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.     

Gaze following among toddlers

Children's cooperative activities with their peers become markedly coordinated during the 3rd year of life. During these activities, the child needs to follow his/her peers’ gaze to objects, and look at the same objects to initiate coordinated action. Since 3-year-olds’ ability to follow their peers’ gaze has not been studied, we experimentally investigated this in our study. In the experimental trials, an experimenter induced a child (looker) to look at a doll on display, and observed the reaction of another child (follower) who was in front of the looker (and not looking at the doll). In the control trials, the experimenter displayed the doll in an identical manner when the follower was alone. The followers followed the gaze of the lookers, looking at the doll in approximately 90% of the experimental trials, compared with 20% of the control trials. These results indicate that 3-year-olds can follow their peers’ gaze.     

中国城区青少年对广告价值的评价:前因和后果

通过对北京市7所中学366名学生的调查,探讨了青少年对广告价值的评价以及何种因素影响青少年对广告价值的评价,广告价值的评价是否会进一步影响其决策等问题。运用AMOS 4.0建立结构方程模型对研究假设进行检验,结果表明：（1）青少年对广告价值的评价直接影响到他们的决策意图;（2）在探讨广告价值的影响因素时发现,对广告的知觉因素（信息性和娱乐性）和青少年对广告以及广告商的社会认知（观看广告的社会动机和对广告劝服技巧的认识）对广告价值的影响作用也十分显著,信息性、娱乐性与社会动机都对广告价值产生积极影响,而对广告劝服技巧的认识则对广告价值产生显著的负面影响     

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.     

Young children's psychological explanations and their relationship to perception‐ and intention‐understanding

The present study examined two key aspects of young children's ability to explain human behaviour in a mentalistic way. First, we explored desires that are of a level of difficulty comparable with that of false beliefs. For this purpose, the so‐called ‘alternative desires’ were created. Second, we examined how children's psychological explanations are related to their understanding of perception and intention. A perception‐understanding task, an intention‐understanding task and a psychological‐explanation task were administered to 80 three‐year‐olds. Results offer support for the thesis that the level of difficulty of belief and desire explanations is comparable. Moreover, children's psychological explanations are related to their understanding of perception and intention. The results lend support to the idea that mentalistic explanations are an explicit manifestation of children's level of theory of mind. Copyright © 2008 John Wiley & Sons, Ltd.     

Action understanding as inverse planning

Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents’ behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent’s behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an “intentional stance” [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a “teleological stance” [Gergely, G., Nádasdy, Z., Csibra, G., & Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165-193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.