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Humans are adept at inferring the mental states underlying other agents' actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents' behavior based on the "principle of rationality": the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent's behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an "intentional stance" [Dennett, D. C. (1987). "The intentional stance." Cambridge, MA: MIT Press] or a "teleological stance" [Gergely, G., Nadasdy, Z., Csibra, G., & Biro, S. (1995). "Taking the intentional stance at 12 months of age." "Cognition," 56, 165-193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent. (Contains 10 figures and 3 tables.)

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. [ABSTRACT FROM AUTHOR]

Abstract: We investigated people's ability to infer others’ mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents’ desires throughout. When the agent observed, but did not cause the outcome, participants’ ability to recover the agent's beliefs depended on the evidence they got (i.e., her reaction only to the actual outcome or to both the expected and actual outcomes; Experiments 1 and 2). When the agent caused the event, participants’ judgments also depended on the probability of the action (Experiments 3 and 4); when actions were improbable given the mental states, people failed to recover the agent's beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout (rs ≥ .95), consistent with the proposal that people rationally integrate information about others’ actions and emotional reactions to infer their unobservable mental states. [ABSTRACT FROM AUTHOR]

We propose a new method that uses an iterative closest point (ICP) algorithm to fit three-dimensional points to a prior geometric model for the purpose of determining the position and orientation (pose) of a sensor with respect to a model. We use a method similar to the Random Sample and Consensus (RANSAC) algorithm. However, where RANSAC uses random samples of points in the fitting trials, DIRSAC DIRects the sampling by ordering the points according to their contribution to the solution constraints. This is particularly important when the data is quasi-degenerate; meaning that some of the degrees of freedom of the pose are under constrained. In this case, the standard RANSAC algorithm often fails to find the correct solution. Our approach uses mutual information to avoid redundant points that result in degenerate sample sets. We demonstrate our approach on real data and show that in the case of quasi-degenerate data, the proposed algorithm outperforms RANSAC. [ABSTRACT FROM AUTHOR]

Human success and even survival depends on our ability to predict what others will do by guessing what they are thinking. If I accelerate, will he yield? If I propose, will she accept? If I confess, will they forgive? Psychologists call this capacity “theory of mind.” According to current theories, we solve this problem by assuming that others are rational actors. That is, we assume that others design and execute efficient plans to achieve their goals, given their knowledge. But if this view is correct, then our theory of mind is startlingly incomplete. Human action is not always a product of rational planning, and we would be mistaken to always interpret others’ behaviors as such. A wealth of evidence indicates that we often act habitually—a form of behavioral control that depends not on rational planning, but rather on a history of reinforcement. We aim to test whether the human theory of mind includes a theory of habitual action and to assess when and how it is deployed. In a series of studies, we show that human theory of mind is sensitive to factors influencing the balance between habitual and planned behavior. [ABSTRACT FROM AUTHOR]

The vasoactive effects of the synthetic cannabinoid (CB) arachidonyl-2-chloroethylamide (ACEA) was tested in the knee joints of urethane-anaesthetised rats. Experiments were also performed to determine whether these vasomotor responses could be blocked by the selective CB1 receptor antagonists AM251 (N-(piperidin-1-yl)-5-(4-iodophenyl)-1-(2,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide) (10−9 mol) and AM281 (1-(2,4-dichlorophenyl)-5-(4-iodophenyl)-4-methyl-N-4-morpholinyl-1H-pyrazole-3-carboxamide) (10−8 mol), as well as the selective CB2 receptor antagonist AM630 (6-iodo-2-methyl-1-[2-4(morpholinyl)ethyl]-[1H-indol-3-yl](4-methoxyphenyl)methanone) (10−8 mol).Peripheral application of ACEA (10−14–10−9 mol) onto the exposed surface of the knee joint capsule caused a dose-dependent increase in synovial blood flow. The dilator action of the CB occurred within 1 min after drug administration and rapidly returned to control levels shortly thereafter. The maximal vasodilator effect of ACEA corresponded to a 30% increase in articular perfusion compared to control levels.The hyperaemic action of ACEA was not significantly altered by coadministration of AM251, AM281 or AM630 (P>0.05; two-way ANOVA).The transient receptor potential channel vanilloid receptor 1 (TRPV1) antagonist capsazepine (10−6 mol) significantly reduced the vasodilator effect of ACEA on joint blood vessels (P=0.002). Furthermore, destruction of unmyelinated and thinly myelinated joint sensory nerves by capsaicin (8-methyl-N-vanillyl-6-nonenamide) treatment also attenuated ACEA responses (P<0.0005).These data clearly demonstrate a vasodilator effect of the cannabinomimetic ACEA on knee joint perfusion. Rather than a classic CB receptor pathway, ACEA exerts its vasomotor influence by acting via TRPV1 receptors located on the terminal branches of capsaicin-sensitive afferent nerves innervating the joint.British Journal of Pharmacology (2004) 142, 1361–1367. doi:10.1038/sj.bjp.0705902 [ABSTRACT FROM AUTHOR]

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., (© 2019 Cognitive Science Society, Inc.)

The vasoactive effects of the synthetic cannabinoid (CB) arachidonyl-2-chloroethylamide (ACEA) was tested in the knee joints of urethane-anaesthetised rats. Experiments were also performed to determine whether these vasomotor responses could be blocked by the selective CB(1) receptor antagonists AM251 (N-(piperidin-1-yl)-5-(4-iodophenyl)-1-(2,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide) (10(-9) mol) and AM281 (1-(2,4-dichlorophenyl)-5-(4-iodophenyl)-4-methyl-N-4-morpholinyl-1H-pyrazole-3-carboxamide) (10(-8) mol), as well as the selective CB(2) receptor antagonist AM630 (6-iodo-2-methyl-1-[2-4(morpholinyl)ethyl]-[1H-indol-3-yl](4-methoxyphenyl)methanone) (10(-8) mol). Peripheral application of ACEA (10(-14)-10(-9) mol) onto the exposed surface of the knee joint capsule caused a dose-dependent increase in synovial blood flow. The dilator action of the CB occurred within 1 min after drug administration and rapidly returned to control levels shortly thereafter. The maximal vasodilator effect of ACEA corresponded to a 30% increase in articular perfusion compared to control levels. The hyperaemic action of ACEA was not significantly altered by coadministration of AM251, AM281 or AM630 (P>0.05; two-way ANOVA). The transient receptor potential channel vanilloid receptor 1 (TRPV(1)) antagonist capsazepine (10(-6) mol) significantly reduced the vasodilator effect of ACEA on joint blood vessels (P=0.002). Furthermore, destruction of unmyelinated and thinly myelinated joint sensory nerves by capsaicin (8-methyl-N-vanillyl-6-nonenamide) treatment also attenuated ACEA responses (P<0.0005). These data clearly demonstrate a vasodilator effect of the cannabinomimetic ACEA on knee joint perfusion. Rather than a classic CB receptor pathway, ACEA exerts its vasomotor influence by acting via TRPV(1) receptors located on the terminal branches of capsaicin-sensitive afferent nerves innervating the joint.