Fig. 5.

Modeling task behavior as a local search in semantic space. (A) Model comparison, summed AIC over all participants (see SI Appendix for model specifications). The winning (4 parameter) model asserts that the probability of observing the t-th emitted word ( $W_t$ ) is a softmax function of both the semantic and orthographic association strength between $W_t$ and the previous emitted word (i.e., $S\left(W_{t-1},W_t\right)$ ), which are multiplicatively scaled by separate free salience parameters $\beta$ . Data from letter and category tasks modeled separately (i.e., this model may be construed as two independent 2-parameter models, one for each task, as shown in equation). (B) Group * task ANOVA for orthographic (Left) and semantic (Right) salience fitted parameters from the winning model (in A). Significant interaction effect for semantic saliencies (** denotes P < 0.01), indicating that control participants exhibit an increased boosting of semantic salience in category vs. letter task, compared to PScz. (C) Across participants, we find a positive correlation between goal-induced semantic modulation ( $\mathrm{\Delta }\omega ,$ a contrast of fitted model parameters quantifying how relative sensitivity to semantic associations increases in line with task context) and performance (mean list length across both tasks, where the length of each list is first expressed as a task-specific rank across participants to account for differences in the distribution of list lengths between tasks). (D) The relationship between negative symptoms of schizophrenia (49) and $\mathrm{\Delta }\omega$ . For (C) and (D), correlation coefficients from Spearman’s correlation. Dashed trend lines represent 95% CI on linear line of best fit. Sample: n = 26 controls, n = 26 PScz.