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Showcase April 2015: How do (some) people form cognitive maps?

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How do (some) people form cognitive maps?

Steven M. Weisberg & Nora S. Newcombe (PI)

Temple University

Think about your daily commute. When you walk or drive or bike, do you usually think about the direction in which your traveling, or operate on auto-pilot - responding to turns as they come? What about when you're learning a new place? Do you try to relate each new landmark and street, or memorize which route to take?

These methods of navigation seem really different, and neuroscientists have long known they are (McDonald & White, 1994; Tolman, 1948). Recent work has shown that different brain areas support these different types of navigation strategies. (The direction-focused strategy is termed place-learning and is supported by the home of place cells, the hippocampus. Whereas the more habitual auto-pilot strategy is termed response-learning, and is supported by the caudate.) Humans naturally vary in terms of which strategy they prefer (Marchette, Bakker, & Shelton, 2011), and many of us use both in our daily lives. But the strategies provide different information. Place-learning relies on encoding directions and distances - metric information that should make it easier to create a mental map of the space and take novel shortcuts. Response-learning relies on remembering routes - specific turns at specific places - but not sufficient information to create a mental map or take novel shortcuts. In other words, navigation strategy might determine the accuracy with which one can represent an environment.

Using virtual environments designed to test navigation strategy (the Dual Solution Paradigm or DSP; Marchette et al. 2012) and navigation accuracy (Virtual Silcton; Weisberg, Schinazi, Newcombe, Shipley, & Epstein, 2014), we decided to test the question of whether navigation strategy preference relates to navigation accuracy. In Virtual Silcton, participants learn two main routes through a virtual environment by virtually traveling along paths which are indicated by arrows on the ground. These two routes are in the same environment, but in separated areas. Along each of the two routes, they must learn the names and locations of four buildings, indicated with a sign. Then, they learn two routes that connect the first two routes to each other, but do not contain any new buildings. At test, participants are placed next to each building they learned, and must point to all other buildings (both within the main route that building first appeared on, and between the two main routes). Previously, we showed that participants differ on within-route and between-route accuracy, suggesting that the two types of learning might be different from each other (Weisberg et al., 2014).

Figure 1
Figure 1. The Virtual Silcton environment contains 4 buildings, first encountered along 2 main routes (A1-A2 and B1-B2). The two main routes are connected by two additional routes (C1-C2 and D1-D2).

Figure 2
Figure 2. A screenshot from the Virtual Silcton environment. Participants used a mouse and keyboard to navigate this desktop environment. The blue gem indicates that there is a building nearby participants must learn; the sign at right names that building. The pointing task is presented in a similar format, but participants cannot move, and must point a crosshair onscreen directly at the building indicated.

In the DSP, participants learn a virtual environment consisting of 12 objects by watching a video showing the same route nine times. At test, they are dropped in different places in the environment and asked to walk (as efficiently as they can) to an object named on the screen. For some of these trials, there is a shortcut, not shown during the video, which offers a shorter path to the object. We calculated the ratio of these trials on which the participant found the goal by taking a shortcuts divided by trials on which the participant found the goal by taking the familiar route. This ratio is that participant's place/response index, providing a measure of how strongly they prefer one strategy over another. We also tallied the total number of objects participants found, regardless of the strategy by which they found them.

Figure 3
Figure 3. The Dual Solution Paradigm contains 12 objects, which participants learn by viewing a video of the same route through the environment nine times. They then must travel from one object to a target. They can take the familiar route (in dark black) or a novel shortcut not shown in the video (in lighter grey). The proportion of novel shortcuts they take provides a measure of their strategy preference, while the total number of objects they find successfully provides a measure of their navigation proficiency.

Figure 4
Figure 4. A screenshot from the Dual Solution Pardigm, which shows one of the objects that must be learned (a Table). The video (learning) and navigation (test) phases looked identical, but participants could navigate using the mouse and keyboard during the latter.

Our prediction was that participants who strongly preferred a place strategy in the DSP would have the highest accuracy on between-route pointing in Virtual Silcton, while participants who preferred a response-strategy would be good at within-route pointing, but poor at between-route pointing. Interestingly, we did not observe an overall relationship between navigation strategy and navigation accuracy. Instead, we found that the most accurate navigators who preferred a place strategy performed better than the most accurate navigators who preferred a response strategy. The least accurate navigators who preferred a response strategy outperformed the least accurate navigators who preferred a place strategy. From this, we conclude that the compatibility between navigation strategy and proficiency is important for determining navigation accuracy.

Figure 5
Figure 5. Accuracy on the Dual Solution Paradigm (DSP; Goals Found) varied by the strategy participants preferred on the DSP, and accuracy on the Virtual Silcton pointing task (inaccurate overall - Imprecise Navigators; accurate on just within-route trials - Non-Integrators; accurate overall - Integrators).

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