Notes on Hierarchies and Inductive Inference

The following notes rework a discussion due to Kevin Kelly on the application of topological notions in the context of learning (see Kelly (1990)). All the results except for (2), (4) and (9) are due to Kelly, but are proved differently

Relevant Consequence and Empirical Inquiry

A criterion of adequacy is proposed for theories of relevant consequence. According to the criterion, scientists whose deductive reasoning is limited to some proposed subset of the standard consequence relation must not thereby suffer a reduction in scientific competence. A simple theory of relevant consequence is introduced and shown to satisfy the criterion with respect to a formally defined paradigm of empirical inquiry

Synthesizing inductive expertise

AbstractWe consider programs that accept descriptions of inductive inference problems and return machines that solve them. Several design specifications for synthesizers of this kind are considered from a recursion-theoretic perspective

Uniform Inductive Improvement

We examine uniform procedures for improving the scientific competence of inductive inference machines. Formally, such procedures are construed as recursive operators. Several senses of improvement are considered, including (a) enlarging the class of functions on which success is certain, and (b) transforming probable success into certain success

A Universal Inductive Inference Machine

A paradigm of scientific discovery is defined within a first-order logical framework. It is shown that within this paradigm there exists a formal scientist that is Turing computable and universal in the sense that it solves every problem that any scientist can solve. It is also shown that universal scientists exist for no regular logics that extend first order logic and satisfy the Lowenheim-Skolem condition

Logic and Learning

The theory of first-order logic - or Model Theory - appears in few studies of learning and scientific discovery. We speculate about the reasons for this omission, and then argue for the utility of Model Theory in the analysis and design of automated systems of scientific discovery. One scientific task is treated from this perspective in detail, namely, concept discovery. Two formal paradigms bearing on this probleni are presented and investigated using the tools of logical theory. One paradigm bears on PAC learning, the other on identification in the limit

Automated Prediction of Preferences Using Facial Expressions

We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available

Accuracy of Inferring Self-and Other-Preferences from Spontaneous Facial Expressions

Abstract Participants' faces were covertly recorded while they rated the attractiveness of people, the decorative appeal of paintings, and the cuteness of animals. Ratings employed a continuous scale. The same participants then returned and tried to guess ratings from 3-s videotapes of themselves and other targets. Performance was above chance in all three stimulus categories, thereby replicating the results of an earlier study (North et al. in J Exp Soc Psychol 46(6):1109-1113, 2010) but this time using a more sensitive rating procedure. Across conditions, accuracy in reading one's own face was not reliably better than otheraccuracy. We discuss our findings in the context of ''simulation'' theories of face-based emotion recognition (Goldman in The philosophy, psychology, and neuroscience of mindreading. Oxford University Press, Oxford, 2006) and the larger body of accuracy research

Similarity, plausibility, and judgments of probability

Judging the strength of an argument may underlie many reasoning and decision-making tasks. In this article, we focus on "category-based" arguments, in which the premises and conclusion are of the form All members of C have property P, where C is a natural category. An example is "Dobermanns have sesamoid bones. Therefore, German shepherds have sesamoid bones." The strength of such an argument is reflected in the judged probability that the conclusion is true given that the premises are true. The processes that mediate such probability judgments depend on whether the predicate is "blank" - an unfamiliar property that does not enter the reasoning process (e.g., "have sesamoid bones") - or "non-blank" - a relatively familiar property that is easier to reason from (e.g., "can bite through wire"). With blank predicates, probability judgments are based on similarity relations between the premise and conclusion categories. With non-blank predicates, probability judgements are based on both similarity relations and the plausibility of premises and conclusion.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30527/1/0000159.pd

Extracting the coherent core of human probability judgement: a research program for cognitive psychology

Human intuition is a rich and useful guide to uncertain events in the environment but suffers from probabilistic incoherence in the technical sense. Developing methods for extracting a coherent body of judgement that is maximally consistent with a person's intuition is a challenging task for cognitive psychology, and also relevant to the construction of artificial expert systems. The present article motivates this problem, and outlines one approach to it.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31673/1/0000609.pd