In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. Here, we ask whether it is possible to characterize formally the choice of navigational plans as a bounded rational process that trades off the quality of the plan (e.g., its length) and the cognitive cost required to find and implement it. We analyze the navigation strategies of two groups of people that are firstly trained to follow a “default policy” taking a route in a virtual maze and then asked to navigate to various known goal destinations, either in the way they want (“Go To Goal”) or by taking novel shortcuts (“Take Shortcut”). We address these wayfinding problems using InfoRL: an information-theoretic approach that formalizes the cognitive cost of devising a navigational plan, as the informational cost to deviate from a well-learned route (the “default policy”). In InfoRL, optimality refers to finding the best trade-off between route length and the amount of control information required to find it. We report five main findings. First, the navigational strategies automatically identified by InfoRL correspond closely to different routes (optimal or suboptimal) in the virtual reality map, which were annotated by hand in previous research. Second, people deliberate more in places where the value of investing cognitive resources (i.e., relevant goal information) is greater. Third, compared to the group of people who receive the “Go To Goal” instruction, those who receive the “Take Shortcut” instruction find shorter but less optimal solutions, reflecting the intrinsic difficulty of finding optimal shortcuts. Fourth, those who receive the “Go To Goal” instruction modulate flexibly their cognitive resources, depending on the benefits of finding the shortcut. Finally, we found a surprising amount of variability in the choice of navigational strategies and resource investment across participants. Taken together, these results illustrate the benefits of using InfoRL to address navigational planning problems from a bounded rational perspective.

/pdf/humans-account-for-cognitive-costs-when-finding-shortcuts-an-5kb9naew.pdf

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation

City planners and architects employ graph-theoretic measures to analyse models of the built environment and predict human navigational behaviour. A recent breakthrough in the neuroscience of spatial cognition has shown that activation in the human hippocampus tracks the change in centrality for subjects navigating a virtual Soho in a fMRI scanner. Based on a well understood information-theoretic framework for modelling intelligent behaviour under cognitive constraints, the existing measures empowerment and relevant goal information, and novel quantity relevant goal information uptake were applied to a graph of the Soho street network navigated in the experiment. Empowerment, relevant goal information and relevant goal information uptake are shown to correlate with graph centrality for the primal graph, and to a lesser extent with centrality for the dual graph as used in the Soho experiment. These results, consistent with the hypothesis, provide preliminary evidence that human navigation employs an empowerment maximisation strategy, and to the author’s knowledge, linking empowerment and relevant goal information with empirical neuroscience in a collaborative study for the first time.

Empowerment and Relevant Goal Information as Alternatives to Graph-Theoretic Centrality for Navigational Decision Making

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Empowerment and Relevant Goal Information as Alternatives to Graph-Theoretic Centrality for Navigational Decision Making