Bayesian generalization with circular consequential regions

Generalization–deciding whether to extend a property from one stimulus to another stimulus–is a fundamental problem faced by cognitive agents in many different settings. Shepard (1987) provided a mathematical analysis of generalization in terms of Bayesian inference over the regions of psychological space that might correspond to a given property. He proved that in the unidimensional case, where regions are intervals of the real line, generalization will be a negatively accelerated function of the distance between stimuli, such as an exponential function. These results have been extended to rectangular consequential regions in multiple dimensions, but not for circular consequential regions, which play an important role in explaining generalization for stimuli that are not represented in terms of separable dimensions. We analyze Bayesian generalization with circular consequential regions, providing bounds on the generalization function and proving that this function is negatively accelerated.     

Alcohol as social lubricant.

This study examines how consuming alcohol differentially affects the communicative behavior and perceptions of high and low social self‐esteem (SSE) women as they engage in a brief interaction with a flirtatious male. Alcohol myopia theory proposes that alcohol affects behavior when it blocks a person's normal inhibitions about enacting a behavior. It was predicted that low SSE women would be more inhibited when talking to a flirtatious male than would high SSE women and, therefore administration of a social self‐esteem measure and random assignment to an alcoholic or nonalcoholic beverage condition, participants (N=50) talked with an attractive, flirtatious male confederate. Low SSE women were less anxious and self‐disclosed more when drinking than when sober, whereas high SSE women were not significantly affected by alcohol consumption. The discussion highlights the complex and often contradictory effects of alcohol consumption on social interaction.     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

The effects of violent video games on aggression.

Violent content video games such as Mortal Kombat and Doom have become very popular among children and adolescents, causing great concern for parents, teachers, and policy makers. This study cumulates findings across existing empirical research on the effects of violent video games to estimate overall effect size and discern important trends and moderating variables. Results suggest there is a smaller effect of violent video games on aggression than has been found with television violence on aggression. This effect is positively associated with type of game violence and negatively related to time spent playing the games. Directions for future programmatic research on video games are outlined.     

A rational model of the effects of distributional information on feature learning

Most psychological theories treat the features of objects as being fixed and immediately available to observers. However, novel objects have an infinite array of properties that could potentially be encoded as features, raising the question of how people learn which features to use in representing those objects. We focus on the effects of distributional information on feature learning, considering how a rational agent should use statistical information about the properties of objects in identifying features. Inspired by previous behavioral results on human feature learning, we present an ideal observer model based on nonparametric Bayesian statistics. This model balances the idea that objects have potentially infinitely many features with the goal of using a relatively small number of features to represent any finite set of objects. We then explore the predictions of this ideal observer model. In particular, we investigate whether people are sensitive to how parts co-vary over objects they observe. In a series of four behavioral experiments (three using visual stimuli, one using conceptual stimuli), we demonstrate that people infer different features to represent the same four objects depending on the distribution of parts over the objects they observe. Additionally in all four experiments, the features people infer have consequences for how they generalize properties to novel objects. We also show that simple models that use the raw sensory data as inputs and standard dimensionality reduction techniques (principal component analysis and independent component analysis) are insufficient to explain our results.     

Vertical position as a cue to pictorial depth: Height in the picture plane versus distance to the horizon

Two often cited but frequently confused pictorial cues to perceived depth are height in the picture plane (HPP) and distance to the horizon (DH). We report two psychophysical experiments that disentangled their influence on perception of relative depth in pictures of the interior of a schematic room. Experiment 1 showed that when HPP and DH varied independently with both a ceiling and a floor plane visible in the picture, DH alone determined judgments of relative depth; HPP was irrelevant. Experiment 2 studied relative depth perception in single-plane displays (floor only or ceiling only) in which the horizon either was not visible or was always at the midpoint of the target object. When the target object was viewed against either a floor or a ceiling plane, some observers used DH, but others (erroneously) used HPP. In general, when DH is defined and unambiguous, observers use it to determine the relative distance to objects, but when DH is undefined and/or ambiguous, at least some observers use HPP.     

Seeking Confirmation Is Rational for Deterministic Hypotheses

The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best‐known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. This analysis rests on two assumptions: (a) that people predict the next event in a sequence in a way that is consistent with Bayesian inference; and (b) when testing hypotheses, people test the hypothesis to which they assign highest posterior probability. We present four behavioral experiments that support these assumptions, showing that a simple Bayesian model can capture people's predictions about numerical sequences (Experiments 1 and 2), and that we can alter the hypotheses that people choose to test by manipulating the prior probability of those hypotheses (Experiments 3 and 4).