The very famous “oddball” paradigm examines people's brain responses following low-probability events compared to more frequent events. The typical result is an elevated fronto-central signal approximately 300 ms following the rare event, which is known as P300. The original oddball paradigm normally required a response following the rare event, and thus confounded the rarity of the event and its performance requirements (Squires et al., 1975). Yet the same findings were also replicated in task-irrelevant rare events (Debener et al., 2005) and in reward prediction tasks (Karis et al., 1983). The elevated neural activation following the rare event appears to be inconsistent with the tendency to underweight rare events in experience-based decisions. One might argue, though, that the dependent variable in decision tasks (i.e., the choice proportion) is also affected by the size of the rare payoff, and this component may be insufficiently integrated upon making choices. Still, this would be inconsistent with the standard way in which the underweighting phenomenon is explained (Hertwig et al., 2004). Moreover, as we shall see below, inconsistency between brain activation and behavior also emerges when the target event (e.g., a loss) is similar in size to the control event (e.g., a gain).

In a seminal EEG study, Gehring and Willoughby (2002) demonstrated that a large portion of the frontal cortex exhibits greater rapid activation following losses than following equivalent gains. This event-related brain potential (ERP) has been referred to as medial frontal negativity (MFN; Gehring and Willoughby, 2002) or fERN (Yeung and Sanfey, 2004). Gehring and Willoughby (2002) suggested that the existence of the increased cortical response following losses is consistent with the behavioral principle of “loss aversion,” which denotes an increased subjective weight of losses compared to gains (Kahneman and Tversky, 1979). Furthermore, they argued that the asymmetric fERN response to losses represents the brain mechanism directly contributing to the representation of subjective value, or “instant utility.” However, in Gehring and Willoughby's (2002) research, as in other studies of experience-based decisions (e.g., Erev et al., 2008; Silberberg et al., 2008; Hochman and Yechiam, 2011; Yechiam and Telpaz, 2011, in press), participants actually made behavioral choices as if they were loss neutral and not loss-averse. Specifically, the fERN was observed in choices between an alternative producing +5 or −5 US cents and a second alternative producing +5 or −25 cents. Loss aversion implies that in choice between a pair of alternatives with symmetric gains and losses, individuals should prefer the alternative producing lower losses. However, the two choice alternatives were selected at about the same rate.

This gap was replicated in follow-up ERP studies, some using higher payoffs (e.g., Nieuwenhuis et al., 2004; Yeung and Sanfey, 2004; Masaki et al., 2006). It has also been demonstrated in studies of autonomic arousal: Losses were found to have greater effect on pupil diameter and heart rate, even in the absence of reliable loss aversion (Hochman and Yechiam, 2011; Yechiam and Telpaz, 2011). Interestingly, the ERP studies noted above did not make a big issue out of this discrepancy, and did not consider it as deserving special attention. Yet we feel that it is a critical issue because participants make experiential choices as if they are loss neutral while their brain acts as if it is loss sensitive.

The third brain–behavior gap we would like to draw attention to involves a discrepancy between what is known about the dynamic function of the dopaminergic system and experiential choice behavior. Under current models of the striatum, the dopaminergic system adapts to reinforcement using a “Delta” learning rule (Rumelhart et al., 1986), whereby a new outcome from a given alternative is compared to the expectancy of the alternative prior to the outcomes, and the expectancy is updated as a function of the difference between the new outcome and the old expectancy. Brain areas within the striatum were found to exhibit activation patterns consistent with the implied mechanism of the delta rule (Schultz et al., 1997). This rule axiomatically leads to a clear behavioral prediction involving the effect of experience on risk taking behavior (Niv et al., 2002; Denrell, 2007): Under the Delta rule, decision makers should be more risk averse with experience because they invariably sample the risky alternative in a biased fashion. For example, say that in an experience-based decision you have a choice between getting zero for sure and a risky alternative producing equal chances to win or lose $10. In the first 10 trials in which the risky alternative is chosen there is about 40% chance that in reality the likelihood of winning $10 will be less than 50%. This can lead participants to avoid the risky alternative in future choices. Moreover, as soon as the risky alternative is avoided, there is no mechanism that reduces this bias. The chance of biased sampling from the risky alternative continues in the next trials as well, and under the Delta learning rule, it is expected to slowly lead participants away from the risky alternative. But this is not how participants behave in experiential tasks! In several studies involving such symmetric risky alternative (with equal chance of winning or losing) participants were found to show remarkable flatness in their learning curves (see Figure 1). This gap between the mechanism considered to govern learning and actual experiential behavior in tasks involving mixed gains and losses bears some similarity to the issue of loss sensitivity because in both cases the brain activation pattern is in the direction of avoiding risk (and potential losses) while behavior leans toward risk neutrality¹.