The Cognitive Architecture of Perceived Animacy: Intention, Attention, and Memory.

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.

Rational Inference of Beliefs and Desires From Emotional Expressions.

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.

Plans, Habits, and Theory of Mind.

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.

The cannabinomimetic arachidonyl-2-chloroethylamide (ACEA) acts on capsaicin-sensitive TRPV1 receptors but not cannabinoid receptors in rat joints.

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.

Attenuation of knee joint inflammation by peripherally administered endomorphin-1.

Endomorphin-1 is a selective endogenous ligand for the micro-opioid receptor, and this study investigated the effect of endomorphin-1 on rat knee joint inflammation by examining the ability of the neuropeptide to modulate synovial protein extravasation. Acute joint inflammation was induced by intraarticular injection of 2% kaolin followed by 2% carrageenan and the animals allowed to recover for 3 h. Immunohistochemical examination of these inflamed joints revealed endomorphin-1-like immunoreactive nerves in deep synovium with a proportion of the nerve fibers occurring in close proximity to synovial blood vessels. Perfusion of inflamed knees with exogenous endomorphin-1 across the dose range 10-9-10-6 M produced a significant reduction in synovial vascular permeability with the 10-7M dose producing the greatest fall in protein exudation (approx 55%). These effects were blocked by the specific micro-opioid receptor antagonist CTOP. Destruction of knee joint unmyelinated afferent nerve fibers by capsaicin treatment significantly attenuated the anti-inflammatory effects of endomorphin-1, suggesting that the peptide is acting via a neurogenic mechanism. The findings of this study indicate that endomorphin-1 acts peripherally in knee joints to reduce synovial protein extravasation. These anti-inflammatory effects are mediated by micro-opioid receptors located on capsaicin-sensitive afferent nerves.

Inferring the intentional states of autonomous virtual agents.

Inferring the mental states of other agents, including their goals and intentions, is a central problem in cognition. A critical aspect of this problem is that one cannot observe mental states directly, but must infer them from observable actions. To study the computational mechanisms underlying this inference, we created a two-dimensional virtual environment populated by autonomous agents with independent cognitive architectures. These agents navigate the environment, collecting "food" and interacting with one another. The agents' behavior is modulated by a small number of distinct goal states: attacking, exploring, fleeing, and gathering food. We studied subjects' ability to detect and classify the agents' continually changing goal states on the basis of their motions and interactions. Although the programmed ground truth goal state is not directly observable, subjects' responses showed both high validity (correlation with this ground truth) and high reliability (correlation with one another). We present a Bayesian model of the inference of goal states, and find that it accounts for subjects' responses better than alternative models. Although the model is fit to the actual programmed states of the agents, and not to subjects' responses, its output actually conforms better to subjects' responses than to the ground truth goal state of the agents.

Action understanding as inverse planning.

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., Nádasdy, Z., Csibra, G., & Biró, 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.