Implicit learning of geometric eigenfaces

The Episodic Prototypes Model (EPM): On the nature and genesis of facial representations

Faces undergo massive changes over time and life events. We need a mental representation which is flexible enough to cope with the existing visual varieties, but which is also stable enough to be the basis for valid recognition. Two main theoretical frameworks exist to describe facial representations: prototype models assuming one central item comprising all visual experiences of a face, and exemplar models assuming single representations of each visual experience of a face. We introduce a much more ecological valid model dealing with episodic prototypes (the Episodic Prototypes Model—EPM), where faces are represented by a low number of prototypes that refer to specific Episodes of Life (EoL, e.g., early adulthood, mature age) during which the facial appearance shows only moderate variation. Such an episodic view of mental representation allows for efficient storage, as the number of needed prototypes is relatively low, and it allows for the needed variation within a prototype that keeps the everyday and steadily ongoing changes across a certain period of time. Studies 1–3 provide evidence that facial representations are highly dependent on temporal aspects which is in accord with EoL, and that individual learning history generates the structure and content of respective prototypes. In Study 4, we used implicit measures (RT) in a face verification task to investigate the postulated power of the EPM. We could demonstrate that episodic prototypes clearly outperformed visual depictions of exhaustive prototypes, supporting the general idea of our approach.

Making Expert Decisions Easier to Fathom: On the Explainability of Visual Object Recognition Expertise

In everyday life, we rely on human experts to make a variety of complex decisions, such as medical diagnoses. These decisions are typically made through some form of weakly guided learning, a form of learning in which decision expertise is gained through labeled examples rather than explicit instructions. Expert decisions can significantly affect people other than the decision-maker (for example, teammates, clients, or patients), but may seem cryptic and mysterious to them. It is therefore desirable for the decision-maker to explain the rationale behind these decisions to others. This, however, can be difficult to do. Often, the expert has a “gut feeling” for what the correct decision is, but may have difficulty giving an objective set of criteria for arriving at it. Explainability of human expert decisions, i.e., the extent to which experts can make their decisions understandable to others, has not been studied systematically. Here, we characterize the explainability of human decision-making, using binary categorical decisions about visual objects as an illustrative example. We trained a group of “expert” subjects to categorize novel, naturalistic 3-D objects called “digital embryos” into one of two hitherto unknown categories, using a weakly guided learning paradigm. We then asked the expert subjects to provide a written explanation for each binary decision they made. These experiments generated several intriguing findings. First, the expert’s explanations modestly improve the categorization performance of naïve users (paired t-tests, p < 0.05). Second, this improvement differed significantly between explanations. In particular, explanations that pointed to a spatially localized region of the object improved the user’s performance much better than explanations that referred to global features. Third, neither experts nor naïve subjects were able to reliably predict the degree of improvement for a given explanation. Finally, significant bias effects were observed, where naïve subjects rated an explanation significantly higher when told it comes from an expert user, compared to the rating of the same explanation when told it comes from another non-expert, suggesting a variant of the Asch conformity effect. Together, our results characterize, for the first time, the various issues, both methodological and conceptual, underlying the explainability of human decisions.

The Influence of Averageness on Adults' Perceptions of Attractiveness: The Effect of Early Visual Deprivation

Adults who missed early visual input because of congenital cataracts later have deficits in many aspects of face processing. Here we investigated whether they make normal judgments of facial attractiveness. In particular, we studied whether their perceptions are affected normally by a face's proximity to the population mean, as is true of typically developing adults, who find average faces to be more attractive than most other faces. We compared the judgments of facial attractiveness of 12 cataract-reversal patients to norms established from 36 adults with normal vision. Participants viewed pairs of adult male and adult female faces that had been transformed 50% toward and 50% away from their respective group averages, and selected which face was more attractive. Averageness influenced patients' judgments of attractiveness, but to a lesser extent than controls. The results suggest that cataract-reversal patients are able to develop a system for representing faces with a privileged position for an average face, consistent with evidence from identity aftereffects. However, early visual experience is necessary to set up the neural architecture necessary for averageness to influence perceptions of attractiveness with its normal potency.

Neuronal codes for visual perception and memory

In this review, I describe and contrast the representation of stimuli in visual cortical areas and in the medial temporal lobe (MTL). While cortex is characterized by a distributed and implicit coding that is optimal for recognition and storage of semantic information, the MTL shows a much sparser and explicit coding of specific concepts that is ideal for episodic memory. I will describe the main characteristics of the coding in the MTL by the so-called concept cells and will then propose a model of the formation and recall of episodic memory based on partially overlapping assemblies.

The PCA learning effect: An emerging correlate of face memory during childhood

Human adults implicitly learn the prototype and the principal components of the variability distinguishing faces (Gao & Wilson, 2014). Here we measured the implicit learning effect in adults and 9-year-olds, and with a modified child-friendly procedure, in 7-year-olds. All age groups showed the implicit learning effect by falsely recognizing the average (the prototype effect) and the principal component faces as having been seen (the PCA learning effect). The PCA learning effect, but not the prototype effect increased between 9years of age and adulthood and at both ages was the better predictor of memory for the actually studied faces. In contrast, for the 7-year-olds, the better predictor of face memory was the prototype effect. The pattern suggests that there may be a developmental change between ages 7 and 9 in the mechanism underlying memory for faces. We provide the first evidence that children as young as age 7 can extract the most important dimensions of variation represented by principal components among individual faces, a key ability that grows stronger with age and comes to underlie memory for faces.