Theories of causal cognition describe how animals code cognitive primitives such as causal strength, directionality of relations, and other variables that allow inferences on the effect of interventions on causal links. We argue that these primitives and importantly causal generalization can be studied within an animal learning framework. Causal maps and other Bayesian approaches provide a normative framework for studying causal cognition, and associative theory provides algorithms for computing the acquisition of data-driven causal knowledge.

The causal power theory of the probabilistic contrast model (or power PC theory) of causal induction (Cheng, in press) states that estimates of the causal importance of a candidate cause are determined by the covariation between the cause and the effect and the probability of the effect as indexed by the probability of the effect in the absence of the cause. In two causal induction experiments we tested predictions derived from the equations of the power PC theory. In Experiment 1, the power PC theory predicted equivalent causal estimates in conditions where the probability of the effect given the presence of the cause, P(effect | cause), equalled 1 and in conditions where P(effect | cause) equalled 0. Judgments, however, differed significantly within these conditions and conformed to the predictions of a simpler contingency model. These prediction failures might be attributable to the particular values of P(effect | cause), and thus Experiment 2 set this probability to values other than 1 or 0. Causal judgments again disconfirmed the predictions of the power PC theory and this time significantly failed to conform to the predictions of a simple contingency model.

Two contingency judgment experiments are reported where one predictive cue was present on every trial of the task. This constant cue was paired with a second variable cue that was either positively correlated (Experiment 1) or negatively correlated with the outcome event (Experiment 2). Outcome base rate was independently varied in both experiments. Probabilistic contrasts could be calculated for the variable cue but not for the constant cue since the probability of the outcome occurring in the absence of the constant cue was undefined. Cheng & Holyoak's (1995) probabilistic contrast model therefore cannot uniquely specify the way in which the constant cue will be judged. In contrast, judgments of the constant cue were systematically influenced by the variable cue's contingency as well as by the outcome base rate. Specifically, judgments of the constant cue 1) were discounted when the variable cue was a positive predictor of the outcome but were enhanced when the variable cue was a negative predictor of the outcome, and 2) were proportional to the outcome base rate. These effects were anticipated by a connectionist network using the Rescorla-Wagner learning rule.

Modern societies can be understood as the intersection of four interdependent systems: (1) the natural environment of geography, climate and weather; (2) the built environment of cities, engineered systems, and physical infrastructure; (3) the social environment of human populations, communities and socio-economic activities; and (4) an information ecosystem that overlays the other three domains and provides the means for understanding, interacting with, and managing the relationships between the natural, built, and human environments. As the nation and its communities become more connected, networked and technologically sophisticated, new challenges and opportunities arise that demand a rethinking of current approaches to public safety and emergency management. Addressing the current and future challenges requires an equally sophisticated program of research, technology development, and strategic planning. The design and integration of intelligent infrastructure-including embedded sensors, the Internet of Things (IoT), advanced wireless information technologies, real-time data capture and analysis, and machine-learning-based decision support-holds the potential to greatly enhance public safety, emergency management, disaster recovery, and overall community resilience, while addressing new and emerging threats to public safety and security. Ultimately, the objective of this program of research and development is to save lives, reduce risk and disaster impacts, permit efficient use of material and social resources, and protect quality of life and economic stability across entire regions.

The propositional or rationalist Bayesian approach to learning is contrasted with an interpretation of causal learning in associative terms. A review of the development of the use of rational causal models in the psychology of learning is discussed concluding with the presentation of three areas of research related to cause-effect learning. We explain how rational context choices, a selective association effect (i.e., blocking of inhibition) as well as causal structure can all emerge from processes that can be modeled using elements of standard associative theory. We present the auto-associator (e.g., Baetu & Baker, 2009) as one such simple account of causal structure.