How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming a trajectory through an abstract two-dimensional value space. During this task, the entorhinal cortex exhibited a grid-like representation with an orientation aligned to the axis through the value space most informative for choices. A network of brain regions, including ventromedial prefrontal cortex, tracked the prospective value difference between options.

These findings suggest that the entorhinal grid system supports the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional value signals to guide prospective decision making.

Prediction of future values is enabled by an internal model, which represents transitions between states and reward contingencies in an environment or task. Reliance on an internal model has been referred to as model-based decision making, and can lead to distinct value computations found in the dorsomedial prefrontal cortex (dmPFC). Moreover, the hippocampus has been implicated in model-based and value-based decision making.

Importantly, [Oliver M.] Vickbladh et al. found that the hippocampus serves as a common neural substrate for both model-based decision-making and spatial navigation is via the formation of cognitive maps.

Cognitive maps encode relationships between states in the form of a map-like format. Neurally, cognitive maps are assumed to rely on the activity of spatially tuned cells in the hippocampal-entorhinal system. For example, during spatial navigation, place cells in the hippocampus exhibit increased firing in specific locations within an environment. Grid cells in the adjacent entorhinal cortex fire at multiple locations within an environment and these locations form a hexagonal grid. Together, these cells enable self-localization and geometric computations supporting spatial navigation, e.g., the computation of distances and directions. Beyond spatial navigation, recent studies have shown hippocampal-entorhinal map-like and grid-like representations of more abstract information, e.g., in graph-like structures as well as in feature and concept spaces. Therefore, hippocampal-entorhinal cognitive maps might be assumed to provide a more general mechanism for organizing information, allowing for adaptive decision making. For example, two recent studies showed distance- and grid-like representations for novel inferences during decision making in a two-dimensional map of social hierarchies.

In decision making, states in the world and values are usually considered different entities, i.e., values (rewards) are received after performing an action in a given state. However, it is conceivable that the cognitive map could also contain value information represented in the cognitive map. In line with this notion, Bongioanni et al. demonstrated first evidence for a grid-like representation of an abstract value space defined by reward magnitude and probability in macaques. While choice options were presented in a specific location on the screen in the abstract space, an interesting question is whether the same map representation would code for values of options changing over time. By facilitating computations of directions and distances between options, such a cognitive map could enable efficient prediction of future values. The representation of a position in a value space spanned by changing reward probabilities could then be used to read out resulting values and generate lower-dimensional signals of the value difference between options and their identities for choices. First evidence for hippocampal neurons encoding position in a value space spanned by changing reward probabilities has been demonstrated in macaques. However, it remains elusive whether an entorhinal grid-like representation would encode changing values during prospective decision making in humans.

I am somewhat self-aware. Preening about a bunch of SwiftUI interfaces I generated clearly invites clinical and unsparing critique of their visual design. Bring it on. But: as a longtime patron of the App Store, I’ll claim these designs are all a step ahead of replacement-level. Five years ago, if I’d had a macOS UI person on my team, I’d have been over the moon to get output of this quality.

Back in 1999, Neal Stephenson wrote an essay about command line interfaces that set the field of human-computer interaction back about 20 years. In it, he depicts the priesthood of Unix nerds wielding CLIs as powerful Morlocks, holding the entire computing industry on their shoulders. The Eloi use GUIs like Microsoft Word. Because this is high-test fan-service, “In The Beginning Was The Command Line” has become one of our field’s sacred texts, despite very little of it holding up 25 years later.

Command-line interfaces and terminal user interfaces are both products of the 1970s, shrink-wrapped around the constraints of teletype interfaces and dumb video terminals. Both tend to be outmoded, hostile, and constrained relative to graphical interfaces. But these tendencies are intrinsic to TUIs, and not to CLIs. CLIs have purposes for which they’re irreplaceable. Building a CLI is almost always a good idea. Building a TUI almost never is.