Is causal induction based on causal power? Critique of Cheng (1997)

The authors empirically evaluate P. W. Cheng's (1997) power PC theory of causal induction. They reanalyze some published data taken to support the theory and show instead that the data are at variance with it. Then, they report 6 experiments in which participants evaluated the causal relationship between a fictitious chemical and DNA mutations. The power PC theory assumes that participants' estimates are based on the causal power p of a potential cause, where p is the contingency between the cause and the effect normalized by the base rate of the effect. Three of the experiments used a procedure in which causal information was presented trial by trial. For these experiments, the power PC theory was contrasted with the predictions of the probabilistic contrast model and the Rescorla-Wagner theory. For the remaining 3 experiments, a summary presentation format was employed to which only the probabilistic contrast model and the power PC theory are applicable. The power PC theory was unequivocally contradicted by the results obtained in these experiments, whereas the other 2 theories proved to be satisfactory.     

Rethinking temporal contiguity and the judgement of causality: effects of prior knowledge, experience, and reinforcement procedure.

Time plays a pivotal role in causal inference. Nonetheless most contemporary theories of causal induction do not address the implications of temporal contiguity and delay, with the exception of associative learning theory. Shanks, Pearson, and Dickinson (1989) and several replications (Reed, 1992, 1999) have demonstrated that people fail to identify causal relations if cause and effect are separated by more than two seconds. In line with an associationist perspective, these findings have been interpreted to indicate that temporal lags universally impair causal induction. This interpretation clashes with the richness of everyday causal cognition where people apparently can reason about causal relations involving considerable delays. We look at the implications of cause-effect delays from a computational perspective and predict that delays should generally hinder reasoning performance, but that this hindrance should be alleviated if reasoners have knowledge of the delay. Two experiments demonstrated that (1) the impact of delay on causal judgement depends on participants' expectations about the timeframe of the causal relation, and (2) the free-operant procedures used in previous studies are ill-suited to study the direct influences of delay on causal induction, because they confound delay with weaker evidence for the relation in question. Implications for contemporary causal learning theories are discussed.     

Cerebellar Substrates for Error Correction in Motor Conditioning

The authors evaluate a mapping of Rescorla and Wagner's (1972) behavioral model of classical conditioning onto the cerebellar substrates for motor reflex learning and illustrate how the limitations of the Rescorla-Wagner model are just as useful as its successes for guiding the development of new psychobiological theories of learning. They postulate that the inhibitory pathway that returns conditioned response information from the cerebellar interpositus nucleus back to the inferior olive is the neural basis for the error correction learning proposed by Rescorla and Wagner (Gluck, Myers, & Thompson, 1994; Thompson, 1986). The authors' cerebellar model expects that behavioral processes described by the Rescorla-Wagner model will be localized within the cerebellum and related brain stem structures, whereas behavioral processes beyond the scope of the Rescorla-Wagner model will depend on extracerebellar structures such as the hippocampus and related cortical regions. Simulations presented here support both implications. Several novel implications of the authors' cerebellar error-correcting model are described including a recent empirical study by Kim, Krupa, and Thompson (1998), who verified that suppressing the putative error correction pathway should interfere with the Kamin (1969) blocking effect, a behavioral manifestation of error correction learning. The authors also discuss the model's implications for understanding the limits of cerebellar contributions to associative learning and how this informs our understanding of hippocampal function in conditioning. This leads to a more integrative view of the neural substrates of conditioning in which the authors' real-time circuit-level model of the cerebellum can be viewed as a generalization of the long-term memory module of Gluck and Myers' (1993) trial-level theory of cerebellar-hippocampal interaction in motor conditioning.     

Mechanisms of predictive and diagnostic causal induction

In predictive causal inference, people reason from causes to effects, whereas in diagnostic inference, they reason from effects to causes. Independently of the causal structure of the events, the temporal structure of the information provided to a reasoner may vary (e.g., multiple events followed by a single event vs. a single event followed by multiple events). The authors report 5 experiments in which causal structure and temporal information were varied independently. Inferences were influenced by temporal structure but not by causal structure. The results are relevant to the evaluation of 2 current accounts of causal induction, the Rescorla-Wagner (R. A. Rescorla & A. R. Wagner, 1972) and causal model theories (M. R. Waldmann & K. J. Holyoak, 1992).     

Callous-Unemotional Traits and Subtypes of Conduct Disorder

There has been growing consensus that children with conduct disorder (CD) constitute a very heterogeneous group containing children who vary substantially on the development, course, and causes of the disorder. While many have recognized the importance of this heterogeneity for developing better causal theories and for developing more effective treatments, there has been little consensus as to the best way to subtype children with CD. In this paper, we review a number of approaches to subtyping, each with some evidence for its validity for certain purposes. We focus on two recent approaches that have great potential for integrating past subtyping approaches and for advancing causal theory. The first approach is the division of children with CD into those with a childhood onset to their severe antisocial behavior and those with an adolescent onset to their behavior. The second approach is to designate children within the childhood-onset group who show callous and unemotional traits, which is analogous to adult conceptualizations of psychopathy. Both approaches help designate children who many show different causal processes underlying their severe aggressive and antisocial behavior, and who may warrant different approaches to treatment.     

Cue interaction and judgments of causality: Contributions of causal and associative processes

In four experiments, the predictions made by causal model theory and the Rescorla-Wagner model were tested by using a cue interaction paradigm that measures the relative response to a given event based on the influence or salience of an alternative event. Experiments 1 and 2 uncorrelated two variables that have typically been confounded in the literature (causal order and the number of cues and outcomes) and demonstrated that overall contingency judgments are influenced by the causal structure of the events. Experiment 3 showed that trial-by-trial prediction responses, a second measure of causal assessment, were not influenced by the causal structure of the described events. Experiment 4 revealed that participants became less sensitive to the influence of the causal structure in both their ratings and their predictions as trials progressed. Thus, two experiments provided evidence for high-level (causal reasoning) processes, and two experiments provided evidence for low-level (associative) processes. We argue that both factors influence causal assessment, depending on what is being asked about the events and participants' experience with those events.     

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.     

Parallel Neural Systems for Classical Conditioning: Support From Computational Modeling

Classical conditioning has been explained by two main types of theories that postulate different learning mechanisms. Rescorla and Wagner (1972) put forth a theory in which conditioning is based on the ability of the US to drive learning through error correction. Alternatively, Mackintosh (1973) put forth a theory in which the ability of the CS to be associated with the unconditioned stimulus is modulated. We have proposed a reconciliation of these two mechanisms as working in parallel within different neural systems: a cerebellar system for US modulation and a hippocampal system for CS modulation. We developed a computational model of cerebellar function in eyeblink conditioning based on the error correction mechanism of the Rescorla-Wagner rule in which learning-related activity from the cerebellum inhibits the inferior olive, which is the US input pathway to the cerebellum (Gluck et al., 1994). We developed a computational model of the hippocampal region that forms altered representations of conditioned stimuli based on their behavioral outcomes (Gluck & Myers, 1993; Myers et al., 1995). Overall, computational modeling and empirical findings support the idea that, at least in the case of eyeblink conditioning, there may be two different neural systems: the cerebellum which mediates US-based error correction and hippocampus which alters representations of CSs.     

The widespread influence of the Rescorla-Wagner model

The theory of Pavlovian conditioning presented by Robert Rescorla and Allan Wagner in 1972 (the Rescorla-Wagner model) has been enormously important in animal learning research. It also has been applied in a variety of areas other than animal learning. We summarize the contribution of the Rescorla-Wagner model to research in verbal learning, social psychology, human category learning, human judgments of correlational relationships, transitive inference, color aftereffects, and physiological regulation. We conclude that there have been few models in experimental psychology as influential as the Rescorla-Wagner model.     

Parallel neural systems for classical conditioning: Support from computational modeling

Classical conditioning has been explained by two main types of theories that postulate different learning mechanisms. Rescorla and Wagner (1972) put forth a theory in which conditioning is based on the ability of the US to drive learning through error correction. Alternatively, Mackintosh (1973) put forth a theory in which the ability of the CS to be associated with the unconditioned stimulus is modulated. We have proposed a reconciliation of these two mechanisms as working in parallel within different neural systems: a cerebellar system for US modulation and a hippocampal system for CS modulation. We developed a computational model of cerebellar function in eyeblink conditioning based on the error correction mechanism of the Rescorla-Wagner rule in which learningrelated activity from the cerebellum inhibits the inferior olive, which is the US input pathway to the cerebellum (Gluck et al., 1994). We developed a computational model of the hippocampal region that forms altered representations of conditioned stimuli based on their behavioral outcomes (Gluck & Myers, 1993; Myers et al., 1995). Overall, computational modeling and empirical findings support the idea that, at least in the case of eyeblink conditioning, there may be two different neural systems: the cerebellum which mediates US-based error correction and hippocampus which alters representations of CSs.     

Two Models of Moral Judgment

This paper compares two theories and their two corresponding computational models of human moral judgment. In order to better address psychological realism and generality of theories of moral judgment, more detailed and more psychologically nuanced models are needed. In particular, a motivationally based theory of moral judgment (and its corresponding computational model) is developed in this paper that provides a more accurate account of human moral judgment than an existing emotion‐reason conflict theory. Simulations based on the theory capture and explain a range of relevant human data. They account not only for the original data that were used to support the emotion – reason conflict theory, but also for a wider range of data and phenomena.     

The origins of inquiry: inductive inference and exploration in early childhood

Analogies between scientific theories and children's folk theories have been central to the study of cognitive development for decades. In support of the comparison, numerous studies have shown that children have abstract, ontologically committed causal beliefs across a range of content domains. However, recent research suggests that the comparison with science is informative not only about how children represent knowledge but also how they acquire it: many of the epistemic practices essential to and characteristic of scientific inquiry emerge in infancy and early childhood.     

Inductive reasoning about causally transmitted properties

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates’ context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.     

Analogical and category-based inference: a theoretical integration with Bayesian causal models

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality.     

Judging the Importance of Constant and Variable Candidate Causes: A Test of the Power PC Theory

In two causal induction experiments subjects rated the importance of pairs of candidate causes in the production of a target effect; one candidate was present on every trial (constant cause), whereas the other was present on only some trials (variable cause). The design of both experiments consisted of a factorial combination of two values of the variable cause's covariation with the effect and three levels of the base rate of the effect. Judgements of the constant cause were inversely proportional to the level of covariation of the variable cause but were proportional to the base rate of the effect. The judgements were consistent with the predictions derived from the Rescorla-Wagner (1972) model of associative learning and with the predictions of the causal power theory of the probabilistic contrast model (Cheng, 1997) or 'power PC theory'. However, judgements of the importance of the variable candidate cause were proportional to the base rate of the effect, a phenomenon that is in some cases anticipated by the power PC theory. An alternative associative model, Pearce's (1987) similaritybased generalization model, predicts the influence of the base rate of the effect on the estimates of both the constant and the variable cause.     

A computational unification of cognitive behavior and emotion

Existing models that integrate emotion and cognition generally do not fully specify why cognition needs emotion and conversely why emotion needs cognition. In this paper, we present a unified computational model that combines an abstract cognitive theory of behavior control (PEACTIDM) and a detailed theory of emotion (based on an appraisal theory), integrated in a theory of cognitive architecture (Soar). The theory of cognitive control specifies a set of required computational functions and their abstract inputs and outputs, while the appraisal theory specifies in more detail the nature of these inputs and outputs and an ontology for their representation. We argue that there is a surprising functional symbiosis between these two independently motivated theories that leads to a deeper theoretical integration than has been previously obtained in other computational treatments of cognition and emotion. We use an implemented model in Soar to test the feasibility of the resulting integrated theory, and explore its implications and predictive power in several task domains.     

Hierarchies,Networks, and Causality: The Applied Evolutionary Epistemological Approach

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds.

     

Resolving arguments by different conceptual traditions of realization

There is currently a significant amount of interest in understanding and developing theories of realization. Naturally arguments have arisen about the adequacy of some theories over others. Many of these arguments have a point. But some can be resolved by seeing that the theories of realization in question fall under different conceptual traditions with different but compatible goals. The arguments I will discuss fit a general pattern. A philosopher argues that one theory of realization is better than another because it provides a better explanation for a particular range of phenomena, say, accounting for common sense cases, or cases within the sciences, when in fact the theories in question are not genuine competitors. I will first describe three different conceptual traditions that are implicated by the arguments under discussion. I will then examine the arguments, from an older complaint by Norman Malcolm against a familiar functional theory to a recent argument by Thomas Polger against an assortment of theories that traffic in inherited causal powers, showing how they can be resolved by situating the theories under their respective conceptual traditions.