Odor-evoked memories: The importance of choosing the right odor

Odors are known to entertain a special link with memory. However, the scientific literature investigating odors as powerful reminders of past experiences shows mixed results. This can be partly due to poor consistency in methodological approaches, especially concerning stimuli choice. Here, we presented 64 odorants to 130 young adults and asked them to freely report what each odor evoked to them. Responses were sorted in 1) mere odors' identification attempts and 2) any other memory-based verbal content, whose phenomenological properties were analyzed quantitatively and qualitatively. As expected, only a limited number of odors managed to spontaneously trigger high-quality autobiographical memories. In most cases, people engage in an odor identification task or evoke unspecific memory contents. We finally selected a pool of 20 odors as a function of the memory contents they're more likely to trigger. This enabled us to formulate recommendations for professionals (researchers or practitioners) performing olfactory-based memory stimulation.

Semantic, Typicality and Odor Representation: A Cross-cultural Study

This study investigated odor-category organization in three cultures by evaluating (i) the relationship between linguistic and perceptual categorization and (ii) the existence of an internal structure of odor categories. In the first experiment, three groups of 30 participants from American, French and Vietnamese cultures performed a sorting task. The first group sorted 40 odorants on the basis of odor similarity, the second group sorted 40 odor names on the basis of name similarity and the last group sorted 40 odor names on the basis of imagined odor similarity. Results showed that odor categorization was based on perceptual or conceptual similarity and was in part independent of word and imagined categorizations. In the second experiment, another group of 30 participants from each culture rated the typicality of the odorants for 11 odor categories. Results showed that some odorants were rated as more typical than others. Moreover, the typicality gradient predicted the odor space obtained in the odor sorting task in a consensual way among the three cultures. These results suggest that, as for other categories, odor categories are based on perceptual similarities rather than on semantic cues. Moreover odor-category structure might have a core representation which might be common to different cultures with boundaries which might be more culturally dependent.

From rotation to disfiguration: testing a dual-strategy model for recognition of faces across view angles

A study is reported of the effect of distinctive marks on the recognition of unfamiliar faces across view angles. Subjects were asked to memorize a set of target faces, half of which had distinctive marks. Recognition was assessed by presenting the target faces, either in the same orientation, or after 90 degrees rotation, mixed with an equal number of distractors. Results show that the effect of distinctive marks depends on the view presented during learning. When a frontal view was learned, as predicted by the dual-strategy model [Valentin et al, in press, in Computational, Geometric, and Process Perspectives on Facial Cognition: Context and Challenges Eds T Wenger, J Townsend (Hillsdale, NJ: Lawrence Erlbaum Associates)], distinctive marks improve recognition performance in the 90 degrees condition but not in the 0 degree condition. However, when a profile view was learned, distinctive marks have no effect on recognition performance, even in the 90 degrees condition where a frontal view is tested.

What represents a face? A computational approach for the integration of physiological and psychological data

Empirical studies of face recognition suggest that faces might be stored in memory by means of a few canonical representations. The nature of these canonical representations is, however, unclear. Although psychological data show a three-quarter-view advantage, physiological studies suggest profile and frontal views are stored in memory. A computational approach to reconcile these findings is proposed. The pattern of results obtained when different views, or combinations of views, are used as the internal representation of a two-stage identification network consisting of an autoassociative memory followed by a radial-basis-function network are compared. Results show that (i) a frontal and a profile view are sufficient to reach the optimal network performance; and (ii) all the different representations produce a three-quarter view advantage, similar to that generally described for human subjects. These results indicate that although three-quarter views yield better recognition than other views, they need not be stored in memory to show this advantage.

The perception of face gender: the role of stimulus structure in recognition and classification

The perception of face gender was examined in the context of extending "face space" models of human face representations to include the perceptual categories defined by male and female faces. We collected data on the recognizability, gender classifiability (reaction time to classify a face as male/female), attractiveness, and masculinity/femininity of individual male and female faces. Factor analyses applied separately to the data for male and female faces yielded the following results. First, for both male and female faces, the recognizability and gender classifiability of faces were independent--a result inconsistent with the hypothesis that both recognizability and gender classifiability depend on a face's "distance" from the subcategory gender prototype. Instead, caricatured aspects of gender (femininity/masculinity ratings) related to the gender classifiability of the faces. Second, facial attractiveness related inversely to face recognizability for male, but not for female, faces--a result that resolves inconsistencies in previous studies. Third, attractiveness and femininity for female faces were nearly equivalent, but attractiveness and masculinity for male faces were not equivalent. Finally, we applied principal component analysis to the pixel-coded face images with the aim of extracting measures related to the gender classifiability and recognizability of individual faces. We incorporated these model-derived measures into the factor analysis with the human rating and performance measures. This combined analysis indicated that face recognizability is related to the distinctiveness of a face with respect to its gender subcategory prototype. Additionally, the gender classifiability of faces related to at least one caricatured aspect of face gender.

Principal Component and Neural Network Analyses of Face Images: What Can Be Generalized in Gender Classification?

We present an overview of the major findings of the principal component analysis (pca) approach to facial analysis. In a neural network or connectionist framework, this approach is known as the linear autoassociator approach. Faces are represented as a weighted sum of macrofeatures (eigenvectors or eigenfaces) extracted from a cross-product matrix of face images. Using gender categorization as an illustration, we analyze the robustness of this type of facial representation. We show that eigenvectors representing general categorical information can be estimated using a very small set of faces and that the information they convey is generalizable to new faces of the same population and to a lesser extent to new faces of a different population. Copyright 1997 Academic Press. Copyright 1997 Academic Press

More about the difference between men and women: evidence from linear neural networks and the principal-component approach

The ability of a statistical/neural network to classify faces by sex by means of a pixel-based representation has not been fully investigated. Simulations with pixel-based codes have provided sex-classification results that are less impressive than those reported for measurement-based codes. In no case, however, have the reported pixel-based simulations been optimized for the task of classifying faces by sex. A series of simulations is described in which four network models were applied to the same pixel-based face code. These simulations involved either a radial basis function network or a perceptron as a classifier, preceded or not by a preprocessing step of eigendecomposition. It is shown that performance comparable to that of the measurement-based models can be achieved with pixel-based input (90%) when the data are preprocessed. The effect of the eigendecomposition preprocessing of the faces is then compared with spatial-frequency analysis of face images and analyzed in terms of the perceptual information it captures. It is shown that such an examination may offer insight into the facial aspects important to the sex-classification process. Finally, the contribution of hair information to the performance of the model is evaluated. It is shown that, although the hair contributes to the sex-classification process, it is not the only important contributor.

Structural aspects of face recognition and the other-race effect

The other-race effect was examined in a series of experiments and simulations that looked at the relationships among observer ratings of typicality, familiarity, attractiveness, memorability, and the performance variables of d' and criterion. Experiment 1 replicated the other-race effect with our Caucasian and Japanese stimuli for both Caucasian and Asian observers. In Experiment 2, we collected ratings from Caucasian observers on the faces used in the recognition task. A Varimax-rotated principal components analysis on the rating and performance data for the Caucasian faces replicated Vokey and Read's (1992) finding that typicality is composed of two orthogonal components, dissociable via their independent relationships to: (1) attractiveness and familiarity ratings and (2) memorability ratings. For Japanese faces, however, we found that typicality was related only to memorability. Where performance measures were concerned, two additional principal components dominated by criterion and by d' emerged for Caucasian faces. For the Japanese faces, however, the performance measures of d' and criterion merged into a single component that represented a second component of typicality, one orthogonal to the memorability-dominated component. A measure of face representation quality extracted from an autoassociative neural network trained with a majority of Caucasian faces and a minority of Japanese faces was incorporated into the principal components analysis. For both Caucasian and Japanese faces, the neural network measure related both to memorability ratings and to human accuracy measures. Combined, the human data and simulation results indicate that the memorability component of typicality may be related to small, local, distinctive features, whereas the attractiveness/familiarity component may be more related to the global, shape-based properties of the face.