04) between decreasing behavioral loss aversion and the level of

04) between decreasing behavioral loss aversion and the level of incentive resulting in peak behavioral performance in the hard difficulty level ( Figure 5C), but not in the easy difficulty level (r = 0.24; p = 0.19). Those participants with greater behavioral loss aversion exhibited peak performance at lower incentive levels and more impaired performance for high incentives. The additional group of participants (n = 20) exhibited a wide range of λ’s and separating these participants based on the degree of their loss aversion,

we found that those Trametinib manufacturer that were less loss averse followed a monotonic response to incentives, whereas more loss averse participants exhibited the paradoxical response to incentives ( Figure 5D). These results provide evidence that participants frame their performance for incentives, during highly skilled tasks, in terms of the loss of a presumed gain that would arise from failure. Moreover, this encoding of loss aversion drives participants’ behavioral performance for incentive. Loss aversion represents a tendency to value losses greater than equal magnitude gains. Risk aversion, on the other hand, is a more general aversion to increased variance in potential gains or losses. To ensure a loss aversion-based hypothesis and not a general aversion to risk was responsible for our findings, we had participants in the follow-up experiment (n = 20)

perform another decision-making task in which they made choices regarding risky gambles that did not include potential losses. Using participants’ responses from this task we were able to calculate a measure α Everolimus Linifanib (ABT-869) that represented their risk aversion. Participants had a median α

estimate of 0.83 (IQR 0.20), indicating that they were on average risk averse. Importantly, no significant correlations were found between our behavioral measures of performance and risk aversion (Table 1). This provides further evidence that an individual’s incentive resulting in peak performance and her performance decrements for large incentives are due specifically to loss aversion. Given that the striatum is also known to encode signals resembling a rewarded prediction error (McClure et al., 2003, O’Doherty et al., 2003 and Pagnoni et al., 2002), we performed a simulation to determine if the deactivations observed during the motor task could be elicited as a byproduct of prediction error signaling. For this analysis we considered a temporal difference (TD) model of prediction error (PE), where a prediction error δ was generated from a difference between a predicted value V(t) at time t and a predicted value V(t + 1) at time t + 1 ( Sutton and Barto, 1990): δ=V(t+1)−V(t).δ=V(t+1)−V(t). In our experiment, participants trained the day before the rewarded portion of the experiment and thus generated an expectation of their probability of success given a presented target size, and an average probability of success over all trials.

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