"Attribute-framing bias" reflects people's tendency to evaluate objects framed positively more favorably than the same objects framed negatively. Although biased by the framing valence, evaluations are nevertheless calibrated to the magnitude of the target attribute. In three experiments that manipulated magnitudes in different ways, we…

People often rely on the covariation between events to infer causality. However, covariation between cues and outcomes may change over time. In the associative learning literature, extinction provides a model to study updating of causal beliefs when a previously established relationship no longer holds. Prediction error theories can explain both…

Stimulus-response binding and retrieval (SRBR) is a fundamental mechanism driving behavior automatization. In five experiments, we investigated the modulatory role of affective consequences (AC) on SRBR effects to test whether binding/retrieval can explain instrumental learning (i.e., the "law of effect"). SRBR effects were assessed in a…

In schema-based source monitoring, people mistakenly predict better source memory for expected sources (e.g., oven in the kitchen; "expectancy effect"), whereas actual source memory is better for unexpected sources (e.g., hairdryer in the kitchen; "inconsistency effect"; Schaper et al., 2019b). In three source-monitoring…

Attribute-framing bias (AFB) refers to addressees' bias in evaluating positively framed objects (80% success) more favorably than negatively framed ones (20% failure), although they are logically equivalent. The novelty of the current study is in examining conditions in which AFB occurs or does not occur. Typically, AFB is examined for favorable…