Disentangling the component processes in complex planning impairments following ventromedial prefrontal lesions.

Abstract

Damage to ventromedial prefrontal cortex (vmPFC) in humans disrupts planning abilities in naturalistic settings. However, it is unknown which components of planning are affected in these patients, including selecting the relevant information, simulating future states, or evaluating between these states. To address this question, we leveraged computational paradigms to investigate the role of vmPFC in planning, using the board game task 'Four-in-a-Row' (18 lesion patients, 9 female; 30 healthy control participants, 16 female), and the simpler 'Two-Step' task measuring model-based reasoning (49 lesion patients, 27 female; 20 healthy control participants, 13 female). Damage to vmPFC disrupted performance in Four-in-a-Row compared to both control lesion patients and healthy age-matched controls. We leveraged a computational framework to assess different component processes of planning in Four-in-a-Row and found that impairments following vmPFC damage included shallower planning depth, and a tendency to overlook game-relevant features. In the 'Two-Step' task, which involves binary choices across a short future horizon, we found little evidence of planning in all groups, and no behavioural differences between groups. Complex yet computationally tractable tasks such as 'Four-in-a-row' offer novel opportunities for characterising neuropsychological planning impairments, which in vmPFC patients we find are associated with oversights and reduced planning depth.Significance Statement The ability to plan in real-world settings is often disrupted after damage to ventromedial prefrontal cortex (vmPFC). However, real-world planning consists of many different cognitive processes, and it is uncertain which processes are disturbed by these lesions. Here we use rich computational models of planning to characterise behaviour in two planning tasks performed by patients with vmPFC damage and controls. VmPFC damage only affected behaviour in the more complex planning task, and behavioural modelling revealed this was associated with planning less far into the future and overlooking important features. These findings shed light on the neural mechanisms supporting complex planning, demonstrating how novel computational methods can strike the balance between task complexity and interpretability.