Taking a closer look: On the operation of nonconscious impression formation

In this article, we analyzed the information processing that underlies nonconscious impression formation. In the first experiment, the nonconscious activation of the impression formation goal led to a faster analysis of the trait implications of behaviors, compared with a control group. In Experiment 2, participants who were nonconsciously primed with an impression formation goal were more likely than those in a control condition to form associations in memory between behaviors and implied traits. In Experiment 3, nonconsciously primed participants were more sensitive than those in a control condition to whether inconsistent trait information was relevant or irrelevant to the actor’s disposition. Moreover, in Experiments 2 and 3, those with a nonconscious goal showed just as much evidence of impression formation as those who were consciously and intentionally trying to form an impression. Implications for nonconscious goal-pursuit and impression formation are discussed.     

A user’s guide to the evolutionary argument against naturalism

Alvin Plantinga has famously argued that metaphysical naturalism is self-defeating, and cannot be rationally accepted. I distinguish between two different ways of understanding this argument, which I call the "probabilistic inference conception", and the "process characteristic conception". I argue that the former is what critics of the argument usually presuppose, whereas most critical responses fail when one assumes the latter conception. To illustrate this, I examine three standard objections to Plantinga's evolutionary argument against naturalism: the Perspiration Objection, the Tu Quoque Objection, and the "Why Can't the Naturalist Just Add a Little Something?" Objection. I show that Plantinga's own responses to these objections fail, and propose counterexamples to his first two principles of defeat. I then go on to construct more adequate responses to these objections, using the distinctions I develop in the first part of the paper.     

Epistemic instrumentalism,exceeding our grasp

In the concluding chapter of Exceeding our Grasp Kyle Stanford outlines a positive response to the central issue raised brilliantly by his book, the problem of unconceived alternatives. This response, called "epistemic instrumentalism", relies on a distinction between instrumental and literal belief. We examine this distinction and with it the viability of Stanford's instrumentalism, which may well be another case of exceeding our grasp.     

Model structure adequacy analysis: selecting models on the basis of their ability to answer scientific questions

Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world.     

The error statistical philosopher as normative naturalist

We argue for a naturalistic account for appraising scientific methods that carries non-trivial normative force. We develop our approach by comparison with Laudan’s (American Philosophical Quarterly 24:19–31, 1987, Philosophy of Science 57:20–33, 1990) “normative naturalism” based on correlating means (various scientific methods) with ends (e.g., reliability). We argue that such a meta-methodology based on means–ends correlations is unreliable and cannot achieve its normative goals. We suggest another approach for meta-methodology based on a conglomeration of tools and strategies (from statistical modeling, experimental design, and related fields) that affords forward looking procedures for learning from error and for controlling error. The resulting “error statistical” appraisal is empirical—methods are appraised by examining their capacities to control error. At the same time, this account is normative, in that the strategies that pass muster are claims about how actually to proceed in given contexts to reach reliable inferences from limited data.     

Abnormality, cognitive virtues, and knowledge

Causal analyses of one’s knowing that p have recently emphasized the involvement of cognitive virtues in coming to believe that p. John Greco suggests that in order to deal with Gettier-type cases, a virtue analysis of knowing should include a requirement that one’s knowing does not in a certain way involve abnormality. Yet Greco’s emphasis on statistical abnormality either renders his analysis subject to a generality problem or to objections regarding certain Gettier-type cases. When we instead consider abnormality in the sense of a causally differentiating factor in relation to a causal contrast situation, the account remains unclear concerning its application to an interesting non-Gettier-type case concerning chance. The exploration of these shortcomings casts doubt on the epistemological usefulness of the schema, ‘If you know, then there is no abnormality in your being right.’     

The Causal Chain Problem

This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be argued that even though the chain and its associated common cause model are empirically equivalent there exists an important asymmetry between the two models with respect to model expansions. This asymmetry might constitute a basis on which to disambiguate corresponding causal inferences on non-empirical grounds.

Using category structures to test iterated learning as a method for identifying inductive biases

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this "iterated learning" procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners.     

Bewitchment, biology, or both: the co-existence of natural and supernatural explanatory frameworks across development

Three studies examined the co-existence of natural and supernatural explanations for illness and disease transmission, from a developmental perspective. The participants (5-, 7-, 11-, and 15-year-olds and adults; N = 366) were drawn from 2 Sesotho-speaking South African communities, where Western biomedical and traditional healing frameworks were both available. Results indicated that, although biological explanations for illness were endorsed at high levels, witchcraft was also often endorsed. More important, bewitchment explanations were neither the result of ignorance nor replaced by biological explanations. Instead, both natural and supernatural explanations were used to explain the same phenomena, and bewitchment explanations were highest among adults. Taken together, these data provide insight into how diverse, culturally constructed belief systems about illness co-exist across development.     

Insight without cortex: lessons from the avian brain

Insight is a cognitive feature that is usually regarded as being generated by the neocortex and being present only in humans and possibly some closely related primates. In this essay we show that especially corvids display behavioral skills within the domains of object permanence, episodic memory, theory of mind, and tool use/causal reasoning that are insightful. These similarities between humans and corvids at the behavioral level are probably the result of a convergent evolution. Similarly, the telencephalic structures involved in higher cognitive functions in both species show a high degree of similarity, although the forebrain of birds has no cortex-like lamination. The neural substrate for insight-related cognitive functions in mammals and birds is thus not necessarily based on a laminated cortical structure but can be generated by differently organized forebrains. Hence, neither is insight restricted to mammals, as predicted from a “scala naturae”, nor is the laminated cortex a prerequisite for the highest cognitive functions.