Robert M. French

High-level perception, representation, and analogy: a critique of artificial intelligence methodology

Abstract High-level perception—the process of making sense of complex data at an abstract, conceptual level—is fundamental to human cognition. Through high-level perception, chaotic environmental stimuli are organized into mental representations that are used throughout cognitive processing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dismissal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models—notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought—-and argue that these are flawed precisely because they downplay the role of high-level perception. Further, we argue that perceptual processes cannot be separated from other cognitive processes even in principle,and therefore that traditional artificial-intelligence models cannot be defended by supp...

The Role of Bottom-Up Processing in Perceptual Categorization by 3- to 4-Month-Old Infants: Simulations and Data

Disentangling bottom-up and top-down processing in adult category learning is notoriously difficult. Studying category learning in infancy provides a simple way of exploring category learning while minimizing the contribution of top-down information. Three- to 4-month-old infants presented with cat or dog images will form a perceptual category representation for cat that excludes dogs and for dog that includes cats. The authors argue that an inclusion relationship in the distribution of features in the images explains the asymmetry. Using computational modeling and behavioral testing, the authors show that the asymmetry can be reversed or removed by using stimulus images that reverse or remove the inclusion relationship. The findings suggest that categorization of nonhuman animal images by young infants is essentially a bottom-up process.

A connectionist account of asymmetric category learning in early infancy.

Young infants show unexplained asymmetries in the exclusivity of categories formed on the basis of visually presented stimuli. A connectionist model is described that shows similar exclusivity asymmetries when categorizing the same stimuli presented to infants. The asymmetries can be explained in terms of an associative learning mechanism, distributed internal representations, and the statistics of the feature distributions in the stimuli. The model was used to explore the robustness of this asymmetry. The model predicts that the asymmetry will persist when a category is acquired in the presence of mixed category exemplars. An experiment with 3-4-month-olds showed that asymmetric exclusivity persisted in the presence of mixed-exemplar familiarization, thereby confirming the models prediction.

Understanding bilingual memory: models and data

Bilingual memory research in the past decade and, particularly, in the past five years, has developed a range of sophisticated experimental, neuropsychological and computational techniques that have allowed researchers to begin to answer some of the major long-standing questions of the field. We explore bilingual memory along the lines of the conceptual division of language knowledge and organization, on the one hand, and the mechanisms that operate on that knowledge and organization, on the other. Various interactive-activation and connectionist models of bilingual memory that attempt to incorporate both organizational and operational considerations will serve to bridge these two divisions. Much progress has been made in recent years in bilingual memory research, which also serves to illuminate general (language-independent) memory processes.

The Turing Test: the first 50 years

The Turing Test, originally proposed as a simple operational definition of intelligence, has now been with us for exactly half a century. It is safe to say that no other single article in computer science, and few other articles in science in general, have generated so much discussion. The present article chronicles the comments and controversy surrounding Turings classic article from its publication to the present. The changing perception of the Turing Test over the last 50 years has paralleled the changing attitudes in the scientific community towards artificial intelligence: from the unbridled optimism of 1960s to the current realization of the immense difficulties that still lie ahead. I conclude with the prediction that the Turing Test will remain important, not only as a landmark in the history of the development of intelligent machines, but also with real relevance to future generations of people living in a world in which the cognitive capacities of machines will be vastly greater than they are now.

Semi-distributed representations and catastrophic forgetting in connectionist networks

Abstract A major problem with connectionist networks is that newly-learned information may completely destroy previously-learned information unless the network is continually retrained on the old information. This phenomenon, known as catastrophic forgetting, is unacceptable both for practical purposes and as a model of mind. This paper advances the claim that catastrophic forgetting is in part the result of the overlap of systems distributed representations and can be reduced by reducing this overlap. A simple algorithm, called activation sharpening, is presented that allows a standard feed-forward backpropagation network to develop semi-distributed representations, thereby reducing the problem of catastrophic forgetting. Activation sharpening is discussed in tight of recent work done by other researchers who have experimented with this and other techniques for reducing catastrophic forgetting.

A Recurrent Connectionist Model of Group Biases

Major biases and stereotypes in group judgments are reviewed and modeled from a recurrent connectionist perspective. These biases are in the areas of group impression formation (illusory correlation), group differentiation (accentuation), stereotype change (dispersed vs. concentrated distribution of inconsistent information), and group homogeneity. All these phenomena are illustrated with well-known experiments, and simulated with an autoassociative network architecture with linear activation update and delta learning algorithm for adjusting the connection weights. All the biases were successfully reproduced in the simulations. The discussion centers on how the particular simulation specifications compare with other models of group biases and how they may be used to develop novel hypotheses for testing the connectionist modeling approach and, more generally, for improving theorizing in the field of social biases and stereotype change.

Do Babies Resemble Their Fathers More Than Their Mothers? A Failure to Replicate Christenfeld and Hill (1995)

Abstract Contrary to Christenfeld and Hill (1995) , we find that children aged 1, 3, and 5 do not appear to resemble their fathers significantly more than their mothers. We provide an explanation as to why this should be. In addition, we note that any father–child resemblance that does exist, although better than chance, is far from overwhelming.

Mechanisms of Categorization in Infancy

This article presents a connectionist model of correlation-based categorization by 10-month-old infants (Younger, 1985). Simple autoencoder networks were exposed to the same stimuli used to test 10-month-olds. The familiarization regime was kept as close as possible to that used with the infants. The performance of the model matched that of the infants. Both infants and networks used covariation information (when available) to segregate items into separate categories. The model provides a mechanistic account of category learning within a test session. It demonstrates how categorization arises as the product of an inextricable interaction between the subject (the infant) and the environment (the stimuli). The computational characteristics of both subject and environment must be considered in conjunction to understand the observed behaviors.

The language of emotion in short blog texts

Emotion is central to human interactions, and automatic detection could enhance our experience with technologies. We investigate the linguistic expression of fine-grained emotion in 50 and 200 word samples of real blog texts previously coded by expert and naive raters. Content analysis (LIWC) reveals angry authors use more affective language and negative affect words, and that joyful authors use more positive affect words. Additionally, a co-occurrence semantic space approach (LSA) was able to identify fear (which naive human emotion raters could not do). We relate our findings to human emotion perception and note potential computational applications.