Extended Data Fig. 8: Basic and extended mean-field models.

a, Schematic of the mean-field neural model that describes generic MD inactivation results (see Extended Data Fig. 7o). The model describes two PL populations that receive separate inputs corresponding to the cues in favour of the two attentional rules (HP - attend to vision or LP – attend to audition). Each population has strong recurrent self-excitation and net inhibition on the other population. The MD component of the model receives inputs from the PL (see Extended Data Fig. 7) and is activated by conflict to inhibit the two PL populations. b, Example model decision variables in a trial early biased to the wrong attentional choice, demonstrating how MD-mediated suppression may improve performance of the model. When MD is intact (left), strong early evidence to the wrong choice (high-pass in this example; cueing sequence in inset) increases the decision variable of the non-preferred population early on, but the preferred population prevails when the preferred stimulus dominates in the latter half of the cueing sequence. On the other hand, in the absence of MD conflict-driven suppression of cue integration in the PL (right), the early non-preferred inputs drive the non-preferred population to maintain high activity, suppressing the preferred population’s response to late inputs. c, Schematic of the mean-field neural model incorporating the two cell types, where MD_GRIK4 is conflict-activated and suppresses PL, and MD_D2 is conflict-suppressed and amplifies PL recurrence. MD_D2 results in enhanced gain of the PL input-output function (bottom). d, Example model decision variables for high conflict trials, with (left) and without (right) MD_D2. Increased PL recurrence due to MD_D2 results in larger response to input cues. However, the effect is less pronounced for preferred cues as the population activity and decision variable saturate with inputs. As a result, the larger response to input cues asymmetrically favours the non-preferred population, and the separation between preferred and non-preferred activity is larger without MD_D2 (shown are median over 1,000 trials). e, Example model decision variables for low signal sparse trials (Fig. 4), with (left) and without (right) MD_D2 module. Increased PL recurrence due to MD_D2 allows amplified response of the preferred population to sparse input cues, but minimally affects the non-preferred population which receives no input cues. As such, MD_D2 results in a larger separation between preferred and non-preferred activity (shown are median over 1,000 trials).