It is in principle possible that the bias in the observers' reports did not arise from the inference stage (which gives P(yes j C t ), or more strictly, P(yes j x t )), but from the subsequent decision stage, when a threshold value P th is chosen such that a response ''yes'' or ''no'' is given if P(yes j x t ) . P th or otherwise respectively [43]. The decision bias would be manifested in the choice of P th , e.g., P th ¼ 0.5, 0.1, or 0.9. Our experiments can not distinguish between these two types of biases. However, if the bias was indeed only in the decision (in terms of P th ), then the inference P(yes j x t ) is independent of the context. Without any insight on how contexts bias the decision threshold P th , the decision bias has to be modelled by introducing one model parameter for each contextual condition (defined by a particular combination of the configuration and contrast C c of the context), in addition to the model parameters for the unbiased inference P(yes j C t ) or P(yes j x t ) shared by all contextual conditions. Hence decision bias is a less parsimonious model to account for our **data** since it would require more model parameters than our model of inference bias. In addition, other than a numerical value P th , the decision bias does not give any insight in why and how the decision should be biased by context when the inference is unbiased. It is most likely that our measured yes rate results from the combined effect of (1) a context specific inference bias in the posterior P(yes j x t ), and (2) a context independent decision bias in P th arising from observers' wishes to give the ''yes'' response in roughly half of all trials. As our task can not distinguish between these two biases, our fitted values for P(yes) manifest the combined effect from both biases, as discussed in the Results section.