Is searching memory like searching space? William James once wrote that “We make search in our memory for a forgotten idea, just as we rummage our house for a lost object.” Both space and memory have structure and we can use that structure to zero in on what we are looking for. In searching space, this is easy to see. A person hunting for their lost keys is not unlike a starling scouring the garden for wayward insects. But in searching memory, what is the map? And by what means does a person move from one memory to the next? In this chapter I will lay out the similarities between foraging in space and mind and then describe a research approach inspired by an ecological model of animal forging. Using this approach, we will combine data from a memory production task with a cognitive map – a network representation – of memory derived from natural language. We will then use this to compare a suite of models aimed at teasing apart how memory search is similar to our garden starling.

The chapter traces the process by which German corporations largely, though still partially, abandoned their Jewish colleagues in the first eighteen months of Nazi rule and simultaneously shed their earlier, recurrent demands for a “state-free” economy in favor of accepting the Nazi statist one. The account places more than customary emphasis on the role of intimidation.

Conspiracy theories explain anomalous events as the outcome of secret plots by small groups of people with malevolent aims. Is every conspiracy unique, or do they all share a common thread? That is, might conspiracy explanations stem from a higher-order belief that binds together a wide variety of overtly independent phenomena under a common umbrella? We can call this belief the conspiracy frame. Network science allows us to examine this frame at two different levels: by examining the structural coherence of individual conspiracies and by examining the higher-level interconnectivity of the conspiracy beliefs as a whole.

When nodes share features we can combine those features in many possible ways. One standard way is to base relationships on shared features. But there are other possibilities. Here we will apply a number of approaches to investigate the concept of distinctiveness. Distinctiveness is how easy it is to discriminate one thing from another thing. In an important sense distinctiveness is therefore a hypothesis about how the mind works. We say two things are distinctive because a mind can distinguish them. But what makes something distinctive? In this chapter, I will introduce some of the theory behind distinctiveness and then demonstrate how we can use network science to investigate distinctiveness in children’s abilities to learn words. This takes a multilayer network approach, in which we will examine many different edge types constructed of various combinations of shared and unshared features. By examining these edge types will discover how best to combine features and which feature combinations best predict early word learning.