Table 1.

Probability Structure of Probabilistic Learning (Weather Prediction) Task.

	Cue
Cue Pattern	1	2	3	4	P(cue combination)	P(outcome)
1	0	0	0	1	.133	.150
2	0	0	1	0	.087	.385
3	0	0	1	1	.080	.083
4	0	1	0	0	.087	.615
5	0	1	0	1	.067	.200
6	0	1	1	0	.040	.500
7	0	1	1	1	.047	.143
8	1	0	0	0	.133	.850
9	1	0	0	1	.067	.500
10	1	0	1	0	.067	.800
11	1	0	1	1	.033	.400
12	1	1	0	0	.080	.917
13	1	1	0	1	.033	.600
14	1	1	1	0	.047	.857

Note. For any given trial, 1 of the 14 possible cue pattern combinations displayed above appeared on the computer screen with a probability indicated as: P(cue combination). As shown above, the probability of the cue combinations to predict “sunshine” (outcome 1) was set at P(outcome). Conversely, the probability of the above cue combinations to predict “rain” (or outcome 2) was equal to 1 − P.