The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature‐vector representation. During recognition, the model compares incoming preprocessed natural input to stored representations. By complementing the recognition memory process with a perceptual front end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We (...) demonstrate that the NIM model is able to simulate experimentally obtained similarity ratings and recognition memory for individual stimuli (i.e., face images). (shrink)

The use of computational models for understanding human face perception and recognition has a long and intriguing history that runs parallel to efforts in the engineering literature to develop algorithms for computer-based face recognition systems. This article considers the insights gained from combining computational and cognitive approaches to the study of human face recognition and discusses the ways in which computational models have informed studies of human face processing and vice versa. It explains the concept of a face space, in (...) its abstract psychological, and physical/computational forms. The study shows how adaptation as a method is beginning to reveal properties of neural representations in a way that connects with the cognitive/perceptual approach. Finally, it discusses recent progress in state-of-the-art computational models of face recognition, which offers a new perspective on the cognitive-computational dialog. (shrink)

To organise future behaviour, it is important to remember both the central and contextual aspects of a situation. We examined the impact of contextual threat or safety, learned through verbal instructions, on face identity recognition. In two studies (N = 140), 72 face–context compounds were presented each once within an encoding session, and an unexpected item/source recognition task was performed afterwards (including 24 new faces). Hierarchical multinomial processing tree modelling served to estimate individual parameters of item (face identity) and source (...) memory (threat or safety context) as well as guessing behaviour. Results show that language was highly effective in establishing threatening and safe context conditions. In Study 1, a fleeting picture stream (1 s per picture) led to poor item and source recognition. Prolonged presentation times (Study 2 with 6 s per picture) improved face memory but no contextual modulation was observed. Thus, incidental face learning was surprisingly poor and rapidly changing contextual settings might have interfered with the accurate encoding of face identity information and item–source binding. (shrink)

The present study used masked repetition priming to examine whether face representations can be accessed without attention. Two experiments using a face recognition task presented masked repetition and control primes in spatially unattended locations prior to target onset. Experiment 1 used the same images as primes and as targets and Experiment 2 used different images of the same individual as primes and targets. Repetition priming was observed across both experiments regardless of whether spatial attention was cued to the location of (...) the prime. Priming occurred for both famous and non-famous targets in Experiment 1 but was only reliable for famous targets in Experiment 2, suggesting that priming in Experiment 1 indexed access to view-specific representations whereas priming in Experiment 2 indexed access to view-invariant, abstract representations. Overall, the results indicate that subliminal access to abstract face representations does not rely on attention. (shrink)

Repetition priming of object identification refers to the phenomenon whereby experience with an object induces systematic changes in subsequent processing of that same object. This data-driven form of priming is distinct from conceptually-driven priming. To date, considerable controversy exists about whether data-driven priming reflects facilitation in perceptual processing or mediation by preexisting object representations. The present study concerned priming of recognizing familiar and unfamiliar faces and how this priming is influenced by face inversion, which interferes with perceptual face processing. Perceptual (...) and representation-based loci conjointly contributed to priming; the perceptual locus was operative similarly for familiar and unfamiliar faces, whereas the representation-based locus was only invoked for familiar faces and resulted in a response-time reduction triple the magnitude of that from the perceptual locus. The results constrain theoretical accounts of data-driven priming by indicating that improved identification can result from the combination of perceptual and representation-based facilitation. (shrink)

Research in face recognition has tended to focus on discriminating between individuals, or “telling people apart.” It has recently become clear that it is also necessary to understand how images of the same person can vary, or “telling people together.” Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos (...) of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic—the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework. (shrink)