Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression.

Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences.

Converging operations and the role of perceptual and decisional influences on the perception of faces: Neural and behavioral evidence.

Theoretical analyses suggest that the regularities indicative of holistic processing can be obtained by combinations of perceptual and decisional factors. Kuefner and colleagues used electrophysiological results to suggest that the composite face effect is driven solely by perceptual factors. Two limitations of their approach are (a) it did not involve behavioral measures of perceptual sensitivity or bias, and (b) it is unclear how the measures used in that study are consistent with other measures of perceptual and decisional processing. Eight observers completed three tasks involving the stimuli used by Kuefner et al.. The first was a direct replication. The second was a complete identification task, associated with the perceptual and decisional distinctions formalized in general recognition theory. The third was an implementation of the Eriksen fianker task, which allows for a pattern of results that have been interpreted in terms of perceptual and decisional influences. While the empirical distinctions used by Kuefner et al. were not consistent with either the EEG data from the other tasks or the established behavioral measures of perceptual sensitivity and decisional bias, the inferences drawn from the EEG and behavioral data from those tasks were consistent with one another, underscoring the importance of converging operations.

The effect of multispectral image fusion enhancement on human efficiency.

The visual system can be highly influenced by changes to visual presentation. Thus, numerous techniques have been developed to augment imagery in an attempt to improve human perception. The current paper examines the potential impact of one such enhancement, multispectral image fusion, where imagery captured in varying spectral bands (e.g., visible, thermal, night vision) is algorithmically combined to produce an output to strengthen visual perception. We employ ideal observer analysis over a series of experimental conditions to (1) establish a framework for testing the impact of image fusion over the varying aspects surrounding its implementation (e.g., stimulus content, task) and (2) examine the effectiveness of fusion on human information processing efficiency in a basic application. We used a set of rotated Landolt C images captured with a number of individual sensor cameras and combined across seven traditional fusion algorithms (e.g., Laplacian pyramid, principal component analysis, averaging) in a 1-of-8 orientation task. We found that, contrary to the idea of fused imagery always producing a greater impact on perception, single-band imagery can be just as influential. Additionally, efficiency data were shown to fluctuate based on sensor combination instead of fusion algorithm, suggesting the need for examining multiple factors to determine the success of image fusion. Our use of ideal observer analysis, a popular technique from the vision sciences, provides not only a standard for testing fusion in direct relation to the visual system but also allows for comparable examination of fusion across its associated problem space of application.

The Impact of Symmetry on the Efficiency of Human Face Perception.

The presence of symmetric properties in a stimulus has been shown to often exert an influence on perception and information processing. Investigations into symmetry have given rise to the notion that it is processed easily and efficiently by the human visual system. However, only a handful of studies have attempted to actually measure symmetry's role in the efficiency of information use. We explored the impact of symmetry on the perception of human faces, a domain where it has been thought to play a particularly important role. Specifically, we measured information processing efficiency, defined as human performance relative to that of an ideal observer, for the detection, discrimination, and identification of symmetric and asymmetric faces. Surprisingly, we found no evidence for significant differences in efficiency between these two classes of stimuli. Training yielded significant improvements in overall efficiency, but had no significant effect on the relative efficiency of asymmetric and symmetric face identification. Our results indicate that although symmetry may be important to other aspects of face perception (e.g., perceived beauty), it has no discernible impact upon the efficiency with which information is used when detecting, discriminating, and identifying faces.

The perception of a familiar face is no more than the sum of its parts.

Why do faces become easier to recognize with repeated exposure? Previous research has suggested that familiarity may induce a qualitative shift in visual processing from an independent analysis of individual facial features to analysis that includes information about the relationships among features (Farah, Wilson, Drain, & Tanaka Psychological Review, 105, 482-498, 1998; Maurer, Grand, & Mondloch Trends in Cognitive Science, 6, 255-260, 2002). We tested this idea by using a "summation-at-threshold" technique (Gold, Mundy, & Tjan Psychological Science, 23, 427-434, 2012; Nandy & Tjan Journal of Vision, 8, 3.1-20, 2008), in which an observer's ability to recognize each individual facial feature shown independently is used to predict their ability to recognize all of the features shown in combination. We find that, although people are better overall at recognizing familiar as opposed to unfamiliar faces, their ability to integrate information across features is similar for unfamiliar and highly familiar faces and is well predicted by their ability to recognize each of the facial features shown in isolation. These results are consistent with the idea that familiarity has a quantitative effect on the efficiency with which information is extracted from individual features, rather than a qualitative effect on the process by which features are combined.

The efficiency of dynamic and static facial expression recognition.

Unlike frozen snapshots of facial expressions that we often see in photographs, natural facial expressions are dynamic events that unfold in a particular fashion over time. But how important are the temporal properties of expressions for our ability to reliably extract information about a person's emotional state? We addressed this question experimentally by gauging human performance in recognizing facial expressions with varying temporal properties relative to that of a statistically optimal ("ideal") observer. We found that people recognized emotions just as efficiently when viewing them as naturally evolving dynamic events, temporally reversed events, temporally randomized events, or single images frozen in time. Our results suggest that the dynamic properties of human facial movements may play a surprisingly small role in people's ability to infer the emotional states of others from their facial expressions.

Evidence for criterion shifts in visual perceptual learning: data and implications.

Work on visual perceptual learning for contrast detection has shown that reliable decreases in detection thresholds are accompanied by reliable increases in false alarm rates (Wenger & Rasche, 2006). The present study assesses the robustness and replicability of these changes, demonstrating that they are independent of a variety of task demands (i.e., the specific method used for perceptual practice and threshold estimation) and the presence or absence of trial-by-trial feedback and that the source of the increases can be found in shifts in changes in sensitivity and in bias for detection, identification, or both. Although the increase in false alarm rates suggests a strategic shift in response criteria for detection, we demonstrate that there are multiple potential explanations, including explanations that do not require strategic shifts on the part of the observer. The empirical evidence and analysis of alternative explanations reinforce the inference that visual perceptual learning may involve more than changes in perceptual sensitivity and that cortical circuits beyond the primary visual areas may be involved.