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Economic decision-making compared with an equivalent motor task

  1. Shih-Wei Wua,b,1,
  2. Mauricio R. Delgadoc and
  3. Laurence T. Maloneyb,d
  1. aDivision of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125;
  2. cDepartment of Psychology, Rutgers University, 101 Warren Street, Newark, NJ 07102; and
  3. bDepartment of Psychology and
  4. dCenter for Neural Science, New York University, 6 Washington Place, New York, NY 10003

  1. Edited by Avinash K. Dixit, Princeton University, Princeton, NJ, and approved February 23, 2009

  1. Fig. 1.
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    Fig. 1.

    Construction of a motor lottery and task design. (A) A stimulus configuration containing 2 non-zero monetary outcomes, O1 and O2, where O1 > O2 > O. The subject earned O1 by hitting the yellow bar, O2 by hitting either of the blue bars, and O if his/her movement endpoint fell elsewhere on the touch screen. The subject had to complete the pointing movement in <700 ms to avoid a large penalty. (B) From one subject with motor uncertainty σ = 6.57 mm, 623 movement endpoints were superimposed on the configuration. For this subject, this configuration was equivalent to a lottery (0.27,O1; 0.6,O2; 0.13,O). (C) Common consequence task. The bottom rung was the pair R (0.27,$24; 0.2,$20,0.53,0) and S (0.52,$20; 0.48,0). Each higher rung was constructed by adding a “common consequence” (0.2,$20) to both lotteries in the rung below it. The lottery in each pair with the higher probability of winning the highest amount ($24 in this example) and the zero outcome was designated as “riskier” (R), whereas the other lottery was designated as safer (S). If the subject's frequency of choosing the R lotteries is the same across rungs, his performance is consistent with the independence axiom.

  2. Fig. 2.
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    Fig. 2.

    Choice data in experiment 1. (A) Mean frequency (collapsed across subjects) of choosing the risky lottery fR was plotted as a function of rung. (B) Hypothetical choice patterns. The solid horizontal lines represent hypothetical patterns of frequencies that do not vary across rungs, consistent with the independence axiom. The dashed lines correspond to patterns of choice that violate the independence axiom. (C) Index of failure. For each subject, we plotted IF of the classical tasks (IFc) against IF of the motor tasks (IFm). IF was computed by taking the standard deviation of fR. The error bars correspond to ±1 SD computed by an application of Efron's bootstrap (27). (D) Averaged verbal estimates on the probability of hit (p̂) were plotted against estimates of probabilities (p) based on subjects' performance during training. The error bars represent ±1 SD of the subjects' verbal estimates.

  3. Fig. 3.
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    Fig. 3.

    CPT. (A) The value function in CPT is used to model the utility of outcome (O) in a lottery. It has the form v(O) = Oα. Different curves showed different values of α. (B) The probability weighting function (w) characterizes the distortion of probability information. Here we illustrate several possible shapes of the probability weighting function with the form w(p) = exp[−(−ln(p))γ], 0 < p < 1.

  4. Fig. 4.
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    Fig. 4.

    Parameter estimation results in experiment 2. (A) The estimate of the value function parameter, α, in the motor task (αmotor) is plotted against the parameter estimate in the classical economic task (αclassical). Each point represents a single subject. The error bars represent the bootstrap interquartile range. Ten of the 14 subjects' results are shown in the graph. The results for the other 4 subjects fall outside the area of the plot as indicated in the graph. (B) The estimated probability weighting function w(p) in the economic task is plotted for each subject (red). The dashed line indicates the identity line, whereas the black thick curve indicates the median w(p). (C) The estimated w(p) in the motor task is plotted for each subject (green). The black thick curve indicates the median w(p).

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