scotoma correctly predicts that the human optimal point of fixation to identify faces shifts from just below the eyes to one that is at the tip of the nose and another at the top of the forehead. However, even after 5000 trials, humans of both sexes surprisingly do not change their initial fixations to adapt to the new optimal fixation points to faces. In contrast, saccades do change for tasks such as object following and to a lesser extent during search. Our findings argue against a central brain motor-compensatory mechanism that generalizes across tasks. They instead suggest task specificity in the learning of oculomotor plans in response to changes in front-end sensory processing and the possibility of separate domain-specific representations of learned oculomotor plans in the brain.SIGNIFICANCE STATEMENT The mechanism by which humans adapt eye movements in response to central vision loss is still not well understood and carries importance for gaining a fundamental understanding of brain plasticity. We show that although humans adapt their eye movements for simpler tasks such as object following and search, these adaptations do not generalize to more complex tasks such as face identification. We provide the first computational model to predict where humans with central vision loss should direct their eye movements in face identification tasks, which could become a critical tool in making patient-specific recommendations. Based on these results, we suggest a novel theory for oculomotor learning: a distributed representation of learned eye-movement plans represented in domain-specific areas of the brain.

Object detection through search with a foveated visual system.

Humans and many other species sense visual information with varying spatial resolution across the visual field (foveated vision) and deploy eye movements to actively sample regions of interests in scenes. The advantage of such varying resolution architecture is a reduced computational, hence metabolic cost. But what are the performance costs of such processing strategy relative to a scheme that processes the visual field at high spatial resolution? Here we first focus on visual search and combine object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We develop a foveated object detector that processes the entire scene with varying resolution, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. We compared the foveated object detector against a non-foveated version of the same object detector which processes the entire image at homogeneous high spatial resolution. We evaluated the accuracy of the foveated and non-foveated object detectors identifying 20 different objects classes in scenes from a standard computer vision data set (the PASCAL VOC 2007 dataset). We show that the foveated object detector can approximate the performance of the object detector with homogeneous high spatial resolution processing while bringing significant computational cost savings. Additionally, we assessed the impact of foveation on the computation of bottom-up saliency. An implementation of a simple foveated bottom-up saliency model with eye movements showed agreement in the selection of top salient regions of scenes with those selected by a non-foveated high resolution saliency model. Together, our results might help explain the evolution of foveated visual systems with eye movements as a solution that preserves perceptual performance in visual search while resulting in computational and metabolic savings to the brain.

Foveal analysis and peripheral selection during active visual sampling.

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.

Temporal and peripheral extraction of contextual cues from scenes during visual search.

Scene context is known to facilitate object recognition and guide visual search, but little work has focused on isolating image-based cues and evaluating their contributions to eye movement guidance and search performance. Here, we explore three types of contextual cues (a co-occurring object, the configuration of other objects, and the superordinate category of background elements) and assess their joint contributions to search performance in the framework of cue-combination and the temporal unfolding of their extraction. We also assess whether observers' ability to extract each contextual cue in the visual periphery is a bottleneck that determines the utilization and contribution of each cue to search guidance and decision accuracy. We find that during the first four fixations of a visual search task observers first utilize the configuration of objects for coarse eye movement guidance and later use co-occurring object information for finer guidance. In the absence of contextual cues, observers were suboptimally biased to report the target object as being absent. The presence of the co-occurring object was the only contextual cue that had a significant effect in reducing decision bias. The early influence of object-based cues on eye movements is corroborated by a clear demonstration of observers' ability to extract object cues up to 16° into the visual periphery. The joint contributions of the cues to decision search accuracy approximates that expected from the combination of statistically independent cues and optimal cue combination. Finally, the lack of utilization and contribution of the background-based contextual cue to search guidance cannot be explained by the availability of the contextual cue in the visual periphery; instead it is related to background cues providing the least inherent information about the precise location of the target in the scene.

Looking just below the eyes is optimal across face recognition tasks.

When viewing a human face, people often look toward the eyes. Maintaining good eye contact carries significant social value and allows for the extraction of information about gaze direction. When identifying faces, humans also look toward the eyes, but it is unclear whether this behavior is solely a byproduct of the socially important eye movement behavior or whether it has functional importance in basic perceptual tasks. Here, we propose that gaze behavior while determining a person's identity, emotional state, or gender can be explained as an adaptive brain strategy to learn eye movement plans that optimize performance in these evolutionarily important perceptual tasks. We show that humans move their eyes to locations that maximize perceptual performance determining the identity, gender, and emotional state of a face. These optimal fixation points, which differ moderately across tasks, are predicted correctly by a Bayesian ideal observer that integrates information optimally across the face but is constrained by the decrease in resolution and sensitivity from the fovea toward the visual periphery (foveated ideal observer). Neither a model that disregards the foveated nature of the visual system and makes fixations on the local region with maximal information, nor a model that makes center-of-gravity fixations correctly predict human eye movements. Extension of the foveated ideal observer framework to a large database of real-world faces shows that the optimality of these strategies generalizes across the population. These results suggest that the human visual system optimizes face recognition performance through guidance of eye movements not only toward but, more precisely, just below the eyes.

Initial eye movements during face identification are optimal and similar across cultures.

Culture influences not only human high-level cognitive processes but also low-level perceptual operations. Some perceptual operations, such as initial eye movements to faces, are critical for extraction of information supporting evolutionarily important tasks such as face identification. The extent of cultural effects on these crucial perceptual processes is unknown. Here, we report that the first gaze location for face identification was similar across East Asian and Western Caucasian cultural groups: Both fixated a featureless point between the eyes and the nose, with smaller between-group than within-group differences and with a small horizontal difference across cultures (8% of the interocular distance). We also show that individuals of both cultural groups initially fixated at a slightly higher point on Asian faces than on Caucasian faces. The initial fixations were found to be both fundamental in acquiring the majority of information for face identification and optimal, as accuracy deteriorated when observers held their gaze away from their preferred fixations. An ideal observer that integrated facial information with the human visual system's varying spatial resolution across the visual field showed a similar information distribution across faces of both races and predicted initial human fixations. The model consistently replicated the small vertical difference between human fixations to Asian and Caucasian faces but did not predict the small horizontal leftward bias of Caucasian observers. Together, the results suggest that initial eye movements during face identification may be driven by brain mechanisms aimed at maximizing accuracy, and less influenced by culture. The findings increase our understanding of the interplay between the brain's aims to optimally accomplish basic perceptual functions and to respond to sociocultural influences.

Neural representations of contextual guidance in visual search of real-world scenes.

Exploiting scene context and object-object co-occurrence is critical in guiding eye movements and facilitating visual search, yet the mediating neural mechanisms are unknown. We used functional magnetic resonance imaging while observers searched for target objects in scenes and used multivariate pattern analyses (MVPA) to show that the lateral occipital complex (LOC) can predict the coarse spatial location of observers' expectations about the likely location of 213 different targets absent from the scenes. In addition, we found weaker but significant representations of context location in an area related to the orienting of attention (intraparietal sulcus, IPS) as well as a region related to scene processing (retrosplenial cortex, RSC). Importantly, the degree of agreement among 100 independent raters about the likely location to contain a target object in a scene correlated with LOC's ability to predict the contextual location while weaker but significant effects were found in IPS, RSC, the human motion area, and early visual areas (V1, V3v). When contextual information was made irrelevant to observers' behavioral task, the MVPA analysis of LOC and the other areas' activity ceased to predict the location of context. Thus, our findings suggest that the likely locations of targets in scenes are represented in various visual areas with LOC playing a key role in contextual guidance during visual search of objects in real scenes.

Isolating the neural mechanisms of interference during continuous multisensory dual-task performance.

The need to engage in multiple tasks simultaneously is often encountered in everyday experience, but coordinating between two or more tasks can lead to impaired performance. Typical investigations of multitasking impairments have focused on the performance of two tasks presented in close temporal proximity on discrete trials; however, such paradigms do not match well with the continuous performance situations more typically encountered outside the laboratory. As a result, the stages of information processing that are affected during multisensory continuous dual tasks and how these changes in processing relate to behavior remain unclear. To address these issues, participants were presented simultaneous rapid visual and auditory stimulus sequences under three conditions: attend visual only, attend auditory only, and dual attention (attend both visual and auditory). Performance, measured in terms of response time and perceptual sensitivity (d'), revealed dual-task impairments only in the auditory task. Neural activity, measured by the ERP technique, revealed that both early stage sensory processing and later cognitive processing of the auditory task were affected by dual-task performance, but similar stages of processing of the visual task were not. Critically, individual differences in neural activity at both early and late stages of information processing accurately rank-ordered individuals based on the observed difference in behavioral performance between the single and dual attention conditions. These results reveal relationships between behavioral performance and the neural correlates of both early and late stage information processing that provide key insights into the complex interplay between the brain and behavior when multiple tasks are performed continuously.

Template changes with perceptual learning are driven by feature informativeness.

Perceptual learning changes the way the human visual system processes stimulus information. Previous studies have shown that the human brain's weightings of visual information (the perceptual template) become better matched to the optimal weightings. However, the dynamics of the template changes are not well understood. We used the classification image method to investigate whether visual field or stimulus properties govern the dynamics of the changes in the perceptual template. A line orientation discrimination task where highly informative parts were placed in the peripheral visual field was used to test three hypotheses: (1) The template changes are determined by the visual field structure, initially covering stimulus parts closer to the fovea and expanding toward the periphery with learning; (2) the template changes are object centered, starting from the center and expanding toward edges; and (3) the template changes are determined by stimulus information, starting from the most informative parts and expanding to less informative parts. Results show that, initially, the perceptual template contained only the more peripheral, highly informative parts. Learning expanded the template to include less informative parts, resulting in an increase in sampling efficiency. A second experiment interleaved parts with high and low signal-to-noise ratios and showed that template reweighting through learning was restricted to stimulus elements that are spatially contiguous to parts with initial high template weights. The results suggest that the informativeness of features determines how the perceptual template changes with learning. Further, the template expansion is constrained by spatial proximity.

What do saliency models predict?

Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements.

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.

Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images.

Purpose: Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3D search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors. Approach: Sixteen nonexpert observers searched through digital breast tomosynthesis (DBT) phantoms and single cross-sectional slices of the DBT phantoms. The 3D/2D searches occurred with and without a convolutional neural network (CNN)-based CADe support system. The model provided observers with bounding boxes superimposed on the image stimuli while they looked for a small microcalcification signal and a large mass signal. Eye gaze positions were recorded and correlated with changes in the area under the ROC curve (AUC). Results: The CNN-CADe improved the 3D search for the small microcalcification signal ( Δ AUC = 0.098 , p = 0.0002 ) and the 2D search for the large mass signal ( Δ AUC = 0.076 , p = 0.002 ). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D ( Δ Δ AUC = 0.066 , p = 0.035 ). Analysis of individual differences suggests that those who explored the least with eye movements benefited the most from the CNN-CADe ( r = - 0.528 , p = 0.036 ). However, for the large signal, the 2D benefit was not significantly greater than the 3D benefit ( Δ Δ AUC = 0.033 , p = 0.133 ). Conclusion: The CNN-CADe brings unique performance benefits to the 3D (versus 2D) search of small signals by reducing errors caused by the underexploration of the volumetric data.

Feature-independent neural coding of target detection during search of natural scenes.

Visual search requires humans to detect a great variety of target objects in scenes cluttered by other objects or the natural environment. It is unknown whether there is a general purpose neural detection mechanism in the brain that codes the presence of a wide variety of categories of objects embedded in natural scenes. We provide evidence for a feature-independent coding mechanism for detecting behaviorally relevant targets in natural scenes in the dorsal frontoparietal network. Pattern classifiers using single-trial fMRI responses in the dorsal frontoparietal network reliably predicted the presence of 368 different target objects and also the observer's choices. Other vision-related areas such as the primary visual cortex, lateral occipital complex, the parahippocampal, and the fusiform gyri did not predict target presence, while high-level association areas related to general purpose decision making, including the dorsolateral prefrontal cortex and anterior cingulate, did. Activity in the intraparietal sulcus, a main area in the dorsal frontoparietal network, correlated with observers' decision confidence and with the task difficulty of individual images. These results cannot be explained by physical differences across images or eye movements. Thus, the dorsal frontoparietal network detects behaviorally relevant targets in natural scenes independent of their defining visual features and may be the human analog of the priority map in monkey lateral intraparietal cortex.

Individual differences in eye movements during face identification reflect observer-specific optimal points of fixation.

In general, humans tend to first look just below the eyes when identifying another person. Does everybody look at the same place on a face during identification, and, if not, does this variability in fixation behavior lead to functional consequences? In two conditions, observers had their free eye movements recorded while they performed a face-identification task. In another condition, the same observers identified faces while their gaze was restricted to specific locations on each face. We found substantial differences, which persisted over time, in where individuals chose to first move their eyes. Observers' systematic departure from a canonical, theoretically optimal fixation point did not correlate with performance degradation. Instead, each individual's looking preference corresponded to an idiosyncratic performance-maximizing point of fixation: Those who looked lower on the face performed better when forced to fixate the lower part of the face. The results suggest an observer-specific synergy between the face-recognition and eye movement systems that optimizes face-identification performance.