Person:

Nakayama, Ken

Proper characterization of each individual's unique pattern of strengths and weaknesses requires good measures of diverse abilities. Here, we advocate combining our growing understanding of neural and cognitive mechanisms with modern psychometric methods in a renewed effort to capture human individuality through a consideration of specific abilities. We articulate five criteria for the isolation and measurement of specific abilities, then apply these criteria to face recognition. We cleanly dissociate face recognition from more general visual and verbal recognition. This dissociation stretches across ability as well as disability, suggesting that specific developmental face recognition deficits are a special case of a broader specificity that spans the entire spectrum of human face recognition performance. Item-by-item results from 1,471 web-tested participants, included as supplementary information, fuel item analyses, validation, norming, and item response theory (IRT) analyses of our three tests: (a) the widely used Cambridge Face Memory Test (CFMT); (b) an Abstract Art Memory Test (AAMT), and (c) a Verbal Paired-Associates Memory Test (VPMT). The availability of this data set provides a solid foundation for interpreting future scores on these tests. We argue that the allied fields of experimental psychology, cognitive neuroscience, and vision science could fuel the discovery of additional specific abilities to add to face recognition, thereby providing new perspectives on human individuality.

For many problems in computer vision, human learners are considerably better than machines. Humans possess highly accurate internal recognition and learning mechanisms that are not yet understood, and they frequently have access to more extensive training data through a lifetime of unbiased experience with the visual world. We propose to use visual psychophysics to directly leverage the abilities of human subjects to build better machine learning systems. First, we use an advanced online psychometric testing platform to make new kinds of annotation data available for learning. Second, we develop a technique for harnessing these new kinds of information – “perceptual annotations” – for support vector machines. A key intuition for this approach is that while it may remain infeasible to dramatically increase the amount of data and high-quality labels available for the training of a given system, measuring the exemplar-by-exemplar difficulty and pattern of errors of human annotators can provide important information for regularizing the solution of the system at hand. A case study for the problem face detection demonstrates that this approach yields state-ofthe- art results on the challenging FDDB data set.

In our everyday lives, we view it as a matter of course that different people are good at different things. It can be surprising, in this context, to learn that most of what is known about cognitive ability variation across individuals concerns the broadest of all cognitive abilities; an ability referred to as general intelligence, general mental ability, or just g. In contrast, our knowledge of specific abilities, those that correlate little with g, is severely constrained. Here, we draw upon our experience investigating an exceptionally specific ability, face recognition, to make the case that many specific abilities could easily have been missed. In making this case, we derive key insights from earlier false starts in the measurement of face recognition’s variation across individuals, and we highlight the convergence of factors that enabled the recent discovery that this variation is specific. We propose that the case of face recognition ability illustrates a set of tools and perspectives that could accelerate fruitful work on specific cognitive abilities. By revealing relatively independent dimensions of human ability, such work would enhance our capacity to understand the uniqueness of individual minds.

The muscles that control the pupil are richly innervated by the autonomic nervous system. While there are central pathways that drive pupil dilations in relation to arousal, there is no anatomical evidence that cortical centers involved with visual selective attention innervate the pupil. In this study, we show that such connections must exist. Specifically, we demonstrate a novel Pupil Frequency Tagging (PFT) method, where oscillatory changes in stimulus brightness over time are mirrored by pupil constrictions and dilations. We find that the luminance–induced pupil oscillations are enhanced when covert attention is directed to the flicker stimulus and when targets are correctly detected in an attentional tracking task. These results suggest that the amplitudes of pupil responses closely follow the allocation of focal visual attention and the encoding of stimuli. PFT provides a new opportunity to study top–down visual attention itself as well as identifying the pathways and mechanisms that support this unexpected phenomenon.

Visual perception and awareness have strict limitations. We suggest that one source of these limitations is the representational architecture of the visual system. Under this view, the extent to which items activate the same neural channels constrains the amount of information that can be processed by the visual system and ultimately reach awareness. Here, we measured how well stimuli from different categories (e.g., faces and cars) blocked one another from reaching awareness using two distinct paradigms that render stimuli invisible: visual masking and continuous flash suppression. Next, we used fMRI to measure the similarity of the neural responses elicited by these categories across the entire visual hierarchy. Overall, we found strong brain-behavior correlations within the ventral pathway, weaker correlations in the dorsal pathway, and no correlations in early visual cortex (V1-V3). These results suggest that the organization of higher level visual cortex constrains visual awareness and the overall processing capacity of visual cognition.

Research in competitive games has exclusively focused on how opponent models are developed through previous outcomes and how peoples' decisions relate to normative predictions. Little is known about how rapid impressions of opponents operate and influence behavior in competitive economic situations, although such subjective impressions have been shown to influence cooperative decision-making. This study investigates whether an opponent's face influences players' wagering decisions in a zero-sum game with hidden information. Participants made risky choices in a simplified poker task while being presented opponents whose faces differentially correlated with subjective impressions of trust. Surprisingly, we find that threatening face information has little influence on wagering behavior, but faces relaying positive emotional characteristics impact peoples' decisions. Thus, people took significantly longer and made more mistakes against emotionally positive opponents. Differences in reaction times and percent correct were greatest around the optimal decision boundary, indicating that face information is predominantly used when making decisions during medium-value gambles. Mistakes against emotionally positive opponents resulted from increased folding rates, suggesting that participants may have believed that these opponents were betting with hands of greater value than other opponents. According to these results, the best “poker face” for bluffing may not be a neutral face, but rather a face that contains emotional correlates of trustworthiness. Moreover, it suggests that rapid impressions of an opponent play an important role in competitive games, especially when people have little or no experience with an opponent.

Emotional and affective processing imposes itself over cognitive processes and modulates our perception of the surrounding environment. In two experiments, we addressed the issue of whether nonconscious processing of affect can take place even under deep states of unawareness, such as those induced by interocular suppression techniques, and can elicit an affective response that can influence our understanding of the surrounding environment. In Experiment 1, participants judged the likeability of an unfamiliar item--a Chinese character--that was preceded by a face expressing a particular emotion (either happy or angry). The face was rendered invisible through an interocular suppression technique (continuous flash suppression; CFS). In Experiment 2, backward masking (BM), a less robust masking technique, was used to render the facial expressions invisible. We found that despite equivalent phenomenological suppression of the visual primes under CFS and BM, different patterns of affective processing were obtained with the two masking techniques. Under BM, nonconscious affective priming was obtained for both happy and angry invisible facial expressions. However, under CFS, nonconscious affective priming was obtained only for angry facial expressions. We discuss an interpretation of this dissociation between affective processing and visual masking techniques in terms of distinct routes from the retina to the amygdala.

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face amongst cars, body amongst hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macro-scale sectors as well as smaller meso-scale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system.

Is visual attention required for visual consciousness? In the past decade, many have claimed that awareness can arise in the absence of attention. This claim is largely based on the notion that natural scene (or "gist") perception occurs without attention. Against this, we first show that when observers perform a variety of demanding, sustained attention tasks, inattentional blindness occurs for natural scenes. In addition, scene perception is impaired under dual-task conditions, but only when using sufficiently demanding tasks. This suggests that previous studies claiming to have demonstrated scene perception without attention failed to fully engage attention and that natural scene perception does indeed require attention. Thus, natural scene perception is not a preattentive process and cannot be used to support the idea of awareness without attention.