Learning a theory of causality

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.     

Bayesian data analysis for newcomers

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.     

Understanding Conspiracy Theories

Scholarly efforts to understand conspiracy theories have grown significantly in recent years, and there is now a broad and interdisciplinary literature. In reviewing this body of work, we ask three specific questions. First, what factors are associated with conspiracy beliefs? Our review of the literature shows that conspiracy beliefs result from a range of psychological, political, and social factors. Next, how are conspiracy theories communicated? Here, we explain how conspiracy theories are shared among individuals and spread through traditional and social media platforms. Next, what are the societal risks and rewards associated with conspiracy theories? By focusing on politics and science, we argue that conspiracy theories do more harm than good. We conclude by suggesting several promising avenues for future research.     

Bayesian just-so stories in psychology and neuroscience

According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak. This weakness relates to the many arbitrary ways that priors, likelihoods, and utility functions can be altered in order to account for the data that are obtained, making the models unfalsifiable. It further relates to the fact that Bayesian theories are rarely better at predicting data compared with alternative (and simpler) non-Bayesian theories. Second, we show that the empirical evidence for Bayesian theories in neuroscience is weaker still. There are impressive mathematical analyses showing how populations of neurons could compute in a Bayesian manner but little or no evidence that they do. Third, we challenge the general scientific approach that characterizes Bayesian theorizing in cognitive science. A common premise is that theories in psychology should largely be constrained by a rational analysis of what the mind ought to do. We question this claim and argue that many of the important constraints come from biological, evolutionary, and processing (algorithmic) considerations that have no adaptive relevance to the problem per se. In our view, these factors have contributed to the development of many Bayesian "just so" stories in psychology and neuroscience; that is, mathematical analyses of cognition that can be used to explain almost any behavior as optimal.     

Learning Facts About Migration: Politically Motivated Learning of Polarizing Information About Refugees

Information processing during heated debates on asylum and immigration may often be influenced by prejudice rather than a desire to learn facts. In this article, we investigate how people process empirical evidence on the consequences of refugee arrivals through a novel survey experiment that disentangles politically motivated learning from other forms of learning and expressive responding. Specifically, we ask respondents to interpret a 2×2 table about the relationship between asylum seekers and crime rates. Crucially, respondents are randomly allocated to evaluate a conclusion that triggers their identity-protective stakes or not. In addition, we test for motivated responding as an alternative explanation by randomly providing some respondents with a response format that motivates them to report their inference truthfully. We find that information processing changes substantially when new information challenges existing asylum attitudes. Politically motivated learning is strongest among voters with strong negative prior attitudes towards asylum seekers. Our results also indicate that expressive responding can only partially account for this gap in correctly reported inferences. Our research has important implications for research on the consequences of refugee migration, theories of motivated reasoning, and survey methodology.     

How to Understand the Completion of Art

There are a number of recent discussions on the question of when an artwork is complete. While it has been observed that a work might be complete in one way and not in another, the impact of this observation has been minimal. Discussion has been continued as if there is only one real sense of completion that matters. I argue that this is a mistake. Even if there were only one (or one most important) kind of completion, extant theories of completion would be bad candidates for that one kind. The best explanation of the failure of extant theories is that there are many kinds of completion, many corresponding senses of “completion,” and no kind of artwork completion is objectively more important than any other. We have a good reason to think that this is the case given the disparate interests we have when we ask completion questions. Once we have realized that those concerns track properties that are often unrelated, the question for theorists to answer becomes, “In how many ways can an artwork be complete?”     

A Model of Knower-Level Behavior in Number Concept Development

Abstract We develop and evaluate a model of behavior on the Give- N task, a commonly used measure of young children's number knowledge. Our model uses the knower-level theory of how children represent numbers. To produce behavior on the Give- N task, the model assumes that children start out with a base rate that makes some answers more likely a priori than others but is updated on each experimental trial in a way that depends on the interaction between the experimenter's request and the child's knower level. We formalize this process as a generative graphical model, so that the parameters—including the base rate distribution and each child's knower level—can be inferred from data using Bayesian methods. Using this approach, we evaluate the model on previously published data from 82 children spanning the whole developmental range. The model provides an excellent fit to these data, and the inferences about the base rate and knower levels are interpretable and insightful. We discuss how our modeling approach can be extended to other developmental tasks and can be used to help evaluate alternative theories of number representation against the knower-level theory.     

Implicit attitudes and the social capacity for free will

In this paper I ask what implicit attitudes tell us about our freedom. I analyze the relation between the literature on implicit attitudes and an important subcategory of theories of free will—self-disclosure accounts. If one is committed to such a theory, I suggest one may have to move to a more social conceptualization of the capacity for freedom. I will work out this argument in five sections. In the first section, I discuss the specific theories of free will that are central to this paper. In the second section, I will show that implicit-bias research raises questions about people’s capacities to exercise (these specific understandings of) free will. In the third section, I will consider how an individual may overcome these failures and argue that the individual ability for self-regulation is significantly limited. One could stop here and conclude that free will is a limited capacity. But I argue that this conclusion would be too hastily drawn. I will instead continue to ask what would be required for free will. By discussing how failures of free will are due to social structures and may be therefore repaired by changing social structures in section 4, I will arrive at an alternative conclusion about the capacity for free will in section 5.     

Bayesian theories of conditioning in a changing world

The recent flowering of Bayesian approaches invites the re-examination of classic issues in behavior, even in areas as venerable as Pavlovian conditioning. A statistical account can offer a new, principled interpretation of behavior, and previous experiments and theories can inform many unexplored aspects of the Bayesian enterprise. Here we consider one such issue: the finding that surprising events provoke animals to learn faster. We suggest that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning. We discuss inference in a world that changes and show how experimental results involving surprise can be interpreted from this perspective, and also how, thus understood, these phenomena help constrain statistical theories of animal and human learning.     

Directed Convergence in Stable Percept Acquisition

We view a perceptual capacity as a nondeductive inference, represented as a function from a set of premises to a set of conclusions. The application of the function to a single premise to produce a single conclusion is called a "percept" or "instantaneous percept." We define a stable percept as a convergent sequence of instantaneous percepts. Assuming that the sets of premises and conclusions are metric spaces, we introduce a strategy for acquiring stable percepts, called directed convergence. We consider probabilistic inferences, where the premise and conclusion sets are spaces of probability measures, and in this context we study Bayesian probabilistic/recursive inference. In this type of Bayesian inference the premises are probability measures, and the prior as well as the posterior is updated nontrivially at each iteration. This type of Bayesian inference is distinguished from classical Bayesian statistical inference where the prior remains fixed, and the posterior evolves by conditioning on successively more punctual premises. We indicate how the directed convergence procedure may be implemented in the context of Bayesian probabilistic/recursive inference. We discuss how the L(infinity) metric can be used to give numerical control of this type of Bayesian directed convergence. Copyright 2001 Academic Press.     

Assessing theories,Bayes style

The problem addressed in this paper is “the main epistemic problem concerning science”, viz. “the explication of how we compare and evaluate theories [...] in the light of the available evidence” (van Fraassen, BC, 1983, Theory comparison and relevant Evidence. In J. Earman (Ed.), Testing scientific theories (pp. 27–42). Minneapolis: University of Minnesota Press). Sections 1– 3 contain the general plausibility-informativeness theory of theory assessment. In a nutshell, the message is (1) that there are two values a theory should exhibit: truth and informativeness—measured respectively by a truth indicator and a strength indicator; (2) that these two values are conflicting in the sense that the former is a decreasing and the latter an increasing function of the logical strength of the theory to be assessed; and (3) that in assessing a given theory by the available data one should weigh between these two conflicting aspects in such a way that any surplus in informativeness succeeds, if the shortfall in plausibility is small enough. Particular accounts of this general theory arise by inserting particular strength indicators and truth indicators. In Section 4 the theory is spelt out for the Bayesian paradigm of subjective probabilities. It is then compared to incremental Bayesian confirmation theory. Section 4 closes by asking whether it is likely to be lovely. Section 5 discusses a few problems of confirmation theory in the light of the present approach. In particular, it is briefly indicated how the present account gives rise to a new analysis of Hempel’s conditions of adequacy for any relation of confirmation (Hempel, CG, 1945, Studies in the logic of comfirmation. Mind, 54, 1–26, 97–121.), differing from the one Carnap gave in § 87 of his Logical foundations of probability (1962, Chicago: University of Chicago Press). Section 6 adresses the question of justification any theory of theory assessment has to face: why should one stick to theories given high assessment values rather than to any other theories? The answer given by the Bayesian version of the account presented in section 4 is that one should accept theories given high assessment values, because, in the medium run, theory assessment almost surely takes one to the most informative among all true theories when presented separating data. The concluding section 7 continues the comparison between the present account and incremental Bayesian confirmation theory.     

Faith and belief in The Anti-Christ: A re-evaluation of Nietzsche’s relation to Jesus

This paper re-evaluates the significance of Jesus for Nietzsche by looking at The Anti-Christ. Specifically we will ask whether a re-evaluation of this relation can shed new light on Nietzsche’s critique of Christianity. And we will do this first by surveying the standard interpretations of this issue, as well as the existing literature on The Anti-Christ. Arguing that the latter picks out nothing new regarding a critique of Christianity, we nonetheless suggest that a new criticism can be developed via the discussion of Jesus there. Further, this can be done by looking at the account given of faith and belief in that text. That is, we will explore the status of Jesus for Nietzsche by looking at the origins and development of “faith” as a mode of belief. In particular, we trace the former’s development as a type from a basic mode of faith. As such, we begin by looking at the psychological origins of this kind of belief in “decadence”, and why Nietzsche is critical of this. However, we will then discuss the emergence of a more positive faith in the form of Buddhism, and see how this represents an analogue for Jesus’s faith. Continuing, we will see how Jesus signifies a similar problematic development, but also “overcoming”, of initial decadence faith. And we will argue, also, that this overcoming is rooted in his emphasis on the immediacy of lived experience. Finally though, we will look at how Christianity returns Jesus’s more productive relation to the world again to a primitive mode of faith. In other words, we will see how Christianity converts the fluid, lived, “faith” of Jesus into something again based on transcendent belief. And lastly, we will ask what new light this point sheds on Nietzsche’s critique of Christianity, and his affinity with Jesus the man.     

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.     

The logic in language: How all quantifiers are alike,but each quantifier is different

Quantifier words like each, every, all and three are among the most abstract words in language. Unlike nouns, verbs and adjectives, the meanings of quantifiers are not related to a referent out in the world. Rather, quantifiers specify what relationships hold between the sets of entities, events and properties denoted by other words. When two quantifiers are in the same clause, they create a systematic ambiguity. “Every kid climbed a tree” could mean that there was only one tree, climbed by all, or many different trees, one per climbing kid. In the present study, participants chose a picture to indicate their preferred reading of different ambiguous sentences – those containing every, as well as the other three quantifiers. In Experiment 1, we found large systematic differences in preference, depending on the quantifier word. In Experiment 2, we then manipulated the choice of a particular reading of one sentence, and tested how this affected participants’ reading preference on a subsequent target sentence. We found a priming effect for all quantifiers, but only when the prime and target sentences contained the same quantifier. For example, all-a sentences prime other all-a sentences, while each-a primes each-a, but sentences with each do not prime sentences with all or vice versa. In Experiment 3, we ask whether the lack of priming across quantifiers could be due to the two sentences sharing one fewer word. We find that changing the verb between the prime and target sentence does not reduce the priming effect. In Experiment 4, we discover one case where there is priming across quantifiers – when one number (e.g. three) is in the prime, and a different one (e.g. four) is in the target. We discuss how these findings relate to linguistic theories of quantifier meaning and what they tell us about the division of labor between conceptual content and combinatorial semantics, as well as the mental representations of quantification and of the abstract logical structure of language.     

The origins of vocal learning: New sounds, new circuits, new cells

We do not know how vocal learning came to be, but it is such a salient trait in human evolution that many have tried to imagine it. In primates this is difficult because we are the only species known to possess this skill. Songbirds provide a richer and independent set of data. I use comparative data and ask broad questions: How does vocal learning emerge during ontogeny? In what contexts? What are its benefits? How did it evolve from unlearned vocal signals? How was brain anatomy altered to enable vocal learning? What is the relation of vocal learning to adult neurogenesis? No one has described yet a circuit or set of circuits that can master vocal learning, but this knowledge may soon be within reach. Moreover, as we uncover how birds encode their learned song, we may also come closer to understanding how we encode our thoughts.     

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.     

One after the other: Effects of sequence patterns of breached and overfulfilled obligations

To date, the study of psychological contracts has primarily centred on the question how retrospective evaluations of the psychological contract impact employee attitudes and behaviours, and/or focus on individual coping processes in explaining responses to breached or overfulfilled obligations. In this study, we aim to assess the extent to which sequences of breached and overfulfilled obligations impact job satisfaction and citizenship behaviour intentions. By integrating psychological contract research and theories on cognitive information processing, we formulate competing hypotheses on how sequences of breached and/or overfulfilled obligations lead to patterns of job satisfaction and citizenship behaviour intentions. These competing hypotheses were tested using a vignette study and an experiment. A Bayesian approach was used to test these pattern hypotheses directly against each other. The results show that breached obligations have an immediate negative impact on our outcome variables. Moreover, sequentially breached obligations lead to a continuous decline of job satisfaction and citizenship behaviour intentions. Overfulfilled obligations do little to compensate this negative impact. Implications for theory and practice are discussed.