Reward Count(s): Negative Recency in Probabilistic Experience-Based Learning

Learning how to make decisions from experience is often studied using probabilistic outcome prediction or choice tasks, as
in conditioning, reward learning, or risky gambles (e.g., response A provides reward in 75% of the cases, response B in
25% over repeated trials with feedback). One debated phenomenon in such tasks is that of negative recency, describing
that learners expect the rare event after observing a streak of
common events (e.g., Gamblers fallacy). Here, we show that
this behavior, despite instructing participants to use a visual
stimulus, also occurs in probabilistic single-cue conditioning
training, where participants predicted whether digging at a specific location on a plane (visual cue) leads to finding a Vase or
Nothing (events), when they received reward for correct predictions. We manipulated reward magnitude in three conditions (equal for both common and rare events vs. high for
common event vs. high for rare event, between factor). We
further manipulated whether the label of the rare event was
framed as event (finding a Vase) or non-event (finding Nothing;
between factor). The results suggest, that reward magnitude
affected the emergence of negative recency, being most prevalent when correctly predicting the rare event yielded a high
reward, and least prevalent when the common event yielded
a high reward. Interestingly, the event label instead rather affected when the rare event was expected, such that common
Vase runs were expected to end earlier than common Nothing
runs. We discuss the findings from conditioning and economic
perspectives, generally concerning experience-based learning.