Does good art take effort or talent?

People appreciate objects more when they know that the maker has invested a lot of time and effort in them. People assume that good work takes time and effort, so they assume that if someone has invested a lot of effort, the work will have to be good in the end. However, we cannot predict what people will infer just by knowing what information they base their judgements on. In order to make a quality assessment based on that information, they must have background knowledge that links the input to the assessment. This is where the term naive theories arises.

What is a naive theory?

A naive theory is a coherent set of knowledge and beliefs about a specific content domain, containing ontological commitments, attention to domain-specific causal principals, and appeal to unobservable entities. A first naive theory states that high-quality products require good work, which requires a lot of effort. This is also called the effort heuristic. Another possible naive theory states that talented makers need to use less effort than untalented makers to achieve the same quality. If someone uses this naive theory, the creator's perceived high talent can lead to doubts about his effort. The effect of the effort heuristic is then no longer visible and it could even lead to a lower quality assessment.

Which naive theories are confirmed by the research?

The inferences people make based on certain information depends on the naive theory they use. A first naive theory is the effort heuristic, which states that good quality requires high effort and assesses quality based on effort. However, another naive theory is that talented creators need to invest less effort than less talented creators. The existence of both naive theories was confirmed. The application of different inference rules to the same input data resulted in different judgements.

What are the implications for future research?

After priming "high stake is high value", the effort heuristic was used. When the talent theory was primed, high stakes led to lower quality ratings. So just knowing what information people base their ratings on is not enough to predict their ratings. Instead, the rules of inference that people use must be understood. These inference rules can be seen as naive theories linking input (information) to output (quality assessment).