(in white). Additional observations will not allow individuals to decide whether one conceptualization is more likely than the other.

![On this figure one may see two faces (in black) or a cup (in white). Image courtesy of Bryan Derksen, under license CC BY-SA 3.0[]{data-label="fig:cup_or_faces"}](Cup_or_faces.png){width="4cm"}

In our model, we assume that individuals evaluate the likelihood of their conceptualization according to their own experience. To do so we define a likelihood landscape as following:

\[def:likelihoodLandscape\] $\forall c\in \mathcal{C}$, we define a function $$\begin{aligned}
    L(\cdot,c): & \, \mathcal{E} \to [0,1],\\
    & \, e \mapsto L(e,c).\end{aligned}$$ with $$\forall e \in \mathcal{E}, \ L(e,0) = \frac{1}{2}.$$

The map $L$ is called the *likelihood landscape*.

For all $e$, $c$ in $\mathcal{E} \times \mathcal{C}$, $L(e,c)$ represents how well the concept $c$ explain the experience (or observation) $x$.

#### Examples of likelihood landscapes.

-   Let us consider the evolution of two different concepts in a population: flat earth ($F$), and round earth ($R$). Individuals can have experiences where the Earth seems flat ($f$) and others where the Earth seems round ($r$) (seeing a picture of the Earth, a boat vanishing behind the horizon, etc). In this case $\mathcal{E} = \{f,r\}$ and $\mathcal{C} = \{0,F,C\}$. We define the likelihood 