Differences and commonalities in the judgment of causality in physical and social contexts: An fMRI study

Wende, Kim C.; Nagels, Arne; Blos, Johannes; Stratmann, Mirjam; Chatterjee, Anjan; Kircher, Tilo; Straube, Benjamin

doi:10.1016/j.neuropsychologia.2013.07.027

Cited by 13 publications

(20 citation statements)

References 70 publications

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“…If the observation of physical interactions drives responses in these candidate regions, this crucial role of observation might explain why our findings differ from those of a previous study that failed to find brain regions that were preferentially recruited for judging physical causality vs. social causality (35). Here, we used much longer observation periods, which allowed subjects to track physical behavior over the course of several seconds.…”

Section: Experiments 1: Physical and Nonphysical Judgments With Visuallymentioning

confidence: 93%

Functional neuroanatomy of intuitive physical inference

Fischer

Mikhael

Tenenbaum

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

142

159

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To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.physical scene understanding | mental simulation | fMRI | premotor cortex | action planning U nderstanding, predicting, and acting on the world requires an intuitive grasp of physics ( Fig. 1). We see not just a table and a coffee cup, but a table supporting a coffee cup. We see not just a ping pong ball moving after contact with a paddle, but the paddle causing the ball to move by exerting a force through that contact. We use physical intuitions to not just understand the world but predict what will happen next-that a stack of dishes is unstable and likely to fall or that a squash ball is on a trajectory to ricochet off the wall and head in our direction. We also need rich physical knowledge to plan our own actions. Before we pick up an object, we must assess its material and weight and prepare our muscles accordingly. To navigate our environment, we need to determine which surfaces will support us (e.g., a linoleum floor but maybe not the surface of a frozen stream; this tree branch but probably not that one) and what barriers are penetrable (e.g., a beaded curtain but not a glass wall). How do we compute these everyday physical inferences with such apparent ease and speed?Battaglia et al.(1) recently proposed a computational mechanism for how humans can make a wide range of physical inferences in natural scenes via a mental simulation engine akin to the "physics engines" used in many video games. Physics engines are software systems that support efficient but approximate simulations of rigid body, soft body, or fluid mechanics for the purpose of generating realistic interactive gameplay in a virtual physical world. Rather than striving for fine-grained physical accuracy, game physics engines make shortcu...

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Section: Experiments 1: Physical and Nonphysical Judgments With Visuallymentioning

confidence: 93%

Functional neuroanatomy of intuitive physical inference

Fischer

Mikhael

Tenenbaum

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

142

159

View full text Add to dashboard Cite

show abstract

“…Imaging data were analysed using SPM8. For detailed descriptions of data acquisition and preprocessing steps see Wende et al (2013).…”

Section: Fmri Data Acquisition and Analysismentioning

confidence: 99%

“…At the neural level, we expected causality judgements to evoke common neural activity in both groups (task effect) in frontal and parietal cortex regions, a neural network confirmed to be active in tasks involving causal inferences (Kranjec et al, 2012;Watson and Chatterjee, 2012) and causality judgements Wende et al, 2013).…”

Section: Introductionmentioning

confidence: 97%

“…Recently, our group conducted a set of functional magnetic resonance imaging (fMRI) studies using healthy volunteers as participants, to investigate the neural correlates of causal judgements on simple motion stimuli in physical and social contexts (Blos et al, 2012;Wende et al, 2013). Causality judgements, as compared to judgements of movement direction, engaged a frontoparietal network (Wende et al, 2013). Similar patterns of neural activity have been associated with explicit (top-down) inferences about visualspatial ("perceptual") attributes during reasoning (Kranjec et al, 2012;Straube et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Evidence from brain lesion studies and neuroimaging suggests a distinction between automatic causality perception and cognitive inference (Blakemore et al, 2001;Fonlupt, 2003;Fugelsang et al, 2005;Roser et al, 2005). Recently, our group conducted a set of functional magnetic resonance imaging (fMRI) studies using healthy volunteers as participants, to investigate the neural correlates of causal judgements on simple motion stimuli in physical and social contexts (Blos et al, 2012;Wende et al, 2013). Causality judgements, as compared to judgements of movement direction, engaged a frontoparietal network (Wende et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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