Abstract

Two kinds of probability expressions, verbal and numerical, have been used to characterize the uncertainty that people face. However, the question of whether verbal and numerical probabilities are cognitively processed in a similar manner remains unresolved. From a levels-of-processing perspective, verbal and numerical probabilities may be processed differently during early sensory processing but similarly in later semantic-associated operations. This event-related potential (ERP) study investigated the neural processing of verbal and numerical probabilities in risky choices. The results showed that verbal probability and numerical probability elicited different N1 amplitudes but that verbal and numerical probabilities elicited similar N2 and P3 waveforms in response to different levels of probability (high to low). These results were consistent with a levels-of-processing framework and suggest some internal consistency between the cognitive processing of verbal and numerical probabilities in risky choices. Our findings shed light on possible mechanism underlying probability expression and may provide the neural evidence to support the translation of verbal to numerical probabilities (or vice versa).

Highlights

  • The 2 × 3 repeated measures ANOVAs conducted on the reaction time (RT) revealed a significant main effect of probability expression, F(1,24) = 25.55, p < 0.001, η2 = 0.52; and a significant main effect of level of probability, F(2,48) = 10.04, p = 0.001, η2 = 0.30

  • Post hoc LSD analyses revealed that participants took a longer time in the verbal probability condition than in the numerical probability condition (p < 0.001)

  • A simple effect analysis indicated that the N1 amplitude elicited by the verbal probability was greater than that elicited by the numerical probability at the Oz electrode (p = 0.001; Figure 2), but the opposite pattern was observed at the Fz electrode (p = 0.03)

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Summary

Introduction

People have to make decisions under uncertainty because they usually cannot control all the factors influencing the consequences of their decisions. Two kinds of probability expressions, verbal and numerical, have been used to characterize the uncertainty that we face. The verbal mode of probability expressions has a long history and has been considered the more natural system for processing probabilistic information (Zimmer, 1984). The numerical mode of probability expressions was first invented by legal scholars and later connected to the mathematical games of chance in the seventeenth century (Shafer, 1988). Numerical probability was suggested to be more accurate and to leave less room for subjective interpretation (Bonnefon and Villejoubert, 2006). The emergence of numerical probability enabled the development of Bayesian analysis

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