Believing Does Not Equal Remembering: The Effects of Social Feedback and Objective False Evidence on Belief in Occurrence, Belief in Accuracy, and Recollection

We examined the impact of social feedback and objective false evidence on belief in occurrence, belief in accuracy, and recollection of an autobiographical experience. Participants viewed six virtual scenes (e.g., park) and were tested on their belief/recollection. After 1-week, participants were randomly assigned to four groups. One group received social feedback that one scene was not experienced. A second group received objective false evidence that one of the scenes was not shown. A third group received both social feedback and objective false evidence and the control group did not receive any manipulation. Belief in occurrence dropped considerably in the social feedback group and in the combined group. Also, nonbelieved memories were most likely to occur in participants receiving both social feedback and objective false evidence. We show that social feedback and objective false evidence undermine belief in occurrence, but that they leave belief in accuracy and recollection unaffected

Reasoning about quantities and concepts: studies in social learning

We live and learn in a ‘society of mind’. This means that we form beliefs not just based on our own observations and prior expectations but also based on the communications from other people, such as our social network peers. Across seven experiments, I study how people combine their own private observations with other people’s communications to form and update beliefs about the environment. I will follow the tradition of rational analysis and benchmark human learning against optimal Bayesian inference at Marr’s computational level. To accommodate human resource constraints and cognitive biases, I will further contrast human learning with a variety of process level accounts. In Chapters 2–4, I examine how people reason about simple environmental quantities. I will focus on the effect of dependent information sources on the success of group and individual learning across a series of single-player and multi-player judgement tasks. Overall, the results from Chapters 2–4 highlight the nuances of real social network dynamics and provide insights into the conditions under which we can expect collective success versus failures such as the formation of inaccurate worldviews. In Chapter 5, I develop a more complex social learning task which goes beyond estimation of environmental quantities and focuses on inductive inference with symbolic concepts. Here, I investigate how people search compositional theory spaces to form and adapt their beliefs, and how symbolic belief adaptation interfaces with individual and social learning in a challenging active learning task. Results from Chapter 5 suggest that people might explore compositional theory spaces using local incremental search; and that it is difficult for people to use another person’s learning data to improve upon their hypothesis

The Sliced Gaussian Mixture Filter with Adaptive State Decomposition Depending on Linearization Error

In this paper, a novel nonlinear/non-linear model decomposition for the Sliced Gaussian Mixture Filter is presented. Based on the level of nonlinearity of the model, the overall estimation problem is decomposed into a severely nonlinear and a slightly nonlinear part, which are processed by different estimation techniques. To further improve the efficiency of the estimator, an adaptive state decomposition algorithm is introduced that allows decomposition according to the linearization error for nonlinear system and measurement models. Simulations show that this approach has orders of magnitude less complexity compared to other state of the art estimators, while maintaining comparable estimation errors

Einfluss des TGF-β2 59941 A/G Polymorphismus auf die Lungenfibrose bei Sarkoidose

Die Ätiologie der Sarkoidose ist bisher unklar. Umwelteinflüsse und genetische Dispositionen stehen gleichermaßen im Verdacht zu der Entstehung der Erkrankung beizutragen. In dieser Arbeit wurde der 59941 A/G (rs1891467) Polymorphismus des TGF-β2 Gens hinsichtlich seines Einflusses auf den Krankheitsverlauf mit möglicher Fibrosierung bei Sarkoidosepatienten untersucht. In vorangegangenen Studien konnte TGF-β mit seinen einzelnen Isoformen quantitativ mit fibrotischen Umbauvorgängen der Lunge in Verbindung gebracht werden. Die hier verwendeten Probandenkollektive formierten sich aus 296 Sarkoidosepatienten, die entsprechend ihres Erkrankungsgrades in röntgenologische Stadien eingeteilt wurden, sowie aus 377 gesunden Vergleichspersonen. Alle Teilnehmer wurden für den 59941 A/G (rs1891467) Polymorphismus genotypisiert. Assoziationsanalysen ergaben signifikante Ergebnisse für die Patientengruppe der chronischen Sarkoidosepatienten im Vergleich zu denen mit nicht chronischen Verläufen und den gesunden Normalpersonen. Mit einem p-Wert von 0,001 war das Vorkommen des homozygoten Mutationstyps in der Gruppe der chronischen Patienten zehn Mal seltener zu finden als bei den Erkrankten mit nicht chronischen bzw. akuten Verläufen. Eine Assoziation zur Entstehung einer Lungenfibrose konnte nicht direkt nachgewiesen werden, jedoch war ein eindeutiger Trend festzustellen. Vor dem Hintergrund der hier erarbeiteten Daten kann die Hypothese aufgestellt werden, dass der 59941 A/G (rs1891467) Polymorphismus im TGF-β2 Gen, als Teil eines multifaktoriellen Geschehens, bei vorhandener Mutation eine protektive Funktion haben könnte

Understanding Social Reasoning in Language Models with Language Models

As Large Language Models (LLMs) become increasingly integrated into our everyday lives, understanding their ability to comprehend human mental states becomes critical for ensuring effective interactions. However, despite the recent attempts to assess the Theory-of-Mind (ToM) reasoning capabilities of LLMs, the degree to which these models can align with human ToM remains a nuanced topic of exploration. This is primarily due to two distinct challenges: (1) the presence of inconsistent results from previous evaluations, and (2) concerns surrounding the validity of existing evaluation methodologies. To address these challenges, we present a novel framework for procedurally generating evaluations with LLMs by populating causal templates. Using our framework, we create a new social reasoning benchmark (BigToM) for LLMs which consists of 25 controls and 5,000 model-written evaluations. We find that human participants rate the quality of our benchmark higher than previous crowd-sourced evaluations and comparable to expert-written evaluations. Using BigToM, we evaluate the social reasoning capabilities of a variety of LLMs and compare model performances with human performance. Our results suggest that GPT4 has ToM capabilities that mirror human inference patterns, though less reliable, while other LLMs struggle

Belief revision in a micro-social network:Modeling sensitivity to statistical dependencies in social learning

In both professional domains and everyday life, people mustintegrate their own experience with reports from social networkpeers to form and update their beliefs. It is therefore importantto understand to what extent people accommodate the statis-tical dependencies that give rise to correlated belief reportsin social networks. We investigate adults’ ability to integratesocial evidence appropriately in a political scenario, varyingthe dependence between the sources of network peers’ beliefs.Using a novel interface that allows participants to express theirprobabilistic beliefs visually, we compare participants against anormative Bayesian standard. We find that they distinguish thevalue of evidence from dependent versus independent sources,but that they also treated social sources as substantially weakerevidence than direct experience. The value of our elicitationmethodology and the implications of our results for modelinghuman-like belief revision in social networks are discussed

Modeling infant object perception as program induction

Infants expect physical objects to be rigid and persist through space and time and in spite of occlusion. Developmentists frequently attribute these expectations to a "core system" for object recognition. However, it is unclear if this move is necessary. If object representations emerge reliably from general inductive learning mechanisms exposed to small amounts of environment data, it could be that infants simply induce these assumptions very early. Here, we demonstrate that a domain general learning system, previously used to model concept learning and language learning, can also induce models of these distinctive "core" properties of objects after exposure to a small number of examples. Across eight micro-worlds inspired by experiments from the developmental literature, our model generates concepts that capture core object properties, including rigidity and object persistence. Our findings suggest infant object perception may rely on a general cognitive process that creates models to maximize the likelihood of observationsComment: 3 pages, 3 figures, accepted at CCN conference 202

The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation

This paper addresses the efficient state estimation for mixed linear/nonlinear dynamic systems with noisy measurements. Based on a novel density representation - sliced Gaussian mixture density - the decomposition into a (conditionally) linear and nonlinear estimation problem is derived. The systematic approximation procedure minimizing a certain distance measure allows the derivation of (close to) optimal and deterministic estimation results. This leads to high-quality representations of the measurement-conditioned density of the states and, hence, to an overall more efficient estimation process. The performance of the proposed estimator is compared to state-of-the-art estimators, like the well-known marginalized particle filter

Social Contract AI: Aligning AI Assistants with Implicit Group Norms

We explore the idea of aligning an AI assistant by inverting a model of users' (unknown) preferences from observed interactions. To validate our proposal, we run proof-of-concept simulations in the economic ultimatum game, formalizing user preferences as policies that guide the actions of simulated players. We find that the AI assistant accurately aligns its behavior to match standard policies from the economic literature (e.g., selfish, altruistic). However, the assistant's learned policies lack robustness and exhibit limited generalization in an out-of-distribution setting when confronted with a currency (e.g., grams of medicine) that was not included in the assistant's training distribution. Additionally, we find that when there is inconsistency in the relationship between language use and an unknown policy (e.g., an altruistic policy combined with rude language), the assistant's learning of the policy is slowed. Overall, our preliminary results suggest that developing simulation frameworks in which AI assistants need to infer preferences from diverse users can provide a valuable approach for studying practical alignment questions.Comment: SoLaR NeurIPS 2023 Workshop (https://solar-neurips.github.io/