How does a bicycle work? A new instrument to assess mechanical reasoning in school aged children

This study demonstrated that a brief interview can reveal the mechanical reasoning that could not be assessed via the Bicycle Drawing Test. This study, conducted on 190 children (6 to 11 years old), shows that mechanical reasoning improves with age. It shows correlations with spatial reasoning and motor control, and with visual reasonin

Norms and the meaning of omissive enabling conditions

People often reason about omissions. One line of research shows that people can distinguish between the semantics of omissive causes and omissive enabling conditions: for instance, not flunking out of college enabled you (but didn’t cause you) to graduate. Another line of work shows that people rely on the normative status of omissive events in inferring their causal role: if the outcome came about because the omission violated some norm, reasoners are more likely to select that omission as a cause. We designed a novel paradigm that tests how norms interact with the semantics of omissive enabling conditions. The paradigm concerns the circuitry of a mechanical device that plays music. Two experiments used the paradigm to stipulate norms and present a distinct set of possibilities to participants. Participants chose which causal verb best described the operations of the machine. The studies revealed that participants’ responses are best predicted by their tendency to consider the semantics of omissive relations. In contrast, norms had little to no effect in participants’ responses. We conclude by marshaling the evidence and considering what role norms may play in people’s understanding of omissions

Hume’s Theory of Causation: Is There More Than One?

It is traditionally assumed that there is only one theory of causality in Hume's writings. In this article it is shown that we can distinguish between an early and mature theory. It is argued that the mature theory, strongly influenced by Newton's physics, accords with the New Hume interpretation by asserting that real causal relations are not accessible to the human mind

Motion as manipulation: Implementation of motion and force analogies by event-file binding and action planning\ud

Tool improvisation analogies are a special case of motion and force analogies that appear to be implemented pre-conceptually, in many species, by event-file binding and action planning. A detailed reconstruction of the analogical reasoning steps involved in Rutherford's and Bohr's development of the first quantized-orbit model of atomic structure is used to show that human motion and force analogies generally can be implemented by the event-file binding and action planning mechanism. Predictions that distinguish this model from competing concept-level models of analogy are discussed, available data pertaining to them are reviewed, and further experimental tests are proposed

Explaining Explanation

It is not a particularly hard thing to want or seek explanations. In fact, explanations seem to be a large and natural part of our cognitive lives. Children ask why and how questions very early in development and seem genuinely to want some sort of answer, despite our often being poorly equipped to provide them at the appropriate level of sophistication and detail. We seek and receive explanations in every sphere of our adult lives, whether it be to understand why a friendship has foundered, why a car will not start, or why ice expands when it freezes. Moreover, correctly or incorrectly, most of the time we think we know when we have or have not received a good explanation. There is a sense both that a given, successful explanation satisfies a cognitive need, and that a questionable or dubious explanation does not. There are also compelling intuitions about what make good explanations in terms of their form, that is, a sense of when they are structured correctly

Grounding-mechanical explanation

Characterization of a form of explanation involving grounding on the model of mechanistic causal explanation

Learning why things change: The Difference-Based Causality Learner

In this paper, we present the Difference-Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL's correctness for learning structure from time series data, an endeavour that is complicated by the existence of latent derivatives that have to be detected. We also prove that, under common assumptions for causal discovery, DBCL will identify the presence or absence of feedback loops, making the model more useful for predicting the effects of manipulating variables when the system is in equilibrium. We argue analytically and show empirically the advantages of DBCL over vector autoregression (VAR) and Granger causality models as well as modified forms of Bayesian and constraintbased structure discovery algorithms. Finally, we show that our algorithm can discover causal directions of alpha rhythms in human brains from EEG data