Immediate and delayed effects of causal uncertainty inductions on uncertainty accessibility

Previous research has focused on enhanced processing as a response to causal uncertainty (CU), but relatively little empirical attention has been given to how CU is activated and the temporal unfolding of this activation. The current research investigates the counterintuitive idea that people inhibit causal uncertainty immediately after its activation. We find that this inhibition weakens over time. Study 1 demonstrates this inhibition effect with self-report uncertainty. Study 2 demonstrates this effect with an implicit accessibility measure. Temporary inhibition of uncertainty may be a general response when uncertainty is activated.     

Inferring latent class lexicographic rules from choice data

A lexicographic rule orders multi-attribute alternatives in the same way as a dictionary orders words. Although no utility function can represent lexicographic preference over continuous, real-valued attributes, a constrained linear model suffices for representing such preferences over discrete attributes. We present an algorithm for inferring lexicographic structures from choice data. The primary difficulty in using such data is that it is seldom possible to obtain sufficient information to estimate individual-level preference functions. Instead, one needs to pool the data across latent clusters of individuals. We propose a method that identifies latent clusters of subjects, and estimates a lexicographic rule for each cluster. We describe an application of the method using data collected by a manufacturer of television sets. We compare the predictions of the model with those obtained from a finite-mixture, multinomial-logit model.     

Categories and Dimensions

ABSTRACT— The distinction between categories and dimensions has important consequences for basic and applied science in many areas of psychological research. Decisions as to whether individuals should be assigned to groups or located along one or more continua often are based on personal preferences or discipline-specific measurement traditions, which can lead to the creation, use, or reification of spurious categories or dimensions. Methods for evaluating the latent structure of psychological constructs, using powerful and informative tests between competing models, are available. Rather than choosing on a priori grounds, investigators can perform structural research to evaluate the strength and consistency with which results tease apart categorical and dimensional models. Here, we review why researchers should make this distinction empirically, briefly discuss methods available for doing so, and describe the breadth of areas ripe for exploiting the largely untapped potential of structural research.     

Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds?

Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect averages over many individuals. On the conjecture that the accuracy of the group response is chiefly a consequence of aggregating across individuals, we constructed simple, heuristic approximations to the Bayesian model premised on the hypothesis that individuals have access merely to a sample of k instances drawn from the relevant distribution. The accuracy of the group response reported by Griffiths and Tenenbaum could be accounted for by supposing that individuals each utilize only two instances. Moreover, the variability of the group data is more consistent with this small-sample hypothesis than with the hypothesis that people utilize veridical or nearly veridical representations of the underlying prior distributions. Our analyses lead to a qualitatively different view of how individuals reason from past experience than the view espoused by Griffiths and Tenenbaum.     

Measuring model flexibility with parameter space partitioning: an introduction and application example

A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data-fitting potential (i.e., flexibility) of the model. In the first part of this article, methods for comparing models and studying their flexibility are reviewed, with a focus on parameter space partitioning (PSP), a general-purpose method for analyzing and comparing all classes of cognitive models. PSP is then demonstrated in the second part of the article in which two connectionist models of speech perception (TRACE and ARTphone) are compared to learn how design differences affect model flexibility.     

What 50 years of research tell us about pausing under ratio schedules of reinforcement

Textbooks in learning and behavior commonly describe performance on fixed-ratio schedules as “break and run,” indicating that after reinforcement subjects typically pause and then respond quickly to the next reinforcement. Performance on variable-ratio schedules, on the other hand, is described as steady and fast, with few long pauses. Beginning with Ferster and Skinner''s magnum opus, Schedules of Reinforcement (1957), the literature on pausing under ratio schedules has identified the influences on pausing of numerous important variables, in particular ratio size and reinforcement magnitude. As a result, some previously held assumptions have been called into question. For example, research has shown that the length of the pause is controlled not only by the preceding ratio, as Ferster and Skinner and others had assumed (and as implied by the phrase postreinforcement pause), but by the upcoming ratio as well. Similarly, despite the commonly held belief that ratio pausing is unique to the fixed-ratio schedule, there is evidence that pausing also occurs under variable-ratio schedules. If such widely held beliefs are incorrect, then what about other assumptions? This article selectively examines the literature on pausing under ratio schedules over the past 50 years and concludes that although there may indeed be some common patterns, there are also inconsistencies that await future resolution. Several accounts of pausing under ratio schedules are discussed along with the implications of the literature for human performances, most notably the behaviors termed procrastination.     

Personal values and relational models

In this study, the comprehensive value research by Schwartz (e.g. 1992) was linked to Fiske's relational models theory (RMT, e.g. Fiske, 1991). A sample of 297 people answered the personal values questionnaire (PVQ), the modes of relationship questionnaire (MORQ) and the relationship profile scale (RPS) in a web‐based online survey. As hypothesized, the set of 10 values correlated in a systematic manner—according to the circular structure of personal value systems—with both trait‐like construal of and motivational investment in the relational models communal sharing (CS), authority ranking (AR) and market pricing (MP). Further research concerning a person–environment value congruency approach to predict well‐being is suggested combining the two research traditions. Copyright © 2008 John Wiley & Sons, Ltd.     

A Note on Priest's Finite Inconsistent Arithmetics

We give a complete characterization of Priest's Finite Inconsistent Arithmetics observing that his original putative characterization included arithmetics which cannot in fact be realized.     

Some determinants of latent inhibition in human predictive learning

The present experiments assessed the effects of different manipulations between cue preexposure and cue-outcome pairings on latent inhibition (LI) in a predictive learning task with human participants. To facilitate LI, preexposure and acquisition with the target cues took place while participants performed a secondary task. Presentation of neither the target cues nor the target outcome was anticipated based on the instructions. Experiment 1 demonstrated the LI effect in the new experimental preparation. Experiment 2 analyzed the impact on LI of different activities that participants performed during the interval between preexposure and acquisition. Experiment 3 assessed LI as a function of changes in the secondary task cues made between preexposure and acquisition, namely presenting novel cues and reversing the cue-outcome contingencies. All of the manipulations in Experiments 2 and 3 resulted in a decrease in LI. The attenuation of LI by these manipulations challenges most current theories of learning and is best accommodated by Conditioned Attention Theory (Lubow, Weiner, & Schnur, 1981).     

Second-order conditioning of human causal learning

This article provides the first demonstration of a reliable second-order conditioning (SOC) effect in human causal learning tasks. It demonstrates the human ability to infer relationships between a cause and an effect that were never paired together during training. Experiments 1a and 1b showed a clear and reliable SOC effect, while Experiments 2a and 2b demonstrated that first-order extinction did not affect SOC. These results were similar to those found in animal and human conditioning and suggested that a similar associative mechanism could explain these effects. However, they can also be used to look into the underlying causal mental model people build and store while they are learning this task. From a cognitive view, overall results suggest that an independent rather than a chain causal mental model is stored after second-order learning in human causal tasks.