The property generation task (i.e. “feature listing”) is often assumed to measure concepts. Typically, researchers assume implicitly that the underlying representation of a concept consists of amodal propositions, and that verbal responses during property generation reveal their conceptual content. The experiments reported here suggest instead that verbal responses during property generation reflect two alternative sources of information: the linguistic form system and the situated simulation system. In two experiments, properties bearing a linguistic relation to the word for a concept were produced earlier than properties not bearing a linguistic relation, suggesting the early properties tend to originate in a word association process. Conversely, properties produced later tended to describe objects and situations, suggesting that late properties tend to originate from describing situated simulations. A companion neuroimaging experiment reported elsewhere confirms that early properties originate in language areas, whereas later properties originate in situated simulation areas. Together, these results, along with other results in the literature, indicate that property generation is a relatively complex process, drawing on at least two systems somewhat asynchronously.

Theories typically emphasize affordances or intentions as the primary determinant of an object's perceived function. The HIPE theory assumes that people integrate both into causal models that produce functional attributions. In these models, an object's physical structure and an agent's action specify an affordance jointly, constituting the immediate causes of a perceived function. The object's design history and an agent's goal in using it constitute distant causes. When specified fully, the immediate causes are sufficient for determining the perceived function--distant causes have no effect (the causal proximity principle). When the immediate causes are ambiguous or unknown, distant causes produce inferences about the immediate causes, thereby affecting functional attributions indirectly (the causal updating principle). Seven experiments supported HIPE's predictions.

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We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p= .05 to .005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of .05, .01, .005, or anything else, is not acceptable.