20 Dynamics and the Perception of Causal Events

Wolff, Phillip

doi:10.1093/acprof:oso/9780195188370.003.0023

Cited by 27 publications

(31 citation statements)

References 52 publications

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“…The impression of causality in simple mechanical events is built on spatial and temporal elements (e.g., Blakemore et al, 2003; Buehner and Humphreys, 2010; Fonlupt, 2003; Fugelsang et al, 2005; Guski and Troje, 2003; Roser et al, 2005; Scholl and Tremoulet, 2000; Wolff, 2007, 2008; Woods et al, 2012). For example, in a mechanical event with two objects, spatial continuity and temporal contiguity increases the likelihood that a person will perceive causality (e.g., Straube and Chatterjee, 2010; Woods et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…When one object—for instance, a billiard ball—moves towards another, the timing and direction of movement of both objects influences our perception of whether one object causes the other to move. This spatial and temporal information conveys a sense of forces underlying causal relationships in an event (Wolff, 2007, 2008). Contextual information, such as recent experience and prior knowledge, also influences the perception of causality (Buehner and May, 2002, 2003; Gruber et al, 1957; Powesland, 1959; Schlottmann, 1999; Shanks, 1985; Woods et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Space, time, and causality in the human brain

et al. 2014

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The ability to perceive causality is a central human ability constructed from elemental spatial and temporal information present in the environment. Although the nature of causality has captivated philosophers and scientists since antiquity, the neural correlates of causality remain poorly understood. In the present study, we used functional magnetic resonance imaging (fMRI) to generate hypotheses for candidate brain regions related to component processes important for perceptual causality in the human brain: elemental space perception, elemental time perception, and decision-making (Experiment 1; n=16). We then used transcranial direct current stimulation (tDCS) to test neural hypotheses generated from the fMRI experiment (Experiment 2; n=16). In both experiments, participants judged causality in billiard-ball style launching events; a blue ball approaches and contacts a red ball. Spatial and temporal contributions to causal perception were assessed by parametrically varying the spatial linearity and the temporal delays of the movement of the balls. Experiment 1 demonstrated unique patterns of activation correlated with spatial, temporal, and decision-making components of causality perception. Using tDCS, we then tested hypotheses for the specific roles of the parietal and frontal cortices found in the fMRI experiment. Parietal stimulation only decreased participants’ perception of causality based on spatial violations, while frontal stimulation made participants less likely to perceive causality based on violations of space and time. Converging results from fMRI and tDCS indicate that parietal cortices contribute to causal perception because of their specific role in processing spatial relations, while the frontal cortices contribute more generally, consistent with their role in decision-making.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space, time, and causality in the human brain

et al. 2014

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“…Causative events are of interest because they are complex events composed of multiple dynamic subcomponents that overlap in time and space. Several studies have looked at the way that infants, children, and adults reason about causative events (Bunger, 2006; Cohen, Rundell, Spellman & Cashon, 1999; Göksun, George, Hirsh-Pasek & Golinkoff, 2013; Leslie, 1984; Leslie & Keeble, 1987; Oakes & Cohen, 1990; Wolff, 2007, 2008) and the way that learners come to encode subcomponents of such events into different types of verbs (Behrend, 1990; Behrend, Harris & Cartwright, 1995; Bowerman, 1989; Bunger, 2006; Forbes & Farrar, 1993, 1995; Gentner, 1978; Gropen, Pinker, Hollander & Goldberg, 1991; Naigles, 1996; Papafragou & Selimis, 2010; Slobin, 1985). However, the cross-linguistic description of causative events has been the topic of only a few studies – and those mostly in adults (Klettke & Wolff, 2003; Wolff, Jeon, Klettke & Yu, 2010; Wolff, Jeon & Yu, 2009; Wolff & Ventura, 2009; but cf.…”

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confidence: 99%

How children and adults encode causative events cross-linguistically: implications for language production and attention

Bunger

Skordos

Trueswell

et al. 2016

Language, Cognition and Neuroscience

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This study investigates the implications of language-specific constraints on linguistic event encoding for the description and on-line inspection of causative events. English-speaking and Greek-speaking adults, 3-year-olds, and 4-year-olds viewed and described causative events, which are composed of Means and Result subevents, in an eyetracking study. The results demonstrate cross-linguistic differences in the informational content of causative event descriptions: Greek speakers across age groups were more likely than English speakers to mention only one causative subevent. Developmental changes in the tendency to encode information about causative events in language were also evident: in both language groups, adults were more likely than children to mention both Means and Result subevents. Finally, for both adult and child speakers of both languages, preparing different types of event descriptions changed the way that events were visually inspected, shifting attention toward to-be-encoded subevents. These findings offer some of the first evidence about the development of the language production system, the attentional mechanisms that it employs, and its workings in speakers of different languages.

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“…The upshot of these experiments is that the kinematics of a movement contains information that is sufficient for the identification of the underlying dynamic force patterns, that is velocities and accelerations (Runesson, 1994). Further psychological evidence [Wolff (2007[Wolff ( , 2008, Wolff and Shepard (2013), Wolff and Thorstad (2017)] supports that people can directly perceive the forces that control different kinds of motion.…”

Section: Causal Reasoning With Forces Human Reasoning About Forcesmentioning

confidence: 99%

“…Another central aspect is that the forces of an agent are not the only elements involved in human causal judgements, but counterforces of various kinds (forces exerted by a patient or contextual forces such as gravitation) are also taken into account. This aspect is included in Talmy's (1988) 'force dynamics' and is further developed in Wolff's (2007Wolff's ( , 2008Wolff's ( , 2012 'dynamics model'. Wolff (2007) has shown that adults can combine different kinds of forces in their reasoning.…”

Section: A Cognitive Approach To Causationmentioning

confidence: 99%

Events and Causal Mappings Modeled in Conceptual Spaces

Gärdenfors

2020

Front. Psychol.

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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 componentsthe 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.

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20 Dynamics and the Perception of Causal Events

Cited by 27 publications

References 52 publications

Space, time, and causality in the human brain

Space, time, and causality in the human brain

How children and adults encode causative events cross-linguistically: implications for language production and attention

Events and Causal Mappings Modeled in Conceptual Spaces

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