A review of data concerning the uneven distribution of phobias suggests that nonassociative, ethological models can account for most of tile important findings that cannot be attributed to expectancy biases. The origin of a variety of fears that appear in fixed developmental patterns across divergent cultures and species can best be explained by biological models.

In his critique of preparedness theory, Davey does not address the limitations of adaptationism. The purpose of this commentary is to outline problems that arise when one assumes that mental illness (e.g., phobic disorder)musthave had adaptive significance for it to have survived the vicissitudes of natural selection.

Most phobias are focussed on a small number of fear-inducing stimuli (e.g., snakes, spiders). A review of the evidence supporting biological and cognitive explanations of this uneven distribution of phobias suggests that the readiness with which such stimuli become associated with aversive outcomes arises from biases in the processing of information about threatening stimuli rather than from phylogenetically based associative predispositions or “biological preparedness.” This cognitive bias, consisting of a heightened expectation of aversive outcomes following fear-relevant stimuli, generates and maintains (...) robust learned associations between them. Some of the features of such stimuli which determine this expectancy bias are estimates of how dangerous they are, the semiotic similarity between them and their aversive outcomes, and the degree of prior fear they elicit. Ontogenetic and cultural factors influence these features of fear-relevant stimuli and are hence important in determining expectancy bias. The available evidence does not exclude the possibility that both expectancy biases and specific evolved predispositions coexist, but the former can explain a number of important findings that the latter cannot. (shrink)

Studies on associative learning in normals and patients need appropriate dependent measures which are sensitive enough to reflect stimulus-specific responses and also consider the context in which the conditioning takes place. Patient's fear responses, once acquired, seem to be maintained by specific cognitive biases such as individual belief systems and a tendency to stay consistent with their previous judgments.

Davey has failed to clarify the critical evidence that could corroborate the expectancy bias hypothesis and refute preparedness theory. Such a clarification is necessary because each theory could potentially allow for multiple distal and proximal influences on selective associations. Expectancies are not the only proximal mediators. Our recent findings indicate that affective response matching may be an additional factor promoting such associations.

In this response, we clarify several misunderstandings of the understanding/acceptance principle and defend our specific operationalization of that principle. We reiterate the importance of addressing the problem of rational task construal and we elaborate the notion of computational limitations contained in our target article. Our concept of thinking dispositions as variable intentional-level styles of epistemic and behavioral regulation is explained, as is its relation to the rationality debate. Many of the suggestions of the commentators for elaborating two-process models are easily (...) integrated into our generic dual-process account. We further explicate how we view the relation between System 1 and System 2 and evolutionary and normative rationality. We clarify our attempt to fuse the contributions of the cognitive ecologists with the insights of the original heuristics and biases researchers. (shrink)

Davey suggests that increased resistance to extinction of CRs conditioned to fear-relevant stimuli may be due to more persistent expectancies of the UCS following these stimuli. However, this viewpoint is contradicted by existing empirical evidence that fear-relevant CRs survive an extinction trials series producing extinction of expectancies whereas CRs conditioned to non-fear-relevant CSs do not.

According to Davey, generalized expectancy biases cause fearrelevant behavior and may complement Seligman's biological preparedness model. Expectancy biases do not explain the preparedness phenomenon, because such cognitive (or covert behavioral) processes are themselves controlled by social and other environmentally based contingencies. Davey's own examination of the importance of cross-cultural factors can show the relationship between FR stimuli and behavior without needing cognitive agency to explain the behavioral phenomenon.

Expectations can facilitate rapid fear conditioning and this may explain some phenomena that have been attributed to preparedness. However, preparedness remains the best explanation for some aspects of clinical phobias and the difficulty of creating fears of modern dangers. Rapid fear conditioning based on expectancy is not an alternative to an evolutionary explanation, but has, like preparedness, been shaped by natural selection.

Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relationship between perceived control and symptoms of (...) mood disorders. We tested these hypotheses with European university samples who were categorized as endorsing (or not) values relevant to control - individualist and collectivist values. Three online experimental contingency learning studies (N1 = 127, N2 = 324, N3 = 272) were carried out. Evidence suggested that individualist values influenced basic learning processes via an effect on learning about the context in which events took place. Participants who endorsed individualist values made control judgments that were more in line with an elemental associative learning model, whilst those who were ambivalent about individualist values made judgments that were more consistent with a configural process. High levels of perceived control and individualist values were directly associated with increased euphoric symptoms of bipolar disorder, and such values completely mediated the relation between perceived control and symptoms. The effect of low perceived control on depression was moderated by collectivist values. Anxiety created by dissonance between values and task may be a catalyst for developing mood symptoms. Conclusions are that values play a significant intermediary role in the relation between perceived control and symptoms of mood disturbance. (shrink)

Davey reviews evidence purporting to distinguish between two accounts of selective associations – expectancy bias and evolved predispositions, although these hypotheses largely apply to different levels of causal analysis. Criticisms of primate studies in which subjects lack prior exposure to stimuli seem uncompelling. Expectancies may sometimes serve as proximal mediators in selective associations, but other factors, both proximate and ultimate, are clearly also involved.

Failure to find an obvious or linear relationship between a developmental experiential factor and a developmental outcome often leads investigators to posit concepts such as “biological preparedness” and “evolved predispositions” that allude to hypothetical geneticmechanisms that may not exist. However, experiential nonlinearities alone may explain the development of certain instinctive behaviors, as shown by studies on alarm call responsivity in mallard ducklings.

Because of their evolutionary importance, threat-detection mechanisms are likely to exist at a variety of levels. A recent study of face recognition suggests that novel stimuli receive enhanced processing when presented as fear-related. This suggests the existence of a complex, context-dependent threat-detection mechanism that can adaptively respond to spatiotemporally varying and unique environmental features.

We endorse Davey's view that expectancy processes are intimately involved in fear reactions, but question his model on three grounds. First, the mechanism for generating expectancy bias to both ontogenetic and phylogenetic stimuli is not spelled out. Second, the selective association component is unnecessary. Third, the model fails to provide a clear explanation for the irrationality of phobic reactions.

Davey's discussion of phobias is criticized because of the lack of distinctions between the various classes of phobias. Psychopharmacological evidence indicates differing pathophysiologies. Clinical psychopharmacological distinctions are not congruent with either a strict phylogenetic preparedness model or with cognitive biases. Davey's critique of the laboratory bred animal studies seems far fetched. His hypothesis concerning the importance of historical significance is clearly ad hoc rather than based on comparative data.

This article reports an empirical investigation of the cognitive effort required to decide in multiattribute binary choice using a variation of the Additive Difference strategy. In contrast with other studies, this paper focuses on the effect of various context variables (rather than task variables) on cognitive effort. In order to select the context variables to be manipulated, we used the model proposed by Shugan (1980; J. Consumer Res. 75 (1980) 99). Our results indicate that there is a positive relationship between (...) the cognitive effort required to decide and the mean of the differences between the dimensions of the choice alternatives. We have also found an inverse relationship between cognitive effort and the variance of the differences between the dimensions of the choice alternatives. Finally, we have found that in negative correlation contexts the effort needed to decide is greater than in positive and null correlation contexts. (shrink)

The evolutionary origin of phobias is strongly supported by behavioral genetics and monkey vicarious conditioning data. Prepared Pavlovian conditioning may be only one of the mechanisms mediating the evolutionarily determined outcome in phobias, avoidance. Davey's alternative biased expectancy hypothesis has merit in accounting for some aspects of laboratory data, but it is insufficient to explain the unconscious origin of phobic fear.

The present commentary emphasizes that the acquisition of fear always involves complex changes in several quasi-independent response systems. Stimulus-specific electrodermal response differentiation as well as the bias to overestimate the belongingness of certain stimulus pairs mainly indicates cognitive processes of selective orienting and attention. Emotion, however, also involves the activation of subcortical motivational circuits. Why certain stimuli acquire rapid access to these basic motivational systems is not explained by the expectancy bias model.

Previous studies of auditory statistical learning have typically presented learners with sequential structural information that is uniformly distributed across the entire exposure corpus. Here we present learners with nonuniform distributions of structural information by altering the organization of trisyllabic nonsense words at midstream. When this structural change was unmarked by low‐level acoustic cues, or even when cued by a pitch change, only the first of the two structures was learned. However, both structures were learned when there was an explicit cue (...) to the midstream change or when exposure to the second structure was tripled in duration. These results demonstrate that successful extraction of the structure in an auditory statistical learning task reduces the ability to learn subsequent structures, unless the presence of two structures is marked explicitly or the exposure to the second is quite lengthy. The mechanisms by which learners detect and use changes in distributional information to maintain sensitivity to multiple structures are discussed from both behavioral and computational perspectives. (shrink)

Many now consider “instinct” and “learning” opposite poles of a unidimensional continuum. An alternative model with two independently varying parameters predicts different selective pressures. Behavioral adaptation matches the organism's utilizations of stimuli and responses to their ecological validities: the mean validity over evolutionary time specifies the optimal initial potency of the prepared association; the variance specifies the optimal prepared plasticity.

Behavioral research suggests that rule-governed behavior should be less sensitive to environmental changes and thus more resistant to extinction (disconfirmation) than contingency-governed behavior. The opposite is implied in Davey's discussion of ontogenetic and phylogenetic contributions to fear development. The generality of the behavioral findings and their apparent inconsistency with the present article should be further explored with more sensitive research paradigms.

This commentary focuses upon two issues raised by Davey's target article: (1) whether there are certain core features of stimuli we learn to fear, rather than specific types of objects or situations, which implies some element of innateness; and (2) whether expectancy biases serve to maintain rather than generate anxiety.

Participants were asked to draw inferences about correlation from single x,y observations. In Experiment 1 statistically sophisticated participants were given the univariate characteristics of distributions of x and y and asked to infer whether a single x, y observation came from a correlated or an uncorrelated population. In Experiment 2, students with a variety of statistical backgrounds assigned posterior probabilities to five possible populations based on single x, y observations, again given knowledge of the univariate statistics. In Experiment 3, statistically (...) naïve participants were given a problem analogous to that given in Experiment 1, framed verbally. Experiment 4 replicated Experiment 3 but added an “impossible to determine” response option. Models that rely on computing sample correlations make no predictions about these investigations. From a Bayesian perspective, participants’ inferences in all four experiments tended to make probabilistically valid inferences as long as the single datum was directional. The results are discussed in light of the Brunswikian notion of vicarious functioning. (shrink)

This commentary acknowledges the importance of Davey's biocognitive approach to the uneven distribution of fears on the basis of its contribution to a human model for understanding fear. An integrated heredity-environment and developmental transactional approach based on field/system theory is recommended in place of the mechanistic heredity × environment interactionism that Davey uses to explain behavioral ontogeny.

The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country and inferred the influence of the policies on the economy. The task was challenging and ambiguous, allowing participants to interpret the relations between the policies and the economy in multiple ways. In three studies, we found evidence of motivated reasoning despite financial incentives for accuracy. For (...) example, participants who believed that border security funding should be increased were more likely to conclude that increasing border security funding actually caused a better economy in the task. In Study 2, we hypothesized that having neutral preferences (e.g., preferring neither increased nor decreased spending on border security) would lead to more accurate assessments overall, compared to having a strong initial preference; however, we did not find evidence for such an effect. In Study 3, we tested whether providing participants with possible functional forms of the policies (e.g., the policy takes some time to work or initially has a negative influence but eventually a positive influence) would lead to a smaller influence of motivated reasoning but found little evidence for this effect. This research advances the field of causal learning by studying the role of prior preferences, and in doing so, integrates the fields of causal learning and motivated reasoning using a novel explore‐exploit task. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

Prelinguistic infants must find a way to isolate meaningful chunks from the continuous streams of speech that they hear. BootLex, a new model which uses distributional cues to build a lexicon, demonstrates how much can be accomplished using this single source of information. This conceptually simple probabilistic algorithm achieves significant segmentation results on various kinds of language corpora - English, Japanese, and Spanish; child- and adult-directed speech, and written texts; and several variations in coding structure - and reveals which statistical (...) characteristics of the input have an influence on segmentation performance. BootLex is then compared, quantitatively and qualitatively, with three other groups of computational models of the same infant segmentation process, paying particular attention to functional characteristics of the models and their similarity to human cognition. Commonalities and contrasts among the models are discussed, as well as their implications both for theories of the cognitive problem of segmentation itself, and for the general enterprise of computational cognitive modeling. (shrink)