What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task

Abstract

The Weather Prediction Task (WPT) was originally designed to assess probabilistic classification learning. Participants were believed to gradually acquire implicit knowledge about cue-outcome association probabilities and solve the task using a multicue strategy based on the combination of all cue-outcome probabilities. However, the cognitive processes engaged in the resolution of this task have not been firmly established, and despite conflicting results, the WPT is still commonly used to assess striatal or procedural learning capacities in various populations. Here, we tested young adults on a modified version of the WPT and performed novel analyses to decipher the learning strategies and cognitive processes that may support above chance performance. The majority of participants used a hierarchical strategy by assigning different weights to the different cues according to their level of predictability. They primarily based their responses on the presence or absence of highly predictive cues and considered less predictive cues secondarily. However, the influence of the less predictive cues was inconsistent with the use of a multicue strategy, since they did not affect choices when both highly predictive cues associated with opposite outcomes were present simultaneously. Our findings indicate that overall performance is inadequate to draw conclusions about the cognitive processes assessed by the WPT. Instead, detailed analyses of performance for the different patterns of cue-outcome associations are essential to determine the learning strategies used by participants to solve the task.

Keywords: conditional learning; explicit; hippocampus; implicit; multiple-cue learning; probabilistic learning; striatum.

Figure 1

(A) Example of one set of four cues (sea animals), which were associated with each outcome (sun or snow) with a fixed level of probability across all patterns. (B) Representation of the computer screen for one trial when three animals were displayed, and the participant had to choose between snow and sun. Note that one, two or three animals could be displayed on any given trial; all four animals were never displayed simultaneously.

Figure 2

Individual performance of young healthy adults in the WPT (closed circles: women; open circles: men). (A) Number of correct choices across all 93 training trials. The black line represents the number of correct choices (56/93) defined as statistically different from chance at the individual level (${\chi }_{\left(1\right)}^2$ = 3.882, p = 0.049). (B) Number of correct choices during the four test trials with individual cues and no feedback.

Figure 3

Means of fitted scores (±SE) for each strategy for the group of 20 young adults who performed the WPT above chance level. Note that in order to make the graphical representation more intuitive, the y-axis represents 1-score generated by the model. Therefore, the higher the value, the more likely it was that a given strategy was used by the group of participants. The strategies are listed in ranking order from the most likely used (congruent cues) to the least likely used (one less predictive cue).

Figure 4

(A) Normalized number of correct choices (NCC; mean ± SE) during the training phase for each category of patterns for the participants who performed the WPT above chance level; (B) Number of correct choices (NCC; mean ± SE) during the test phase for each category of patterns for the participants who performed the WPT above chance level.