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Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between‐subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk‐seeking behavior among participants. We developed cognitive and heuristics models using instance‐based learning theory and natural mean heuristic. Results reveal models' dependency on recent and frequent observations during information sampling. These experience‐based cognitive models could help build artificial intelligence models with fewer preference reversals. This paper focuses on investigating the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Furthermore, Uttrani et al. develop cognitive models using instance‐based learning theory and natural mean heuristic to account for human choices across different problem frames and types in the experience format. [ABSTRACT FROM AUTHOR]