Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague's task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches.Comment: Accepted at AAAI 1

Modeling Human Ad Hoc Coordination

Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-agent collaboration.Comment: AAAI 201

Bayesian theory of mind

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 127-139).This thesis proposes a computational framework for understanding human Theory of Mind (ToM): our conception of others' mental states, how they relate to the world, and how they cause behavior. Humans use ToM to predict others' actions, given their mental states, but also to do the reverse: attribute mental states - beliefs, desires, intentions, knowledge, goals, preferences, emotions, and other thoughts - to explain others' behavior. The goal of this thesis is to provide a formal account of the knowledge and mechanisms that support these judgments. The thesis will argue for three central claims about human ToM. First, ToM is constructed around probabilistic, causal models of how agents' beliefs, desires and goals interact with their situation and perspective (which can differ from our own) to produce behavior. Second, the core content of ToM can be formalized using context-specific models of approximately rational planning, such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), and Markov games. ToM reasoning will be formalized as rational probabilistic inference over these models of intentional (inter)action, termed Bayesian Theory of Mind (BToM). Third, hypotheses about the structure and content of ToM can be tested through a combination of computational modeling and behavioral experiments. An experimental paradigm for eliciting fine-grained ToM judgments will be proposed, based on comparing human inferences about the mental states and behavior of agents moving within simple two-dimensional scenarios with the inferences predicted by computational models. Three sets of experiments will be presented, investigating models of human goal inference (Chapter 2), joint belief-desire inference (Chapter 3), and inference of interactively-defined goals, such as chasing and fleeing (Chapter 4). BToM, as well as a selection of prominent alternative proposals from the social perception literature will be evaluated by their quantitative fit to behavioral data. Across the present experiments, the high accuracy of BToM, and its performance relative to alternative models, will demonstrate the difficulty of capturing human social judgments, and the success of BToM in meeting this challenge.by Chris L. Baker.Ph.D

Eating As Treatment (EAT): A Stepped-Wedge, Randomized Controlled Trial of a Health Behavior Change Intervention Provided by Dietitians to Improve Nutrition in Patients With Head and Neck Cancer Undergoing Radiation Therapy (TROG 12.03)

Purpose: Malnutrition in head and neck cancer (HNC) treatment is common and associated with poorer morbidity and mortality outcomes. This trial aimed to improve nutritional status during radiation therapy (RT) using a novel method of training dietitians to deliver psychological techniques to improve nutritional behaviors in patients with HNC. Methods and Materials: This trial used a stepped-wedge, randomized controlled design to assess the efficacy of the Eating As Treatment (EAT) program. Based on motivational interviewing and cognitive behavioral therapy, EAT was designed to be delivered by oncology dietitians and integrated into their clinical practice. During control steps, dietitians provided treatment as usual, before being trained in EAT and moving into the intervention phase. The training was principles based and sought to improve behavior-change skills rather than provide specific scripts. Patients recruited to the trial (151 controls, 156 intervention) were assessed at 4 time points (the first and the final weeks of RT, and 4 and 12 weeks afterward). The primary outcome was nutritional status at the end of RT as measured by the Patient-Generated Subjective Global Assessment. Results: Patients who received the EAT intervention had significantly better scores on the primary outcome of nutritional status at the critical end-of-treatment time point (β = −1.53 [−2.93 to −.13], P =.03). Intervention patients were also significantly more likely than control patients to be assessed as well-nourished at each time point, lose a smaller percentage of weight, have fewer treatment interruptions, present lower depression scores, and report a higher quality of life. Although results were not statistically significant, patients who received the intervention had fewer and shorter unplanned hospital admissions. Conclusions: This trial is the first of its kind to demonstrate the effectiveness of a psychological intervention to improve nutrition in patients with HNC who are receiving RT. The intervention provides a means to ameliorate malnutrition and the important related outcomes and consequently should be incorporated into standard care for patients receiving RT for HNC

Making sense of real-world scenes

To interact with the world, we have to make sense of the continuous sensory input conveying information about our environment. A recent surge of studies has investigated the processes enabling scene understanding, using increasingly complex stimuli and sophisticated analyses to highlight the visual features and brain regions involved. However, there are two major challenges to producing a comprehensive framework for scene understanding. First, scene perception is highly dynamic, subserving multiple behavioral goals. Second, a multitude of different visual properties co-occur across scenes and may be correlated or independent. We synthesize the recent literature and argue that for a complete view of scene understanding, it is necessary to account for both differing observer goals and the contribution of diverse scene properties

Transcranial magnetic stimulation to the occipital place area biases gaze during scene viewing

We can understand viewed scenes and extract task-relevant information within a few hundred milliseconds. This process is generally supported by three cortical regions that show selectivity for scene images: parahippocampal place area (PPA), medial place area (MPA) and occipital place area (OPA). Prior studies have focused on the visual information each region is responsive to, usually within the context of recognition or navigation. Here, we move beyond these tasks to investigate gaze allocation during scene viewing. Eye movements rely on a scene’s visual representation to direct saccades, and thus foveal vision. In particular, we focus on the contribution of OPA, which is i) located in occipito-parietal cortex, likely feeding information into parts of the dorsal pathway critical for eye movements, and ii) contains strong retinotopic representations of the contralateral visual field. Participants viewed scene images for 1034 ms while their eye movements were recorded. On half of the trials, a 500 ms train of five transcranial magnetic stimulation (TMS) pulses was applied to the participant’s cortex, starting at scene onset. TMS was applied to the right hemisphere over either OPA or the occipital face area (OFA), which also exhibits a contralateral visual field bias but shows selectivity for face stimuli. Participants generally made an overall left-to-right, top-to-bottom pattern of eye movements across all conditions. When TMS was applied to OPA, there was an increased saccade latency for eye movements toward the contralateral relative to the ipsilateral visual field after the final TMS pulse (400ms). Additionally, TMS to the OPA biased fixation positions away from the contralateral side of the scene compared to the control condition, while the OFA group showed no such effect. There was no effect on horizontal saccade amplitudes. These combined results suggest that OPA might serve to represent local scene information that can then be utilized by visuomotor control networks to guide gaze allocation in natural scenes

Cerebral perfusion in chronic stroke: Implications for lesion-symptom mapping and functional MRI

Lesion-symptom mapping studies are based upon the assumption that behavioral impairments are directly related to structural brain damage. Given what is known about the relationship between perfusion deficits and impairment in acute stroke, attributing specific behavioral impairments to localized brain damage leaves room for speculation, as impairments could also reflect abnormal neurovascular function in brain regions that appear structurally intact on traditional CT and MRI scans. Compared to acute stroke, the understanding of cerebral perfusion in chronic stroke is far less clear. Utilizing arterial spin labeling (ASL) MRI, we examined perfusion in 17 patients with chronic left hemisphere stroke. The results revealed a decrease in left hemisphere perfusion, primarily in peri-infarct tissue. There was also a strong relationship between increased infarct size and decreased perfusion. These findings have implications for lesion-symptom mapping studies as well as research that relies on functional MRI to study chronic stroke