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.

A theoretical framework is proposed to understand binocular visual surface perception based on the idea of a mobile observer sampling images from random vantage points in space. Application of the generic sampling principle indicates that the visual system acts as if it were viewing surface layouts from generic not accidental vantage points. Through the observer's experience of optical sampling, which can be characterized geometrically, the visual system makes associative connections between images and surfaces, passively internalizing the conditional probabilities of image sampling from surfaces. This in turn enables the visual system to determine which surface a given image most strongly indicates. Thus, visual surface perception can be considered as inverse ecological optics based on learning through ecological optics. As such, it is formally equivalent to a degenerate form of Bayesian inference where prior probabilities are neglected.

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 object recognition is subserved by ventral temporal and occipital regions of the brain. Regions comprising the dorsal visual pathway have not been considered relevant for object recognition, despite strong categorical biases for tool-related information in those regions. Here, we show that dorsal stream processes influence object categorization. We used two techniques to render prime pictures invisible: continuous flash suppression (CFS), which obliterates input into ventral temporal regions, but leaves dorsal stream processes largely unaffected, and backward masking (BM), which allows suppressed information to reach both ventral and dorsal stream structures. Categorically congruent primes suppressed under CFS facilitate categorization of tools but have no effect on nonmanipulable objects; in contrast, primes rendered invisible through BM facilitate target categorization for both tools and nonmanipulable things. Our findings demonstrate that information computed by the dorsal stream is used in object categorization, but only for a category of manipulable objects.

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.

Visual short-term memory (VSTM) plays an important role in visual cognition. Although objects are located on different 3-dimensional (3-D) surfaces in the real world, how VSTM capacity may be influenced by the presence of multiple 3-D surfaces has never been examined. By manipulating binocular disparities of visual displays, the authors found that more colored objects could be held in VSTM when they were placed on 2 rather than on I planar 3-D surfaces. This between-surface benefit in VSTM was present only when binding of objects' colors to their 3-D locations was required (i.e., when observers needed to remember which color appeared where). When binding was not required, no between-surface benefit in VSTM was observed. This benefit in VSTM could not be attributed to the number of spatial locations attended within a given surface. It was not due to a general perceptual grouping effect either, because grouping by motion and grouping by different regions of the same surface did not yield the same benefit. This increment in capacity indicates that VSTM benefits from the placement of objects in a 3-D scene.