When the Europeans began to come to stay in North America in the seventeenth century, they found a completely new horizon, not only in flora and fauna, climate and geography, but also in the people they encountered. Differences between Europeans and North Americans in physique, dress, customs, manners , and economy all stood out with great clarity. With similar clarity, the differences in language stood out. The languages the Europeans encountered were unlike any they had encountered before, not only in sound and structure but also in the social settings in which they were found. In Europe there were but few languages, which were spoken by large numbers of people-hundreds of thousands, if not millions, in some cases, such as English and French; in North America, there were dozens of languages, spoken by very small speech communities in the hundreds of thousands. In Mexico and Peru, where the Aztec and Inca empires flourished, the language structure was imperial, with subject peoples continuing to speak their own languages even though many learned that of the rulers of the empire. Furthermore, the European languages, as welI as the languages Europeans had encountered in the Middle East and in Asia, were written languages, with grammatical and literary traditions. The languages of North America were neither written nor did they have such traditions. They presented an uncharted sea of variation and diversity. So the newness and unexpectedness of the languages and those who spoke them had their impact on the Europeans. Faced with these new languages, along with the new cultures and customs, the Europeans set about to record and describe the new linguistic horizon as best they could. The recording and description of languages, which here will be our major concern, was admittedly a secondary linguistic task for the Europeans. Their first concern was to learn the languages they had come in contact with; the second was to write down what they had learned. This recording they did as best they could within the intellectual framework that was available to them and in the absence of native literary or grammatical traditions to use as a guide-much early description of North American languages sounds like a description of Latin or Greek, or even Hebrew- and with a great variety of success and failure. But record they did, and the result was a fairly large body of writings of various sorts- dictionaries, word lists, phrase books, translations (mostly of the holy scriptures), grammatical disquisitions, and the like. The pattern of European response to North American languages can be organized in terms of a cycle that seems to repeat itself throughout the history of this response. The sequence includes, first, the response of explorers and travelers; second, that of missionaries; third, that of scholars. From the earliest times of Europeans in North America this triad of explorer, missionary, and scholar has reacted and interacted with the languages encountered, and the result of this interaction is a large, sophisticated body of data and techniques that has contributed greatly to the development of linguistics, and to the preservation, analysis, and even the spread of the languages concerned.

Searching natural environments, as for example, when foraging or looking for a landmark, combines reasoningunder uncertainty, planning and visual search. Existing paradigms for studying search in humans focus on step-by-step infor-mation sampling, without examining advance planning. We propose and evaluate a Bayesian model of how people search in anaturalistic maze-solving task. The model encodes environment exploration as a sequential process of acquiring informationmodelled by a Partially Observable Markov Decision Process (POMDP), which maximises the information gained. We showthat the search policy averaged across participants is optimal. Individual solutions, however, are highly variable and can beexplained by two heuristics: thinking and guessing. Self-report and inference, a Gaussian Mixture Model over inverse POMDP,consistently assign most subjects to one style or the other. By analysing individual participants’ decision times we show thatindividuals solve partial POMDPs and plan their search a limited number of steps in advance.

People have a common-sense notion of intelligence and use itto evaluate decisions and decision-makers. One can attributeintelligence by evaluating the strategy or the outcome of agoal-directed agent. We propose a model of intelligence at-tribution, based on inverse planning in Partially ObservableMarkov Decision Processes (POMDPs) in a probabilistic envi-ronment, inferring the most likely planning parameters givenobserved actions. The model explains the agent’s decisionsby a combination of probabilistic planning, a softmax decisionnoise, prior knowledge about the world and forgetting, estimat-ing the agent’s intelligence by a proxy measure of efficientlyoptimising costs and rewards. Behavioural evidence from twoexperiments shows that people cluster into those who attributeintelligence to the strategy and those who attribute intelligenceto the outcome of the observed actions. People in the strat-egy cluster attribute more intelligence to decisions that min-imise the agent’s overall cost, even if the outcome is unlucky.People in the outcome cluster attribute intelligence to the out-come, judging low-cost outcomes as a sign of intelligence evenif the outcome is accidental and make neutral judgements be-fore they observe the result. Our model explains human in-telligence judgements better than perceptual cues such as thenumber of revisits or moves.