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Bayesian Causal Inference (BCI) models multisensory perception as inference about two causal structures: either the sensory data stems from several separate causes or from a common cause. If the sensory evidence supports a common cause, the multimodal sensory input is integrated into a single percept. BCI has been applied to the rubber hand illusion, in which the subject integrates tactile stimulation with seen brush strokes on a rubber hand and experiences said hand as their own. A model of body ownership is relevant for virtual reality design, especially for strengthening avatar embodiment. In previous work, we have criticized the applied BCI model for its extraordinarily wide priors. Here we investigate whether the priors can be narrowed by increasing the sensory evidence. We try to accomplish this by extending the model across time. Preliminary results look encouraging, but further research, especially psychophysical experiments to inform the parameter choices, is needed.


CogSci 2022

Extending the Bayesian Causal Inference of Body Ownership Modell Across Time

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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





When judging the relative difficulty of impossible actions within the context of a magical world like that of Harry Potter, individuals honor real-world causal principles (e.g., assuming that heavier objects would be harder to levitate than lighter ones even though levitation itself is impossible; Shtulman & Morgan, 2017). We examined whether this effect persists when events are presented outside of this context. U.S. (Studies 1 and 2) and Chinese (Study 2) adults were asked to rate the relative difficulty of two impossible events that varied according to an irrelevant causal principle in one of three contexts: present science, future science, or magical. Though Chinese and U.S. adults honored irrelevant causal principles to a similar degree across the three contexts, Chinese adults’ confidence in their judgments varied by context. Additionally, individual differences in cognitive reflection (U.S.) and fantasy engagement (Chinese) related to judgments. Findings indicate that adults honor irrelevant causal constraints when reasoning about the impossible across multiple contexts, though subtle differences exist at both the cultural and individual level.


Cross-Cultural Sensitivity to Context when Reasoning about the Impossible

An outstanding issue in cognitive science is whether the computational principles that apply to causal reasoning also guide the way that participants encode the relations among events in memory. The outcome density effect is a behavioral pattern in causal reasoning in which participants’ causal ratings linearly decrease as the base rate of the effect also decreases, even while contingency remains 0. It is key evidence for Bayesian models of causal reasoning as it reflects decreasing uncertainty. We queried whether it may also, separately, affect memory for events. We measured both recall and causal ratings in a causal learning task to test whether the outcome density manipulation affects causal judgment, recall, or both. We replicated the outcome density effect on causal judgment, lending support to Bayesian models, but found that memory instead exhibited a U-shaped relationship with base rate, and therefore, causal judgment and memory had divergent signatures.

Patterns of Causal Judgements Diverge from Patterns of Recall: a Test of the Outcome Density Effect

Children are capable of using the causal properties of objects to determine category membership. Can they also recognize causal system categories, or abstract patterns of causation that apply across phenomena (Rottman et al., 2012)? For example, do children recognize that one causal chain (e.g. the transmission of salmonella across species) shares the same underlying structure as a chain in another domain (e.g. heat transfer between objects)? If so, recognizing that superficially distinct events share the same underlying causal system may facilitate knowledge transfer. In Study 1, 6- to 7-year-olds and adults, but not 4 to 5-year-olds, are capable of identifying simple machines that share the same causal structure. In ongoing work, we control for low-level perceptual similarities and explore whether adults and children recognize similar causal structures among unique mechanical systems. We also consider whether their recognition of causal systems can be used to support knowledge transfer.

Children’s Recognition of Shared Causal Structure in Mechanical Systems

An accurate mental model of the partner's behavior is fundamental for efficient cooperation. The theory of mind demonstrates that humans are able to create such a model from repeated interactions with their human partners. However, it is an open question whether humans are also willing and capable of taking the perspective of artificial agents and creating similar mental models of agent behavior. We developed a repeated cooperative task that allows us to investigate the process that guides the formation of a specific partner model by repeatedly asking for a prediction of an artificial agent's actions. We found that humans learn to anticipate the artificial partners' behavior if it is goal-directed. An inability to explicitly explain the partner's behavior suggests that this is an implicit learning process. The role of the acquisition of task knowledge in the model of the other agent's behavior is discussed.

Thinking into a machine's mind - Anticipation of an agent's behavior in a cooperative game

Language studies in Asperger Syndrome (AS) report problems in intention interpretation, figurative language and pragmatic abilities. Those abilities require to differentiate constructional and contextual meaning. Previous research use a functional framework to look for literal and figurative language processes. Method. We use Conceptual Metaphor Theory (CMT) and Steen's five steps analysis to compare metaphorical expression frequencies in AS and Typical Development (TD) from an experiential-based perspective. We documented the conceptual metaphors (A is B form), and the metaphor's types: Structural, Orientational, and Ontological. We applied three tasks to elicit speech: (a) conversation task, (b) narration task, and (c) description task. Results. Our Mexico City’s data indicates that AS children are able to produce common metaphorical expressions at the same levels as TD children, at equal ages in Spanish. We found both populations use intentional and contextualized metaphor expressions, and the metaphors are mostly conventionalized expressions previously not considered.


Frequencies of Metaphorical Expressions in Asperger Syndrome and Typical Development

Prior work suggests children understand how speech conveys information and influences others’ minds. Although these studies have focused on communication under ideal conditions, auditory noise plagues the real world, often corrupting the transmission of information. The current study examines how children reason about the impact of auditory noise on communication. Children (N=72, Age:3;0-5;11) watched scenarios where a teacher tells a learner about two toys, but loud auditory noise masks one of the explanations. When asked which toy the learner wants to hear about again, children were more likely to select the noise-masked toy when the learner knew about neither toy (No Knowledge) than when he already knew about the masked toy (Partial Knowledge). However, their preference for the masked toy also increased with age in both conditions. Overall, these results demonstrate children's developing understanding of when and how communication affects listeners' knowledge and information-seeking behaviors.

Young children's reasoning about the epistemic consequences of auditory noise

Apperly and Butterfill (2009) argue that adult theory of mind (ToM) requires two parallel systems. One system, efficient but inflexible, enables rapid judgements by operating without explicit modeling of beliefs, while a separate, effortful system, enables richer predictions over more complex belief encodings. Here, we agree with their qualitative distinction but propose a different model: a single process, but with effortful re-representation leading to two phases of ToM reasoning. Efficient reasoning, in our view, occurs over representations that include actions, but not necessarily explicit belief states. Effortful reasoning, then, involves re-representation of these initial encodings in order to handle errors, resolve real-world conflicts, and fully account for others’ belief states. We present an implemented computational model, based in memory retrieval and structural alignment, that illustrates our approach.

Efficient and Effortful Theory of Mind Reasoning in the AToM Cognitive Model

As efficient systems of communication, languages are usually expected to map meanings to forms in a one-to-one way, using for example the same affix form (e.g., -s in English) every time a particular meaning is intended (e.g., plural number), and placing affixes with the same meaning consistently in the same position (e.g., always suffixal). Forms and positional rules extending over contexts with a common meaning (e.g., plural in 1PL, 2PL, 3PL) are thus considered natural, and those extending over contexts with no consistent common meaning (e.g., 1PL and 3SG) are considered unnatural. Natural patterns are most common cross-linguistically, and most learnable in experiments; however, little is yet know about differences between unnatural classes. In this study we explore syncretism (i.e., use of the same form in different functions) and affix position in the domain of person and number agreement in verbs, both cross-linguistically and in artificial language learning experiments. Results from the two approaches and both phenomena converge in finding a gradient of (un)naturalness. Rather than a dichotomous natural/unnatural distinction, we found that both cross-linguistic frequency and learnability are proportional to the amount of shared feature values among the contexts requiring the same form or position. We argue that a cognitive bias towards similarity-based structure explains our experimental results and could be driving the patterns observed in natural languages.

A Naturalness Gradient Shapes the Learnability and Cross-Linguistic Distribution of Morphological Paradigms

Words in human languages cluster together in phonological neighborhoods more closely than would be expected by chance. But why? One explanation is that large neighborhoods are directly selected for, possibly because they scaffold word learning and production. But it's also possible that they emerge as a byproduct of other constraints or selection pressures operating over real lexica. We advance one such selection pressure as a candidate explanation. A pressure to avoid overloading unique wordforms with homophones may lead to clusters of words that are not identical but similar. Using simulated baselines, we test the viability of this alternative account. We find that a pressure against loading too many meanings on unique wordforms––paired with the phonotactics of a target language––produces lexica with neighborhoods that are at least as large on average as those in real lexica. This does not rule out the possibility of a pro-neighborhood pressure, but it does demonstrate the viability of a parsimonious alternative account based on a pressure against homonymy for which there is independent evidence.

Can a pressure against homophones explain phonological neighborhoods?

Recent experimental and computational modelling work has found that languages are shaped by the referential context in which they operate. Wray and Grace (2007) argue that even compositionality, traditionally regarded as a universal and fundamental feature of human languages, may have only culturally evolved in response to changing social contexts. But how can the referential contexts of individual interactions come to shape the level of compositionality in the language of an entire community? To explore this question, we propose an iterated hierarchical Bayesian model that shows how partner-specific linguistic innovations can be generalized as community-wide features via a context-sensitive pathway. Our simulations show that the degree of compositionality that evolves in the language of a community depends on the communicative needs of its members, but also on the degree of user uncertainty over the nature of those needs, and on the level of heterogeneity in the community's needs.

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Extending the Bayesian Causal Inference of Body Ownership Modell Across Time

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