Matthew F. Peterson

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.

Statistical decision theory to relate neurons to behavior in the study of covert visual attention.

Scrutiny of the numerous physiology and imaging studies of visual attention reveal that integration of results from neuroscience with the classic theories of visual attention based on behavioral work is not simple. The different subfields have pursued different questions, used distinct experimental paradigms and developed diverse models. The purpose of this review is to use statistical decision theory and computational modeling to relate classic theories of attention in psychological research to neural observables such as mean firing rate or functional imaging BOLD response, tuning functions, Fano factor, neuronal index of detectability and area under the receiver operating characteristic (ROC). We focus on cueing experiments and attempt to distinguish two major leading theories in the study of attention: limited resources model/increased sensitivity vs. selection/differential weighting. We use Bayesian ideal observer (BIO) modeling, in which predictive cues or prior knowledge change the differential weighting (prior) of sensory information to generate predictions of behavioral and neural observables based on Gaussian response variables and Poisson process neural based models. The ideal observer model can be modified to represent a number of classic psychological theories of visual attention by including hypothesized human attentional limited resources in the same way sequential ideal observer analysis has been used to include physiological processing components of human spatial vision (Geisler, W. S. (1989). Sequential ideal-observer analysis of visual discrimination. Psychological Review 96, 267-314.). In particular we compare new biologically plausible implementations of the BIO and variant models with limited resources. We find a close relationship between the behavioral effects of cues predicted by the models developed in the field of human psychophysics and their neuron-based analogs. Critically, we show that cue effects on experimental observables such as mean neural activity, variance, Fano factor and neuronal index of detectability can be consistent with the two major theoretical models of attention depending on whether the neuron is assumed to be computing likelihoods, log-likelihoods or a simple model operating directly on the Poisson variable. Change in neuronal tuning functions can also be consistent with both theories depending on whether the change in tuning is along the dimension being experimentally cued or a different dimension. We show that a neurons sensitivity appropriately measured using the area under the Receive Operating Characteristic curve can be used to distinguish across both theories and is robust to the many transformations of the decision variable. We provide a summary table with the hope that it might provide some guidance in interpreting past results as well as planning future studies.

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.

Essential role of postsynaptic NMDA receptors in developmental refinement of excitatory synapses

Neurons in the brains of newborns are usually connected with many other neurons through weak synapses. This early pattern of connectivity is refined through pruning of many immature connections and strengthening of the remaining ones. NMDA receptors (NMDARs) are essential for the development of excitatory synapses, but their role in synaptic refinement is controversial. Although chronic application of blockers or global knockdown of NMDARs disrupts developmental refinement in many parts of the brain, the ubiquitous presence of NMDARs makes it difficult to dissociate direct effects from indirect ones. We addressed this question in the thalamus by using genetic mosaic deletion of NMDARs. We demonstrate that pruning and strengthening of immature synapses are blocked in neurons without NMDARs, but occur normally in neighboring neurons with NMDARs. Our data support a model in which activation of NMDARs in postsynaptic neurons initiates synaptic refinement.

Learning optimal eye movements to unusual faces

Eye movements, which guide the foveas high resolution and computational power to relevant areas of the visual scene, are integral to efficient, successful completion of many visual tasks. How humans modify their eye movements through experience with their perceptual environments, and its functional role in learning new tasks, has not been fully investigated. Here, we used a face identification task where only the mouth discriminated exemplars to assess if, how, and when eye movement modulation may mediate learning. By interleaving trials of unconstrained eye movements with trials of forced fixation, we attempted to separate the contributions of eye movements and covert mechanisms to performance improvements. Without instruction, a majority of observers substantially increased accuracy and learned to direct their initial eye movements towards the optimal fixation point. The proximity of an observers default face identification eye movement behavior to the new optimal fixation point and the observers peripheral processing ability were predictive of performance gains and eye movement learning. After practice in a subsequent condition in which observers were directed to fixate different locations along the face, including the relevant mouth region, all observers learned to make eye movements to the optimal fixation point. In this fully learned state, augmented fixation strategy accounted for 43% of total efficiency improvements while covert mechanisms accounted for the remaining 57%. The findings suggest a critical role for eye movement planning to perceptual learning, and elucidate factors that can predict when and how well an observer can learn a new task with unusual exemplars.

The surprisingly high human efficiency at learning to recognize faces

We investigated the ability of humans to optimize face recognition performance through rapid learning of individual relevant features. We created artificial faces with discriminating visual information heavily concentrated in single features (nose, eyes, chin or mouth). In each of 2500 learning blocks a feature was randomly selected and retained over the course of four trials, during which observers identified randomly sampled, noisy face images. Observers learned the discriminating feature through indirect feedback, leading to large performance gains. Performance was compared to a learning Bayesian ideal observer, resulting in unexpectedly high learning compared to previous studies with simpler stimuli. We explore various explanations and conclude that the higher learning measured with faces cannot be driven by adaptive eye movement strategies but can be mostly accounted for by suboptimalities in human face discrimination when observers are uncertain about the discriminating feature. We show that an initial bias of humans to use specific features to perform the task even though they are informed that each of four features is equally likely to be the discriminatory feature would lead to seemingly supra-optimal learning. We also examine the possibility of inefficient human integration of visual information across the spatially distributed facial features. Together, the results suggest that humans can show large performance improvement effects in discriminating faces as they learn to identify the feature containing the discriminatory information.

Perceptual learning of discriminating features for facial recognition

Purpose Various studies have shown humans preferentially utilize certain facial features in identity discrimination (Schyns et. al, 2002). However, there has not been thorough investigation into the ability of humans to learn which features are more discriminating. The goal of this study is to measure human perceptual learning in comparison to a learning optimal Bayesian for a face identification task.