The influence of cognitive ability and instructional set on causal conditional inference

We report a large study in which participants are invited to draw inferences from causal conditional sentences with varying degrees of believability. General intelligence was measured, and participants were split into groups of high and low ability. Under strict deductive-reasoning instructions, it was observed that higher ability participants were significantly less influenced by prior belief than were those of lower ability. This effect disappeared, however, when pragmatic reasoning instructions were employed in a separate group. These findings are in accord with dual-process theories of reasoning. We also took detailed measures of beliefs in the conditional sentences used for the reasoning tasks. Statistical modelling showed that it is not belief in the conditional statement per se that is the causal factor, but rather correlates of it. Two different models of belief-based reasoning were found to fit the data according to the kind of instructions and the type of inference under consideration.     

Subjectivity and unconsciousness

Concepts of the unconscious were crucial to both Jung's and Freud's thinking. Psychoanalytic and analytical psychological views of the unconscious are compared and contrasted, and both are critically reviewed. It is suggested that we need to revise our conceptualization so as to take better account of the role of the analyst's expectations and inferences, and therefore of his or her subjectivity, whenever he or she makes a clinical judgement that unconscious mental processes are in operation. Some technical implications of a revised definition of unconsciousness are considered, especially indications for self-disclosure by an analyst of his or her own experience of events within the treatment.     

On aims and methods in the neuroimaging of derived relations

Ingenious and seemingly powerful technologies have been developed recently that enable the visualization in some detail of events in the brain concomitant upon the ongoing behavioral performance of a human participant. Measurement of such brain events offers at the very least a new set of dependent variables in relation to which the independent variables familiarly manipulated in the operant laboratory may be explored. Two related paradigms in which a start has been made in such research concern the derivation of novel or emergent relations from a baseline set of trained relations, and include the phenomenon of transitive inference (TI), observed in studies of stimulus equivalence (SE) and serial learning (SL) or seriation. This paper reviews some published and forthcoming neuroimaging studies of these and related phenomena, and considers how this line of research both demands and represents a welcome synthesis between types of question and levels of explanation in behavioral science that often have been seen as antithetical.     

Where do "constraints" come from? A comparison between inferences in biology and in word learning

In this paper, as a commentary on the papers in this special issue, the nature of constraints is discussed in terms of the comparison between inferences in biology and in word learning. Young children's inferences in biology could be constrained by three sources of information: factual knowledge, expectations based on a theory, and ontological knowledge. For example, young children's inference about the length of noses could be constrained by the facts the children know about the animals, the similarity between the animals and humans, and the ontological distinction between living things and nonliving things. In the same way, young children might figure out word meanings by linguistic and pragmatic knowledge, expectations of word meanings (e.g., the whole-object assumption), and ontological knowledge. Interactions among these sources of knowledge are documented by the papers in this special issue and related studies. It is argued that learning biases such as the whole-object assumption could not be induced only by linguistic and pragmatic cues in a social context, but should be regarded as a product of the interaction between a social context and children's theories.     

Principles for Designing Randomized Preventive Trials in Mental Health: An Emerging Developmental Epidemiology Paradigm

An emerging population-based paradigm is now being used to guide the design of preventive trials used to test developmental models. We discuss elements of the designs of several ongoing randomized preventive trials involving reduction of risk for children of divorce, for children who exhibit behavioral or learning problems, and for children whose parents are being treated for depression. To test developmental models using this paradigm, we introduce three classes of design issues: design for prerandomization, design for intervention, and design for postintervention. For each of these areas, we present quantitative results from power calculations. Both scientific and cost implications of these power calculations are discussed in terms of variation among subjects on preintervention measures, unit of intervention, assignment, balancing, number of pretest and posttest measures, and the examination of moderation effects.     

Binary action based estimation of propensities

An observer is to make inference statements about a quantityp, called apropensity and bounded between 0 and 1, based on the observation thatp does or does not exceed a constantc. The propensityp may have an interpretation as a proportion, as a long-run relative frequency, or as a personal probability held by some subject. Applications in medicine, engineering, political science, and, most especially, human decision making are indicated. Bayes solutions for the observer are obtained based on prior distributions in the mixture of beta distribution family; these are then specialized to power-function prior distributions. Inference about logp and log odds is considered. Multiple-action problems are considered in which the focus of inference shifts to theprocess generating the propensitiesp, both in the case of a process parameter known to the subject and unknown. Empirical Bayes techniques are developed for observer inference aboutc when is known to the subject. A Bayes rule, a minimax rule and a beta-minimax rule are constructed for the subject when he is uncertain about.This research was partially supported by the Defense Advanced Research Projects Agency of the Department of Defense and was monitored by ONR under Contract No. N00014-77-C-0095. Any opinions, findings, conclusions or recommendations expressed herein are those of the author and do not necessarily reflect the views of the Defense Advanced Research Projects Agency, the Office of Naval Research, or Carnegie-Mellon University.     

Clinical judgment in forensic neuropsychology: A comment on the risks of claiming more than can be delivered

There is a vast and growing literature in psychology demonstrating the general limits of human judgment and clinical inference. These findings clearly apply in the new specialty of clinical neuropsychology, and there is little empirical research to support the widespread belief that judgmental accuracy correlates substantially with experience, professional stature, or reputation as a neuropsychologist. However, the demand characteristics of expert testimony in the forensic arena may encourage individual neuropsychologists to state or intimate that they have unique or special expertise in understanding brain-behavior relationships, or in predicting outcomes following cerebral insult or injury. These claims will be increasingly difficult to substantiate as attorneys become more conversant with the literature on human judgment.     

Bayesian inference for finite mixtures of generalized linear models with random effects

We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However, class membership is not known a priori, so the heterogeneity in the regression coefficients becomes a finite mixture of normal distributions. This approach combines the flexibility of semiparametric, latent class models that assume common parameters for each sub-population and the parsimony of random effects models that assume normal distributions for the regression parameters. The number of subpopulations is selected to maximize the posterior probability of the model being true. Simulations are presented which document the performance of the methodology for synthetic data with known heterogeneity and number of sub-populations. An application is presented concerning preferences for various aspects of personal computers.