Chris L. Baker

Rational quantitative attribution of beliefs, desires and percepts in human mentalizing

Social cognition depends on our capacity for ‘mentalizing’, or explaining an agent’s behaviour in terms of their mental states. The development and neural substrates of mentalizing are well-studied, but its computational basis is only beginning to be probed. Here we present a model of core mentalizing computations: inferring jointly an actor’s beliefs, desires and percepts from how they move in the local spatial environment. Our Bayesian theory of mind (BToM) model is based on probabilistically inverting artificial-intelligence approaches to rational planning and state estimation, which extend classical expected-utility agent models to sequential actions in complex, partially observable domains. The model accurately captures the quantitative mental-state judgements of human participants in two experiments, each varying multiple stimulus dimensions across a large number of stimuli. Comparative model fits with both simpler ‘lesioned’ BToM models and a family of simpler non-mentalistic motion features reveal the value contributed by each component of our model.

Probabilistic Gaze Imitation and Saliency Learning in a Robotic Head

Imitation is a powerful mechanism for transferring knowledge from an instructor to a naïve observer, one that is deeply contingent on a state of shared attention between these two agents. In this paper we present Bayesian algorithms that implement the core of an imitation learning framework. We use gaze imitation, coupled with task-dependent saliency learning, to build a state of shared attention between the instructor and observer. We demonstrate the performance of our algorithms in a gaze following and saliency learning task implemented on an active vision robotic head. Our results suggest that the ability to follow gaze and learn instructor-and task-specific saliency models could play a crucial role in building systems capable of complex forms of human-robot interaction.

Modeling Human Plan Recognition Using Bayesian Theory of Mind

The human brain is the most powerful plan-recognition system we know. Central to the brain’s remarkable plan-recognition capacity is a theory of mind (ToM): our intuitive conception of other agents’ mental states—chiefly, beliefs and desires—and how they cause behavior. We present a Bayesian framework for ToM-based plan recognition, expressing generative models of belief- and desire-dependent planning in terms of partially observable Markov decision processes (POMDPs), and reconstructing an agent’s joint belief state and reward function using Bayesian inference, conditioned on observations of the agent’s behavior in the context of its environment. We show that the framework predicts human judgments with surprising accuracy, and substantially better than alternative accounts. We propose that “reverse engineering” human ToM by quantitatively evaluating the performance of computational cognitive models against data from human behavioral experiments provides a promising avenue for building plan recognition systems.

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.

Rational Inference of Beliefs and Desires From Emotional Expressions

Abstract We investigated peoples 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 agents 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 agents 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.

Learning temporal clusters with synaptic facilitation and lateral inhibition

Short-term synaptic plasticity has been proposed as a way for cortical neurons to process temporal information. We present a model network that uses short-term plasticity to implement a temporal clustering algorithm. The models facilitory synapses learn temporal signals drawn from mixtures of nonlinear processes. Units in the model correspond to populations of cortical pyramidal cells arranged in columns; each column consists of neurons with similar spatiotemporal receptive fields. Clustering is based on mutual inhibition similar to Kohonens SOMs. A generalized expectation maximization (GEM) algorithm, guaranteed to increase model likelihood with each iteration, learns the synaptic parameters.