observational and interventional learning of a simple causal chain, and to ascertain whether people represent their interventions in accordance with the normative model proposed by Pearl (2000). In the observation condition people treated putative causes as independent, and systematically selected the wrong model. In the intervention condition performance improved, in particular greater sensitivity was shown to the relevant conditional independencies. However, participants' likelihood judgments approximated the observed frequencies rather than reflecting the appropriate causal model.

The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed (“seeing”) or was actively manipulated (“doing”). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency. Causal knowledge underlies our ability to predict future events, to explain the occurrence of present events, and to achieve goals by means of actions. Thus, causal knowledge belongs to one of our most central cognitive competencies. However, the nature of causal knowledge has been debated. A number of philosophers and

There are many psychological tasks that involve the pairing of binary variables. The various tasks used often address different questions and are motivated by different theoretical issues and traditions. Upon closer examination, however, the tasks are remarkably similar in structure. In the present paper, we examine two such tasks, the contingency judgment task and the signal detection task, and we apply a signal detection analysis to contingency judgment data. We suggest that the signal detection analysis provides a novel interpretation of a well-established but poorly understood phenomenon of contingency judgments—the outcome-density effect. We must often make a decision even though the information we have is ambiguous or uncertain. One such situation is illustrated by a patient being treated by an allergist. The patient sometimes, but not always, develops hives after eating strawberries. Moreover, the patient sometimes develops hives even when strawberries are not eaten. Although the relationship between eating strawberries and developing hives is uncertain, the allergist must decide whether or not to recommend that the patient stop eating strawberries. Another type of ambiguous situation is illustrated by the task confronted by the radiologist. The radiologist must decide whether or not an X-ray indicates the presence of lung cancer. The signals seen in the X-ray are ambiguous, some consistent with lung cancer and others inconsistent with lung cancer. Even though the correct diagnosis is unclear, the radiologist must decide whether or not to recommend treatment. Despite the obvious similarities between the tasks, they have been treated quite differently. The allergy task has often been used by researchers interested in contingency assessment; that is, how humans judge that a cue (strawberry ingestion) imperfectly signals an outcome (see

How do humans discover causal relations when the effect is not immediately observable? Previous experiments have uniformly demonstrated detrimental effects of outcome delays on causal induction. These findings seem to conflict with everyday causal cognition, where humans can apparently identify long-term causal relations with relative ease. Three experiments investigated whether the influence of delay on adult human causal judgments is mediated by experimentally induced assumptions about the timeframe of the causal relation in question, as suggested by Einhorn & Hogarth (1986). Causal judgments generally decreased when a delay separated cause and effect. This decrease was less pronounced when the thematic context of the causal relation induced participants to expect a delay. Experiment 3 ruled out an alternative explanation of the effect based on variations of cue and outcome saliencies, and showed that detrimental effects of delay are reduced even more when instructions explicitly mentioned the timeframe of the causal relation in question. Knowledge thus mediates the impact of

Associative learning theory postulates two main determinants for human causal learning: contingency and contiguity. In line with such an account, participants in Shanks, Pearson, and Dickinson (1989) failed to discover causal relations involving delays of more than two seconds. More recent research has shown that the impact of contiguity and delay is mediated by prior knowledge about the timeframe of the causal relation in question. Buehner and May (2002, 2003) demonstrated that the detrimental effect of delay can be significantly reduced if reasoners are aware of potential delays. Here we demonstrate for the first time that the negative influence of delay can be abolished completely by a subtle change in the experimental instructions. Temporal contiguity is thus not essential for human causal learning. An associative learning analysis of human causal learning postulates two main determinants of judged causal strength: the contingency and the contiguity between the potential cause (cue) and the effect (outcome) (e.g., see Shanks & Dickinson, 1987). Empirical research in the last decades has largely focused on the congruency between cue–outcome contingency and judged causal strength. Early reports suggested that human causal judgements closely track variations in cue-outcome contingency (e.g., Jenkins & Ward, 1965), while more recent studies revealed a more complex picture (e.g., Chapman & Robbins, 1990). In fact, the theoretical and empirical relations between contingency and judged causality are still the subject of a hot debate

Two experiments are reported that examine the role of temporal contiguity on judgments of contingency in a human analogue of the Pavlovian task. The data show that the effect of the actual delay on contingency judgment depends on the observers expectation regarding the delay. For a fixed contingency between the cue and the outcome, ratings of the contingency are higher when the actual delay is congruent with the observers expectation than when it is incongruent. We argue that our data can be understood within the context of the temporal coding hypothesis. There is considerable evidence of similarities between the operations that modulate the strength of conditioning in nonhuman animals and those that modulate the rating of the contingency between events by humans (see Allan, 1993). One of these similarities is the effect of temporal contiguity. It is well established in the animal literature that temporal contiguity is an important variable in both instrumental and Pavlovian conditioning (see Allan, Balsam, Church, & Terrace, 2002; Allan & Church, 2002). For example, increasing the delay between a response and reinforcement in an instrumental task decreases the rate of responding. Similarly, increasing the delay between a conditioned stimulus and an unconditioned stimulus in a Pavlovian task retards the acquisition of the conditioned response. The studies that have examined the effect of temporal contiguity on ratings of contingency have used human analogues of the animal instrumental procedure (e.g., Buehner & May,

Three experiments investigated the impact of delay on human causal learning. We present a new paradigm based on the presentation of continuous event streams, and use it to test two hypotheses drawn from associative learning theories of causal inference. Unlike free-operant procedures traditionally used to study temporal aspects of causal learning (Shanks, Pearson, & Dickinson, 1989; Shanks & Dickinson, 1987; Buehner & May, 2002, 2003, 2004), the procedure employed here allows full control over all aspects of stimulus delivery while at the same time overcoming the ecologically invalid notion of discrete learning trials. Results show that delays generally impair causal learning, but prior knowledge and experience mediate this detrimental effect. In accordance with associative learning theory, pre-exposure to an unreinforced background context facilitates the discovery of delayed causal relationships. However, contrary to associative learning theory, increasing the amount of experience with a delayed causal relationship does not improve discovery. Implications for associative learning and causal model theories are discussed.

Reinforcement learning (RL) uses sequential experience with situations (‘‘states’’) and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task, we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior.

Associative processes build the structural-representational framework upon which cognitive processes of computation and inference can act. I review evidence I have collected showing how associative processes are involved in building spatial, temporal, and causal maps. Evidence comes from studies on simple associative acquisition such as Pavlovian and instrumental conditioning, higher-order conditioning procedures such as sensory preconditioning and conditioned inhibition, and from cue-competition studies. Parallels are drawn between acquisition and integration of information in conventional associative paradigms on the one hand and cognitive paradigms on the other. 1

Many studies of contingency judgments have used a task in which, on each trial, the participant is free either to respond or not to respond, and an outcome may, or may not, be presented. Typically, the experimenter specifies a nominal value for the contingency between responding and outcome, but the actual values of a variety of variables experienced by a particular participant depend on that participant’s frequency of responding. The results of computer simulations of various strategies for implementing the contingency manipulation, and the results of an experiment, indicate that the same nominal contingency value will lead to considerable variability in the actual contingency experienced by participants. Moreover, nominal contingency manipulations are confounded with the probability that the subject experiences an outcome. While researchers might be aware of these issues, not enough attention has been paid to their potential impact. © 2006 Published by Elsevier B.V.