In this chapter we shall examine the characteristic properties of a construction wide-spread in the world’s languages, the passive. In section 1 below we discuss defining characteristics of passives, contrasting them with other foregrounding and backgrounding constructions. In section 2 we present the common syntactic and semantic properties of the most wide-spread types of passives, and in section 3 we consider passives which differ in one or more ways from these. In section 4, we survey a variety of constructions that (...) resemble passive constructions in one way or another. In section 5, we briefly consider differences between languages with regard to the roles passives play in their grammars. Specifically, we show that passives are a more essential part of the grammars of some languages than of others. (shrink)

What role does language play during attention allocation in perceiving and remembering events? We recorded adults‟ eye movements as they studied animated motion events for a later recognition task. We compared native speakers of two languages that use different means of expressing motion (Greek and English). In Experiment 1, eye movements revealed that, when event encoding was made difficult by requiring a concurrent task that did not involve language (tapping), participants spent extra time studying what their language treats as the (...) details of the event. This „linguistic encoding‟ effect was eliminated both when event encoding was made easier (no concurrent task) and when the concurrent task required the use of language (counting aloud). In Experiment 2, under conditions of a delayed concurrent task of counting aloud, participants used language covertly just prior to engaging in the additional task. Together, the results indicate that language can be optionally recruited for encoding events, especially under conditions of high cognitive load. Yet, these effects are malleable and flexible and do not appear to shape core biases in event perception and memory. (shrink)

We study how people attend to and memorize endings of events that differ in the degree to which objects in them are affected by an action: Resultative events show objects that undergo a visually salient change in state during the course of the event (peeling a potato), and non‐resultative events involve objects that undergo no, or only partial state change (stirring in a pan). We investigate general cognitive principles, and potential language‐specific influences, in verbal and nonverbal event encoding and memory, (...) across two experiments with Dutch and Estonian participants. Estonian marks a viewer's perspective on an event's result obligatorily via grammatical case on direct object nouns: Objects undergoing a partial/full change in state in an event are marked with partitive/accusative case, respectively. Therefore, we hypothesized increased saliency of object states and event results in Estonian speakers, as compared to speakers of Dutch. Findings show (a) a general cognitive principle of attending carefully to endings of resultative events, implying cognitive saliency of object states in event processing; (b) a language‐specific boost on attention and memory of event results under verbal task demands in Estonian speakers. Results are discussed in relation to theories of event cognition, linguistic relativity, and thinking for speaking. (shrink)

Human vision supports social perception by efficiently detecting agents and extracting rich information about their actions, goals, and intentions. Here, we explore the cognitive architecture of perceived animacy by constructing Bayesian models that integrate domain‐specific hypotheses of social agency with domain‐general cognitive constraints on sensory, memory, and attentional processing. Our model posits that perceived animacy combines a bottom–up, feature‐based, parallel search for goal‐directed movements with a top–down selection process for intent inference. The interaction of these architecturally distinct processes makes perceived (...) animacy fast, flexible, and yet cognitively efficient. In the context of chasing, in which a predator (the “wolf”) pursues a prey (the “sheep”), our model addresses the computational challenge of identifying target agents among varying numbers of distractor objects, despite a quadratic increase in the number of possible interactions as more objects appear in a scene. By comparing modeling results with human psychophysics in several studies, we show that the effectiveness and efficiency of human perceived animacy can be explained by a Bayesian ideal observer model with realistic cognitive constraints. These results provide an understanding of perceived animacy at the algorithmic level—how it is achieved by cognitive mechanisms such as attention and working memory, and how it can be integrated with higher‐level reasoning about social agency. (shrink)