This paper considers the cognitive penetrability of our experiences of the durations of everyday events. I defend an account of subjective duration based in contemporary psychological and neurobiological research. I show its philosophical adequacy by demonstrating its utility in explain-ing the phenomenology of duration experiences. I then consider whether cognitive penetrability is a problem for these experiences. I argue that, to the contrary, the problem presupposes a relationship between perception and belief that duration perceptions and beliefs do not exhibit. In-stead, (...) the assignment of epistemic features to particular processing stages appears to answer to pragmatic needs, not psychological facts. (shrink)

In this article, we explore whether cross-linguistic differences in grammatical aspect encoding may give rise to differences in memory and cognition. We compared native speakers of two languages that encode aspect differently (English and Swedish) in four tasks that examined verbal descriptions of stimuli, online triads matching, and memory-based triads matching with and without verbal interference. Results showed between-group differences in verbal descriptions and in memory-based triads matching. However, no differences were found in online triads matching and in memory-based triads (...) matching with verbal interference. These findings need to be interpreted in the context of the overall pattern of performance, which indicated that both groups based their similarity judgments on common perceptual characteristics of motion events. These results show for the first time a cross-linguistic difference in memory as a function of differences in grammatical aspect encoding, but they also contribute to the emerging view that language fine tunes rather than shapes perceptual processes that are likely to be universal and unchanging. (shrink)

Events can be modeled through a geometric approach, representing event structures in terms of spaces and mappings between spaces. At least two spaces are needed to describe an event, an action space and a result space. In this article, we invoke general mathematical structures in order to develop this geometric perspective. We focus on three cognitive processes that are crucially involved in events: causal thinking, control of action and learning by generalization. These cognitive processes are supported by three corresponding mathematical (...) properties: monotonicity (that we relate to qualitative causal thinking and allows extrapolation); continuity (that plays a key role in our activities of action control); and convexity (that facilitates generalization and the categorization of events, and enables interpolation). We define how such properties constrain events representations and relate them to thinking about events. We discuss the relevance of the three constraints for event segmentation and explore the implications of such constraints for semantics. We conclude by a discussion that relates our approach to other accounts of events. (shrink)

We describe a novel computational theory of how individuals segment perceptual information into representations of events. The theory is inspired by recent findings in the cognitive science and cognitive neuroscience of event segmentation. In line with recent theories, it holds that online event segmentation is automatic, and that event segmentation yields mental simulations of events. But it posits two novel principles as well: first, discrete episodic markers track perceptual and conceptual changes, and can be retrieved to construct event models. Second, (...) the process of retrieving and reconstructing those episodic markers is constrained and prioritized. We describe a computational implementation of the theory, as well as a robotic extension of the theory that demonstrates the processes of online event segmentation and event model construction. The theory is the first unified computational account of event segmentation and temporal inference. We conclude by demonstrating now neuroimaging data can constrain and inspire the construction of process-level theories of human reasoning. (shrink)

The aim of the article is to present a model of causal relations that is based on what is known about human causal reasoning and that forms guidelines for implementations in robots. I argue for two theses concerning human cognition. The first is that human causal cognition, in contrast to that of other animals, is based on the understanding of the forces that are involved. The second thesis is that humans think about causality in terms of events. I present a (...) two-vector model of events, developed by Gärdenfors and Warglien, which states that an event is represented in terms of two main components – the force of an action that drives the event, and the result of its application. Apart from the causal mapping, the event model contains representations of a patient, an agent, and possibly some other roles. Agents and patients are objects (animate or inanimate) that have different properties. Following my theory of conceptual spaces, they can be described as vectors of property values. At least two spaces are needed to describe an event, an action space and a result space. The result of an event is modeled as a vector representing the change of properties of the patient before and after the event. In robotics the focus has been on describing results. The proposed model also includes the causal part of events, typically described as an action. A central part of an event category is the mapping from actions to results. This mapping contains the central information about causal relations. In applications of the two-vector model, the central problem is how the event mapping can be learned in a way that is amenable to implementations in robots. Three processes are central for event cognition: causal thinking, control of action and learning by generalization. Although it is not yet clear which is the best way to model how the mappings can be learned, they should be constrained by three corresponding mathematical properties: monotonicity (related to qualitative causal thinking); continuity (plays a key role in activities of action control); and convexity (facilitates generalization and the categorization of events). I argue that Bayesian models are not suitable for these purposes, but some more geometrically oriented approach to event mappings should be used. (shrink)

Toutes les propriétés sont-elles sur un pied d’égalité ? Selon une réponse populaire, toutes les propriétés ne sont pas également fondamentales : certaines sont fondamentales ou rares (« sparses »), tandis que d’autres ne le sont pas. Une version de cette réponse soutient que les propriétés fondamentales sont les propriétés causalement efficaces. Une autre version soutient que les propriétés fondamentales sont des entités temporelles ou même des forces ou des pouvoirs. Une autre version encore soutient que les propriétés positives, contrairement (...) aux propriétés négatives ou disjonctives, sont fondamentales. (shrink)

Reasoning is not just following logical rules, but a large part of human reasoning depends on our expectations about the world. To some extent, non-monotonic logic has been developed to account for the role of expectations. In this article, the focus is on expectations based on actions and their consequences. The analysis is based on a two-vector model of events where an event is represented in terms of two main components – the force of an action that drives the event, (...) and the result of its application. Actions are modelled in terms of the force domain and the results are modelled with the aid of different domains for locations or properties of objects. As a consequence, the assumption that reasoning about causal relations should be made in terms of propositional structures becomes very unnatural. Instead, the reasoning will be based on the geometric and topological properties of causes and effects modelled in conceptual spaces. (shrink)