Required reading at more than 100 colleges and universities throughout North America.

With this third edition of Nelson Goodman's The Structure of Appear ance, we are pleased to make available once more one of the most in fluential and important works in the philosophy of our times. Professor Geoffrey Hellman's introduction gives a sustained analysis and appreciation of the major themes and the thrust of the book, as well as an account of the ways in which many of Goodman's problems and projects have been picked up and developed by others. Hellman also (...) suggests how The Structure of Appearance introduces issues which Goodman later continues in his essays and in the Languages of Art. There remains the task of understanding Good man's project as a whole; to see the deep continuities of his thought, as it ranges from logic to epistemology, to science and art; to see it therefore as a complex yet coherent theory of human cognition and practice. What we can only hope to suggest, in this note, is the b. road Significance of Goodman's apparently technical work for philosophers, scientists and humanists. One may say of Nelson Goodman that his bite is worse than his bark. Behind what appears as a cool and methodical analysis of the conditions of the construction of systems, there lurks a radical and disturbing thesis: that the world is, in itself, no more one way than another, nor are we. It depends on the ways in which we take it, and on what we do. (shrink)

"Like Dewey, he has revolted against the empiricist dogma and the Kantian dualisms which have compartmentalized philosophical thought.... Unlike Dewey, he has provided detailed incisive argumentation, and has shown just where the dogmas and dualisms break down." --Richard Rorty, _The Yale Review_.

Essays discuss cognition, perception, art, science, truth, metaphor, education, philosophy, and cognitive psychology.

Knowing and Making 1. Obstacles to Knowing The theory of knowledge to be sketched here rejects both absolutism and nihilism, both unique truth and the ...

Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the (...) notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call “resource-rational analysis.”. (shrink)

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...) inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition. (shrink)

Is language understanding a special case of social cognition? To help evaluate this view, we can formalize it as the rational speech-act theory: Listeners assume that speakers choose their utterances approximately optimally, and listeners interpret an utterance by using Bayesian inference to “invert” this model of the speaker. We apply this framework to model scalar implicature (“some” implies “not all,” and “N” implies “not more than N”). This model predicts an interaction between the speaker's knowledge state and the listener's interpretation. (...) We test these predictions in two experiments and find good fit between model predictions and human judgments. (shrink)

This article proposes a new model of human concept learning that provides a rational analysis of learning feature‐based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well‐known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7‐feature concepts—a more natural setting in several (...) ways—and again finds that the model explains human performance. (shrink)

We derive a probabilistic account of the vagueness and context-sensitivity of scalar adjectives from a Bayesian approach to communication and interpretation. We describe an iterated-reasoning architecture for pragmatic interpretation and illustrate it with a simple scalar implicature example. We then show how to enrich the apparatus to handle pragmatic reasoning about the values of free variables, explore its predictions about the interpretation of scalar adjectives, and show how this model implements Edgington’s Vagueness: a reader, 1997) account of the sorites paradox, (...) with variations. The Bayesian approach has a number of explanatory virtues: in particular, it does not require any special-purpose machinery for handling vagueness, and it is integrated with a promising new approach to pragmatics and other areas of cognitive science. (shrink)

Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher‐level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea that (...) higher‐level theories guide learning at lower levels. In addition, they help resolve certain issues for Bayesians, such as scientific preference for simplicity and the problem of new theories. *Received July 2009; revised October 2009. †To contact the authors, please write to: Leah Henderson, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 32D‐808, Cambridge, MA 02139; e‐mail: [email protected]. (shrink)

Recent debates over adults' theory of mind use have been fueled by surprising failures of perspective-taking in communication, suggesting that perspective-taking may be relatively effortful. Yet adults routinely engage in effortful processes when needed. How, then, should speakers and listeners allocate their resources to achieve successful communication? We begin with the observation that the shared goal of communication induces a natural division of labor: The resources one agent chooses to allocate toward perspective-taking should depend on their expectations about the other's (...) allocation. We formalize this idea in a resource-rational model augmenting recent probabilistic weighting accounts with a mechanism for (costly) control over the degree of perspective-taking. In a series of simulations, we first derive an intermediate degree of perspective weighting as an optimal trade-off between expected costs and benefits of perspective-taking. We then present two behavioral experiments testing novel predictions of our model. In Experiment 1, we manipulated the presence or absence of occlusions in a director–matcher task. We found that speakers spontaneously modulated the informativeness of their descriptions to account for “known unknowns” in their partner's private view, reflecting a higher degree of speaker perspective-taking than previously acknowledged. In Experiment 2, we then compared the scripted utterances used by confederates in prior work with those produced in interactions with unscripted directors. We found that confederates were systematically less informative than listeners would initially expect given the presence of occlusions, but listeners used violations to adaptively make fewer errors over time. Taken together, our work suggests that people are not simply “mindblind”; they use contextually appropriate expectations to navigate the division of labor with their partner. We discuss how a resource-rational framework may provide a more deeply explanatory foundation for understanding flexible perspective-taking under processing constraints. (shrink)

Obviously enough the tongue, the spelling, the typography, the verbosity of a description reflect no parallel features in the world. Coherence is a characteristic of descriptions, not of the world: the significant question is not whether the world is coherent, but whether our account of it is. And what we call the simplicity of the world is merely the simplicity we are able to achieve in describing it.

An important, but relatively neglected, aspect of human theory of mind is emotion inference: understanding how and why a person feels a certain why is central to reasoning about their beliefs, desires and plans. The authors review recent work that has begun to unveil the structure and determinants of emotion inference, organizing them within a unified probabilistic framework.

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

Arthur Schopenhauer ranked the several arts in a hierarchy, with literary and dramatic arts at the top, music soaring in a separate even higher heaven, and architecture sinking to the ground under the weight of beams and bricks and mortar.1 The governing principle seems to be some measure of spirituality, with architecture ranking lowest by vice of being grossly material.Nowadays such rankings are taken less seriously. Traditional ideologies and mythologies of the arts are undergoing deconstruction and disvaluation, making way for (...) a neutral comparative study that can reveal a good deal not only about relations among the several arts2 but also about the kinships and contrasts between the arts, the sciences, and other ways that symbols of various kinds participate in the advancement of the understanding.In comparing architecture with the other arts, what may first strike us, despite Schopenhauer, is a close affinity with music: architectural and musical works, unlike paintings or plays or novels, are seldom descriptive or representational. With some interesting exceptions, architectural works do not denote—that is, do not describe, recount, depict, or portray. They mean, if at all, in other ways.On the other hand, and architectural work contrasts with other works of art in scale. A building or park or city3 is not only bigger spatially and temporally than a musical performance or painting—it is bigger even than we are. We cannot take it all in from a single point of view; we must move around and within it to grasp the whole. Moreover, the architectural work is normally fixed in place. Unlike a painting that may be reframed and rehung or a concerto that may be heard in different halls, the architectural work is firmly set in physical and cultural environment that alters slowly. 1. See Bryan Magee, The Philosophy of Schopenhauer , pp. 176-78. Nelson Goodman, emeritus professor of philosophy at Harvard University, is the author of, among other works, The Structure of Appearance, Languages of Art, Ways of Worldmaking, and Of Mind and Other Matters. His previous contributions to Critical Inquiry are “The Status of Style” , “Metaphor as Moonlighting” , “Twisted Tales; or, Story, Study, and Symphony” , “The Telling and the Told” , and “Routes of Reference) Autumn 1981). (shrink)

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...) objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. (shrink)

The acknowledged difficulty and even impossibility of finding a literal paraphrase for most metaphors is offered by [Donald] Davidson1 as evidence that there is nothing to be paraphrased - that a sentence says nothing metaphorically that it does not say literally, but rather functions differently, inviting comparisons and stimulating thought. But paraphrase of many literal sentences also is exceedingly difficult, and indeed we may seriously question whether any sentence can be translated exactly into other words in the same or any (...) other language. Let's agree, though, that literal paraphrase of metaphor is on the whole especially hard. That is easily understood since the metaphorical application of terms has the effect, and usually the purpose, of drawing significant boundaries that cut across ruts worn by habit, of picking out new relevant kinds for which we have no simple and familiar literal descriptions. We must note in passing, though, that the metaphorical application may nevertheless be quite clear. For just as inability to define "desk" is compatible with knowing which articles are desks, so inability to paraphrase a metaphorical term is compatible with knowing what it applies to, And as I have remarked elsewhere,2 whether a man is metaphorically a Don Quixote or a Don Juan is perhaps easier to decide than whether he is literally a schizoid or a paranoiac. · 1. In "What Metaphors Mean," Critical Inquiry 5 : 31-47.· 2. In "Stories upon Stories; or, Reality on Tiers," delivered at the conference Levels of Reality, in Florence, Italy, September 1978. Nelson Goodman, emeritus professor of philosophy at Harvard, has written The Structure of Appearance; Fact, Fiction, and Forecast; Languages of Art: An Approach to a Theory of Symbols; Problems and Projects; and, most recently, Ways of Worldmaking. His contributions to Critical Inquiry include "The Status of Style" , "Twisted Tales; or, Story, Study, and Symphony" , and "The Telling and the Told". (shrink)