Abstract and concrete data in the perseverance of social theories: When weak data lead to unshakeable beliefs

The perseverance effect—the finding that people cling to their initial beliefs more strongly than appears warranted—has been demonstrated in a wide variety of settings. The existing explanation of the effect implies that beliefs based on concrete data should be more resistant to challenges than beliefs based on abstract data. The present studies compared the amount of belief perseverance when the beliefs were initially based on either abstract or concrete data. Subjects examined either two case histories (concrete data) or a statistical summary (abstract data) suggestive of either a positive or a negative relationship between fire fighter trainees' level of preference for high risk and their subsequent success as firefighters. These data sets were equated for the initial strength of beliefs they induced. Subjects were the thoroughly debriefed about the fictitious nature of their initial data. Subsequent assessments of subjects' personal beliefs about the true relationship revealed (a) significant levels of theory perseverance both immediately and 1 week later; (b) significantly more perseverance in the concrete data conditions, both immediately and 1 week later. Experiment 2 revealed that subjects frequently engage in causal processing spontaneously, especially when examining concrete data. Overall, the data suggested that memory for initial data did not contribute to the abstract/concrete effects, but that the generation of general, causal explanations did contribute to the stronger perseverance of theories in the concrete conditions.     

Abstract versus Causal Explanations?

In the recent literature on causal and non-causal scientific explanations, there is an intuitive assumption (which we call the ‘abstractness assumption’) according to which an explanation is non-causal by virtue of being abstract. In this context, to be ‘abstract’ means that the explanans in question leaves out many or almost all causal microphysical details of the target system. After motivating this assumption, we argue that the abstractness assumption, in placing the abstract and the causal character of an explanation in tension, is misguided in ways that are independent of which view of causation or causal explanation one takes to be most accurate. On major accounts of causation, as well as on major accounts of causal explanation, the abstractness of an explanation is not sufficient for it being non-causal. That is, explanations are not non-causal by dint of being abstract.     

There are Non‐Causal Explanations of Particular Events

A defence of non‐causal explanations of events is presented in cases where explanation is understood as modal explanation. In such cases the source of modal information is crucial. All explanations implicitly use contrast classes, and relative to a particular contrast we can privilege some difference makers over others. Thinking about changes in these privileged “actual” difference makers is then the source of modal information for any given explanandum. If an actual difference maker is non‐causal, then we have a principled definition of a non‐causal explanation of an event regardless of how much causal information is also contained in the explanans. A demarcation of explanation into causal and non‐causal in this way recovers ordinary language about explanation as well as reflecting genuine differences in practice, such as the in‐principle evidential base for any modal claim.     

Degree of explanation

Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself.     

Manipulation is key: on why non-mechanistic explanations in the cognitive sciences also describe relations of manipulation and control

A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is not clear how to distinguish explanatory from non-explanatory relations. In this paper, I draw from both views and suggest that, in the cognitive sciences, relations of counterfactual dependence that allow manipulation and control can be explanatory even when they are neither causal nor mechanistic. Furthermore, the ability to allow manipulation can determine whether non-causal counterfactual dependence relations are explanatory. I present a preliminary framework for manipulation relations that includes some non-causal relations and use two examples from the cognitive sciences to show how this framework distinguishes between explanatory and non-explanatory, non-causal relations. The proposed framework suggests that, in the cognitive sciences, causal and non-causal relations have the same criterion for explanatory value, namely, whether or not they allow manipulation and control.     

Selective copying of the majority suggests children are broadly “optimal‐” rather than “over‐” imitators

Human children, in contrast to other species, are frequently cast as prolific “over‐imitators”. However, previous studies of “over‐imitation” have overlooked many important real‐world social dynamics, and may thus provide an inaccurate account of this seemingly puzzling and potentially maladaptive phenomenon. Here we investigate this topic using a cultural evolutionary approach, focusing particularly on the key adaptive learning strategy of majority‐biased copying. Most “over‐imitation” research has been conducted using consistent demonstrations to the observer, but we systematically varied the frequency of demonstrators that 4‐ to 6‐year‐old children observed performing a causally irrelevant action. Children who “over‐imitate” inflexibly should copy the majority regardless of whether the majority solution omits or includes a causally irrelevant action. However, we found that children calibrated their tendency to acquire the majority behavior, such that copying did not extend to majorities that performed irrelevant actions. These results are consistent with a highly functional, adaptive integration of social and causal information, rather than explanations implying unselective copying or causal misunderstanding. This suggests that our species might be better characterized as broadly “optimal‐” rather than “over‐” imitators.     

Old Wine in New Bottles

Evolutionary debunking arguments (EDAs) purport to show that robust moral realism, the metaethical view that there are non-natural and mind-independent moral properties and facts that we can know about, is incompatible with evolutionary explanations of morality. One of the most prominent evolutionary debunking arguments is advanced by Sharon Street, who argues that if moral realism were true, then objective moral knowledge is unlikely because realist moral properties are evolutionary irrelevant and moral beliefs about those properties would not be selected for. However, no evolutionary, causal explanation plays an essential role in reaching the argument’s epistemological conclusion. Street’s argument depends on the Benacerraf-Field challenge, which is the challenge to explain the reliability of our moral beliefs about causally inert moral properties. The Benacerraf-Field challenge relies on metaphysically necessary facts about realist moral properties, rather than on contingent Darwinian facts about the origin of our moral beliefs. Attempting to include an essential causal empirical premise yet avoiding recourse to the Benacerraf-Field problem yields an argument that is either self-defeating or of limited scope. Ultimately, evolutionary, causal explanations of our moral beliefs and their consequences do not present the strongest case against robust moral realism. Rather, the question is whether knowledge of casually-inert, mind-intendent properties is plausible at all.     

Causal uncertainty and metacognitive inferences about goal attainment

These four studies investigated G. Weary and J. A. Edwards’ (1996) hypothesis that causal uncertainty feelings serve as input to perceivers regarding the adequacy of their causal knowledge and thus determine the amount of processing accorded a given task. Participants worked on a task until they had satisfied an assigned stop rule. In three experiments, high causally uncertain people processed more information under a sufficiency of information rule and less under an enjoyment rule, whereas low causally uncertain people generally did not differentiate between the rules. In the last experiment, low causally uncertain people exhibited a similar pattern to the chronic causally uncertain individuals in the first experiments, but only after their causal uncertainty beliefs and feelings had been primed.     

Functional explanation and the function of explanation

Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted--for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3-5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.     

Topological explanations and robustness in biological sciences

This paper argues that besides mechanistic explanations, there is a kind of explanation that relies upon “topological” properties of systems in order to derive the explanandum as a consequence, and which does not consider mechanisms or causal processes. I first investigate topological explanations in the case of ecological research on the stability of ecosystems. Then I contrast them with mechanistic explanations, thereby distinguishing the kind of realization they involve from the realization relations entailed by mechanistic explanations, and explain how both kinds of explanations may be articulated in practice. The second section, expanding on the case of ecological stability, considers the phenomenon of robustness at all levels of the biological hierarchy in order to show that topological explanations are indeed pervasive there. Reasons are suggested for this, in which “neutral network” explanations are singled out as a form of topological explanation that spans across many levels. Finally, I appeal to the distinction of explanatory regimes to cast light on a controversy in philosophy of biology, the issue of contingence in evolution, which is shown to essentially involve issues about realization.     

The selective power of causality on memory errors

We tested the influence of causal links on the production of memory errors in a misinformation paradigm. Participants studied a set of statements about a person, which were presented as either individual statements or pairs of causally linked statements. Participants were then provided with causally plausible and causally implausible misinformation. We hypothesised that studying information connected with causal links would promote representing information in a more abstract manner. As such, we predicted that causal information would not provide an overall protection against memory errors, but rather would preferentially help in the rejection of misinformation that was causally implausible, given the learned causal links. In two experiments, we measured whether the causal linkage of information would be generally protective against all memory errors or only selectively protective against certain types of memory errors. Causal links helped participants reject implausible memory lures, but did not protect against plausible lures. Our results suggest that causal information may promote an abstract storage of information that helps prevent only specific types of memory errors.     

Causal coherence and the availability of locations and objects during narrative comprehension

The aim of this study was to examine whether locations of objects are encoded and available to the reader at different points in a narrative, depending on their causal relevance. Participants in five experiments read narratives in which the spatial relation between an object and its location either did or did not provide a causal explanation for a later critical event. Object and location target words were presented to the participants immediately before or after the critical event. Speeded recognition response times to target words demonstrated that both locations and objects were reactivated, but only after they became causally relevant. The results suggest that the causal structure of a text can influence the availability of spatial information and that at least some spatial relations are encoded during reading and are available to the reader when they are needed to build coherence.     

Functional analysis and mechanistic explanation

Piccinini and Craver (Synthese 183:283–311, 2011) argue for the surprising view that psychological explanation, properly understood, is a species of mechanistic explanation. This contrasts with the ‘received view’ (due, primarily, to Cummins and Fodor) which maintains a sharp distinction between psychological explanation and mechanistic explanation. The former is typically construed as functional analysis, the analysis of some psychological capacity into an organized series of subcapacities without specifying any of the structural features that underlie the explanandum capacity. The latter idea, of course, sees explanation as a matter of describing structures that maintain (or produce) the explanandum capacity. In this paper, I defend the received view by criticizing Piccinini and Craver’s argument for the claim that psychological explanation is not distinct from mechanistic explanation, and by showing how psychological explanations can possess explanatory force even when nothing is known about the underlying neurological details. I conclude with a few brief criticisms about the enterprise of mechanistic explanation in general.     

Exploring the Effects of the Naturalistic Fallacy: Evidence That Genetic Explanations Increase the Acceptability of Killing and Male Promiscuity

The naturalistic fallacy is the erroneous belief that what is natural is morally acceptable. Two studies assessed whether people commit the naturalistic fallacy by testing whether genetic explanations for killing and male promiscuity, as compared to experiential explanations (i.e., learning/“nurture” explanations) increase acceptance of these behaviors. In Study 1, participants who read a genetic explanation for why people kill bugs viewed bug killing as more morally acceptable than participants who read an experiential explanation, although they did not reliably kill more bugs. In Study 2, men who read a genetic explanation for why men are more promiscuous than women reported decreased interest in long‐term romantic commitment compared with men who read experiential explanations and women who read either explanation.     

The relation between visual imagery mediators and recall

Three hypotheses are discussed as explanations for the result that pairs of concrete nouns are more easily remembered than are pairs of abstract nouns: the imagery hypothesis, the familiarity hypothesis, and the concreteness hypothesis. Two experiments are reported in which the degree of visual imagery associated with the components of paired associate items was not indicative of the degree of visual imagery experienced during their learning or with the accuracy with which they were recalled. It was found that pairs of related abstract nouns were rated higher in imagery and familiarity than were pairs of unrelated concrete nouns, but recall of the higher imagery pairs was poorer. The concreteness hypothesis is discussed as the best explanation for the results. The concreteness hypothesis proposes that people learn to associate the labels of concrete objects by using their real-world knowledge of the potential relations between categories of objects. Dual coding theory and schema theory are also discussed as explanations for mediation learning, and the issue of visual imagery as an epiphenomenon is addressed.     

Social judgments of spurious and causal relations between attributes and outcomes

People can learn about relations between attributes and outcomes by observing the attributes and outcomes of others, but, as this experiment indicates, such learning is not always veridical. Each subject received information about the ages, educations, and salaries of groups of employees in a fictitious corporation. Within a group, either age or education was related to salaries and the two attributes were either orthogonal or correlated. In each case, subjects judged the strength of the causal relation between each attribute and salaries. The results confirmed our hypothesis that observers are more likely to view a causally relevant attribute as irrelevant, and a causally irrelevant attribute as relevant, when relevant and irrelevant attributes are correlated. However, this tendency seemed to be mediated by subjects' bias to prefer education as an explanation of salary differences: That is, when age and education were correlated, subjects tended to view education as relevant even when it was irrelevant and age as irrelevant even when it was relevant. The results suggest that when attributes are correlated, factors extraneous to observed data may have a major influence on inferred attribute-outcome associations.     

Accounting for macro-level causation

By a macro-level feature, I understand any feature that supervenes on, and is thus realized in, lower-level features. Recent discussions by Kim have suggested that such features cannot be causally relevant insofar as they are not classically reducible to lower-level features. This seems to render macro-level features causally irrelevant. I defend the causal relevance of some such features. Such features have been thought causally relevant in many examples that have underpinned philosophical work on causality. Additionally, in certain typical biological cases, we conceive of causally relevant features at various compatible levels of analysis. When elaborated, these points make a strong prima facie case for macro-level causal relevance. However, we might abandon both the philosophical guideposts and the corresponding explanatory practice in the special sciences were we convinced that no reflective philosophical account could provide for the causal relevance there supposed. I show that such drastic measures are not necessary, for we can make sense of macro-level causal relevance by drawing on Paul Humphreys' recent work in ways suggested by the concrete examples considered here.Some of the work on this paper was undertaken at the 1991 NEH Summer Seminar on Causality, directed by Paul Humphreys. I wish to thank my fellow participants, and Paul Humphreys, John Tienson, and Terry Horgan for helpful comments on earlier drafts of this paper.     

Projective Explanation: How Theories Explain Empirical Data in Spite of Theory-Data Incommensurability

In scientific explanations, the explanans theory is sometimes incommensurable with the explanandum empirical data. How is this possible, especially when the explanation is deductive in nature? This paper attempts to solve the puzzle without relying on any particular theory of reference. For us, it is rather obvious that the geometric idea of projection plays a key role in Kepler’s explanation of Tycho Brahe’s empirical data. We discover that a similar mechanism operates in theoretic explanations in general. In short, all theoretic explanations are “projective” explanations. If so, there should be no logical reason why explanans theories cannot be incommensurable with explanandum data. For illustration, we analyse Einstein’s explanation of the results of the Michelson–Morley experiment in some detail.