Stimulus uncertainty and relative reward rates determine adaptive responding in perceptual decision-making
Fig 2
a. Principle of condition design. In each condition, 3-4 out of a set of 5 stimuli were chosen (here, 3). Each stimulus (S) i was assigned to a response category (C) j and had its unique probability of presentation P(Si) and its unique reward probability P(Rew|Si) (rewards were given only when the response was correct). This example refers to the construction of condition “Lean L”. Solid lines in the left and middle bottom panels represent stimulus distributions (as in Fig 1A), bold dashed lines in the bottom right panel represent ‘decision distributions’, i.e., the distributions for each of the two categories (C) j ∈ {1;2} scaled by presentation and reward probability, i.e., for category 1, p(x|C1)*P(C1)*P(Rew|C1,R1). Note that the x-value at the intersection of the two decision distributions equals the optimal (reward-maximizing) criterion (see Methods, section “Criterion setting according to optimal account”). b. Steady-state criterion predictions of the three criterion-setting models and a reward-maximizing account for all experimental conditions. Bold dashed lines in each panel represent the decision distributions. Solid vertical lines denote the steady-state criterion predictions of the three models and a reward-maximizing account. The parameters used for this example are γ = 0.99, δ = υ = 0.04. See Table 3 and S1 Fig for more details on each condition.