Fig. 6.
A model of increased weighting on negative utilities captures the observed Nlgn1−/− behavioral phenotype across tasks. a An agent selects actions by comparing the net utility of each available action, sum of positive and negative utilities weighted by two separate parameters (βP and βN). The model assumes different types of positive and negative utilities are under the general control of βP and βN (e.g., βN affects both the weighting on physical effort as well as immersion in water where appropriate). Net utilities of potential actions are then fed into a softmax function for probabilistic action selection where actions with higher net utilities are selected with higher probabilities. b Left panel: Increasing βN has (i) no impact on the choice between correct and incorrect responding in the simulated binary choice task, (ii) reduces the number of responses made in the simulated fixed ratio task where the choice is between responding (high-effort action) and resting (low-effort action), and (iii) increases mobility in the simulated Porsolt swim test where the choice is between swimming (high-effort action) and resting (low-effort action). b Right panel: For comparison, our experimental data from (i) object-location paired associate learning (PAL) task, (ii) fixed ratio task, and (iii) Porsolt swim test