Table 1.

Overview of heuristic variables

Variable name	Explanation	Theoretically possible values of this variable in the task	Example value of this variable (as in Fig. 1 choice phase)	Grand mean of variable across fMRI participants (mean within participant SD)
Probability of foraging success (p with q = 1−p)	Momentary probability that the participant can gain a certain magnitude of energy points (vs. losing two energy points). The participant can infer this probability by counting the number of subfields with gains (i.e., subfields with blue dots in Fig. 1) in the grid that contains ten subfields.	0.1–0.9 in steps of 0.1	0.6	0.55 (0.24)
Magnitude of foraging gain g	Momentary magnitude of the possible gain if foraging is successful. This is depicted by the number of (blue) “gain dots” per subfield of the grid.	0–4 in steps of 1	1	1.97 (1.41)
Expected value (EV) of the foraging option	Momentary probability of foraging success multiplied by the corresponding magnitude of foraging gain g plus (1- probability of foraging success) multiplied by the loss incurred for unsuccessful foraging, which is always −2. The EV of the waiting option is always −1.	−1.8 to 3.8	−0.2	−0.14 (1.13)
Continuous energy state s	Current state of the energy bar. (An energy state of zero is synonymous with starvation and therefore participants cannot make choices at an energy state of zero.)	1–5 in steps of 1	2	2.97 (1.09)
Binary energy state	When the continuous energy state is one, waiting leads to sure death. In higher energy states, waiting will never lead to starvation. The variable “binary energy state” distinguishes between these situations (1=energy state is one; 0=energy state is two or higher).	Binary variable: 1 or 0	0 “waiting does not lead to starvation”	0.07 (0.26)
Weather type	Each forest type specifies two weather types that can be roughly classified as “good” or “bad” depending on whether they imply a lower or higher probability of starvation. Weather types are relative to each other (i.e., a given combination of p and g can be the “good” weather type if paired with a relatively worse weather type with lower p and g, or the “bad” weather type if paired with a relatively better weather type, higher p and g).	Categorical variable: 1 “bad” or 2 “good”	2 “good”	1.50 (0.5)
Days past in a forest (i.e., number of time steps t)	Participants remain within a given forest (i.e., mini-block) for up to 5 days (i.e., trials). The number of days is not explicitly depicted on the screen but participants can easily infer it by counting the number of choice phases after the last occurrence of the forest phase.	1–5 in steps of 1	1	1.57 (0.91)
Change between past and current energy states	Participants might track the difference between their energy states in the past trial and the current trial (within and across forests).	−2 to +4 in steps of 1 (maximum loss was 2 energy points & maximum gain was 4 energy points)	Not available in Fig. 1 because the change depends on the previous trial that is not depicted. In the next choice phase, the change in energy states is +1.	−0.90 (1.55)
“Win-stay-lose-shift” (WSLS) strategy	Participants might use a strategy, which prescribes foraging if the energy state increased with respect to the past trial and waiting if the energy state decreased. WSLS is a binarized version of the change between past and current energy states	1 “energy state increased” or 0 “energy state decreased”	Not available in Fig. 1 because previous trial not depicted. In next choice phase WSLS is 1 “energy state increased”	0.38 (0.48)