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Invariant information preferences across reward valence and magnitude

Shi Xian Liew
University of New South Wales ~ Psychology
Prof. Ben Newell
UNSW Sydney ~ Psychology

Most theoretical accounts of non-instrumental information seeking suggest that the nature of future rewards has a direct impact on the attractiveness of the information in two specific ways. First, positively valenced outcomes (e.g., monetary gains) are predicted to result in preference for information, while negatively valenced outcomes (e.g., monetary losses) are predicted to result in information avoidance. Second, the magnitude of rewards is assumed to be proportional to the strength of information seeking (or avoidant) behaviour. In a series of experiments using both primary and secondary reinforcers, we explore the extent to which observed information seeking behaviour tracks these predictions. Our findings indicate a robust independence of information seeking from outcome valence and magnitude with preferences for information largely remaining constant across different reward valence and magnitudes. We discuss these results in the context of current computational models with suggestions for future theoretical and empirical work.



information seeking
reward valence
reward magnitude
computational modeling


Mathematical Psychology

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Cite this as:

Liew, S., & Newell, B. (2021, February). Invariant information preferences across reward valence and magnitude. Paper presented at Australasian Mathematical Psychology Conference 2021. Via