Philosophers and psychologists make many different, seemingly incompatible parsimony claims in support of competing models of cognition in nonhuman animals. This variety of parsimony claims is problematic. Firstly, it is difficult to justify each specific variety. This problem is especially salient for Morgan's Canon, perhaps the most important variety of parsimony claimed. Secondly, there is no systematic way of adjudicating between particular claims when they conflict. I argue for a view of parsimony in comparative psychology that solves these problems, based (...) on Sober's view that parsimony claims are claims that one model is more plausible given background theory. (shrink)

This article discusses various dangers that accompany the supposedly benign methods in behavioral evoltutionary biology and evolutionary psychology that fall under the framework of "methodological adaptationism." A "Logic of Research Questions" is proposed that aids in clarifying the reasoning problems that arise due to the framework under critique. The live, and widely practiced, " evolutionary factors" framework is offered as the key comparison and alternative. The article goes beyond the traditional critique of Stephen Jay Gould and Richard C. Lewontin, to (...) present problems such as the disappearance of evidence, the mishandling of the null hypothesis, and failures in scientific reasoning, exemplified by a case from human behavioral ecology. In conclusion the paper shows that "methodological adaptationism" does not deserve its benign reputation. (shrink)

Douglas proposes a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, protecting the integrity and objectivity of science.

The chapter discusses the principle of conservatism and traces how the general principle is related to the specific one. This tracing suggests that the principle of conservatism needs to be refined. Connecting the principle in cognitive science to more general questions about scientific inference also allows us to revisit the question of realism versus instrumentalism. The framework deployed in model selection theory is very general; it is not specific to the subject matter of science. The chapter outlines some non-Bayesian ideas (...) that have been developed in model selection theory. The principle of conservatism, like C. Lloyd Morgan's canon, describes a preference concerning kinds of parameters. It says that a model that postulates only lower-level intentionality is preferable to one that postulates higher-level intentionality if both fit the data equally well. The model selection approach to parsimony helps explain why unification is a theoretical virtue. (shrink)

Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate (...) the higher-order, systematic, relational capabilities of a physical symbol system (PSS) (Newell 1980). We show that this symbolic-relational discontinuity pervades nearly every domain of cognition and runs much deeper than even the spectacular scaffolding provided by language or culture alone can explain. We propose a representational-level specification as to where human and nonhuman animals' abilities to approximate a PSS are similar and where they differ. We conclude by suggesting that recent symbolic-connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds. (shrink)

Morgan’s Canon is a very widely endorsed methodological principle in animal psychology, believed to be vital for a rigorous, scientific approach to the study of animal cognition. In contrast I argue that Morgan’s Canon is unjustified, pernicious and unnecessary. I identify two main versions of the Canon and show that they both suffer from very serious problems. I then suggest an alternative methodological principle that captures all of the genuine methodological benefits that Morgan’s Canon can bring but suffers from none (...) of its problems. (shrink)

Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...) is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a meta-statistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies. Introduction and overview 1.1 Behavioristic and inferential rationales for Neyman–Pearson (N–P) tests 1.2 Severity rationale: induction as severe testing 1.3 Severity as a meta-statistical concept: three required restrictions on the N–P paradigm Error statistical tests from the severity perspective 2.1 N–P test T(): type I, II error probabilities and power 2.2 Specifying test T() using p-values Neyman's post-data use of power 3.1 Neyman: does failure to reject H warrant confirming H? Severe testing as a basic concept for an adequate post-data inference 4.1 The severity interpretation of acceptance (SIA) for test T() 4.2 The fallacy of acceptance (i.e., an insignificant difference): Ms Rosy 4.3 Severity and power Fallacy of rejection: statistical vs. substantive significance 5.1 Taking a rejection of H0 as evidence for a substantive claim or theory 5.2 A statistically significant difference from H0 may fail to indicate a substantively important magnitude 5.3 Principle for the severity interpretation of a rejection (SIR) 5.4 Comparing significant results with different sample sizes in T(): large n problem 5.5 General testing rules for T(), using the severe testing concept The severe testing concept and confidence intervals 6.1 Dualities between one and two-sided intervals and tests 6.2 Avoiding shortcomings of confidence intervals Beyond the N–P paradigm: pure significance, and misspecification tests Concluding comments: have we shown severity to be a basic concept in a N–P philosophy of induction? (shrink)

"Theoretical pluralism" obtains when there are good evidential reasons for accommodating multiple theories of the same domain. Issues of "relative significance" often arise in connection with the investigation of such domains. In this paper, I describe and give examples of theoretical pluralism and relative significance issues. Then I explain why theoretical pluralism so often obtains in biology--and why issues of relative significance arise--in terms of evolutionary contingencies and the paucity or lack of laws of biology. Finally, I turn from explanation (...) to justification, and raise questions about the purpose and value of concerns and disagreements about relative significance. (shrink)

Two strategies for using a model as “null” are distinguished. Null modeling evaluates whether a process is causally responsible for a pattern by testing it against a null model. Baseline modeling measures the relative significance of various processes responsible for a pattern by detecting deviations from a baseline model. When these strategies are conflated, models are illegitimately privileged as accepted until rejected. I illustrate this using the neutral theory of ecology and draw general lessons from this case. First, scientists cannot (...) draw certain conclusions using null modeling. Second, these conclusions follow using baseline modeling, but doing so requires more evidence. (shrink)

There is currently a consensus among comparative psychologists that nonhuman animals are capable of some forms of mindreading. Several philosophers and psychologists have criticized this consensus, however, arguing that there is a “logical problem” with the experimental approach used to test for mindreading in nonhuman animals. I argue that the logical problem is no more than a version of the general skeptical problem known as the theoretician’s dilemma. As such, it is not a problem that comparative psychologists must solve before (...) providing evidence for mindreading. (shrink)

Amidst long-running debates within the field, high energy physics has adopted a statistical methodology that primarily employs standard frequentist techniques such as significance testing and confidence interval estimation, but incorporates Bayesian methods for limited purposes. The discovery of the Higgs boson has drawn increased attention to the statistical methods employed within HEP. Here I argue that the warrant for the practice in HEP of relying primarily on frequentist methods can best be understood as pragmatic, in the sense that statistical methods (...) are chosen for their suitability for the practical demands of experimental HEP, rather than reflecting a commitment to a particular epistemic framework. In particular, I argue that understanding the statistical methodology of HEP through the perspective of pragmatism clarifies the role of and rationale for significance testing in the search for new phenomena such as the Higgs boson. (shrink)

An accessible and illuminating exploration of the conceptual basisof scientific and statistical inference and the practical impact this has on conducting psychological research. The book encourages a critical discussion of the different approaches and looks at some of the most important thinkers and their influence.

A widespread assumption in experimental comparative cognition is that, barring compelling evidence to the contrary, the default hypothesis should postulate the simplest cognitive ontology consistent with the animal’s behavior. I call this assumption the principle of cognitive simplicity . In this essay, I show that PoCS is pervasive but unjustified: a blanket preference for the simplest cognitive ontology is not justified by any of the available arguments. Moreover, without a clear sense of how cognitive ontologies are to be carved up (...) at the joints—and which tools are appropriate for the job—PoCS rests on shaky conceptual ground. (shrink)

Contrary to formal theories of induction, I argue that there are no universal inductive inference schemas. The inductive inferences of science are grounded in matters of fact that hold only in particular domains, so that all inductive inference is local. Some are so localized as to defy familiar characterization. Since inductive inference schemas are underwritten by facts, we can assess and control the inductive risk taken in an induction by investigating the warrant for its underwriting facts. In learning more facts, (...) we extend our inductive reach by supplying more localized inductive inference schemes. Since a material theory no longer separates the factual and schematic parts of an induction, it proves not to be vulnerable to Hume's problem of the justification of induction. (shrink)

Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a background theory. In this paper, we describe a result due to Akaike [1973], which shows how the data can underwrite an inference concerning the curve's form based on an estimate of how predictively accurate it will be. We argue that this approach throws light (...) on the theoretical virtues of parsimoniousness, unification, and non ad hocness, on the dispute about Bayesianism, and on empiricism and scientific realism. * Both of us gratefully acknowledge support from the Graduate School at the University of Wisconsin-Madison, and NSF grant DIR-8822278 (M.F.) and NSF grant SBE-9212294 (E.S.). Special thanks go to A. W. F. Edwards.William Harper. Martin Leckey. Brian Skyrms, and especially Peter Turney for helpful comments on an earlier draft. (shrink)

Genetic drift (variously called “random drift”, “random genetic drift”, or sometimes just “drift”) has been a source of ongoing controversy within the philosophy of biology and evolutionary biology communities, to the extent that even the question of what drift is has become controversial. There seems to be agreement that drift is a chance (or probabilistic or statistical) element within population genetics and within evolutionary biology more generally, and that the term “random” isn’t invoking indeterminism or any technical mathematical meaning, but (...) that’s about where agreement ends. Yet genetic drift models are a staple topic in population genetics textbooks and research, with genetic drift described as one of the main factors of evolution alongside selection, mutation, and migration. Some claim that genetic drift has played a major role in evolution (particularly molecular evolution), while others claim it to be minor. This article examines these and other controversies. -/- In order to break through the logjam of competing definitions of drift, this entry begins with a brief history of the concept, before examining various philosophical claims about the proper characterization of drift and whether it can be distinguished from natural selection; the relation of drift to debates over statisticalism; whether drift can be detected empirically and if so, how; and the proper understanding of drift as a model and as a (purported) law. (shrink)

Akaike’s framework for thinking about model selection in terms of the goal of predictive accuracy and his criterion for model selection have important philosophical implications. Scientists often test models whose truth values they already know, and they often decline to reject models that they know full well are false. Instrumentalism helps explain this pervasive feature of scientific practice, and Akaike’s framework helps provide instrumentalism with the epistemology it needs. Akaike’s criterion for model selection also throws light on the role of (...) parsimony considerations in hypothesis evaluation. I explain the basic ideas behind Akaike’s framework and criterion; several biological examples, including the use of maximum likelihood methods in phylogenetic inference, are considered. (shrink)

The question of whether chimpanzees, like humans, reason about unobservable mental states remains highly controversial. On one account, chimpanzees are seen as possessing a psychological system for social cognition that represents and reasons about behaviors alone. A competing account allows that the chimpanzee's social cognition system additionally construes the behaviors it represents in terms of mental states. Because the range of behaviors that each of the two systems can generate is not currently known, and because the latter system depends upon (...) the former, determining the presence of this latter system in chimpanzees is a far more difficult task than has been assumed. We call for recognition of this problem, and a shift from experimental paradigms that cannot resolve this question, to ones that might allow researchers to intelligently determine when it is necessary to postulate the presence of a system which reasons about both behavior and mental states. (shrink)

But do animals know that other creatures have minds? And how would we know if they do? In "Mindreading Animals," Robert Lurz offers a fresh approach to the hotly debated question of mental-state attribution in nonhuman animals.

Akaike's framework for thinking about model selection in terms of the goal of predictive accuracy and his criterion for model selection have important philosophical implications. Scientists often test models whose truth values they already know, and they often decline to reject models that they know full well are false. Instrumentalism helps explain this pervasive feature of scientific practice, and Akaike's framework helps provide instrumentalism with the epistemology it needs. Akaike's criterion for model selection also throws light on the role of (...) parsimony considerations in hypothesis evaluation. I explain the basic ideas behind Akaike's framework and criterion; several biological examples, including the use of maximum likelihood methods in phylogenetic inference, are considered. (shrink)

Neyman-Pearson methods in statistics distinguish between Type I and Type II errors. Through rigid control of Type I error, the "null" hypothesis typically receives the benefit of the doubt. I compare philosophers' interpretations of this feature of Neyman-Pearson tests with interpretations given in statistics textbooks. The pragmatic view of the tests advocated by Neyman, largely rejected by philosophers, lives on in many textbooks. Birnbaum thought the pragmatic view has a useful "heuristic" role in understanding testing. I suggest that it may (...) have the opposite effect. (shrink)