How can we quantify the degree to which a piece of evidence affects a person's belief? Philosophers investigating theories of Bayesian Confirmation have identified a plurality of potential measures, each with their own virtues and shortcomings. Psychologists meanwhile have largely neglected this question, which has limited their ability to understand differential belief updating, cases where certain individuals or groups respond to the same evidence in different ways. In this study, we examine how competing Bayesian confirmation measures track commonsense notions of evidential strength. We demonstrate how these measures can be computed from participants' belief reports, and identify cases where the measures come apart in their characterization of participants' belief updating. In so doing, this project seeks to build a connections between investigations of psychological belief updating processes and formal epistemic theories of confirmation.


Bayesian confirmation and commonsense notions of evidential strength

When we reason about what could have been, some possibilities seem plausible, and others far-fetched. According to a recent theory, counterfactual possibilities are plausible if they can be generated by making local, probabilistic adjustments to the causes of what actually happened. We provide evidence that people think about counterfactuals in this way even when they have to infer the causes of what happened. We told participants about the diet of a fictional animal, and then asked them simple counterfactual questions. For example, given that the animal has eaten 1 berry today, how much food could it plausibly have eaten instead? When the amount of food eaten by the animal licensed an inference about a causally upstream variable, participants inferred the state of this variable and used it to guide their counterfactual plausibility judgments. 
More generally, the distribution over counterfactual values derived from participants' judgments was remarkably similar to the distribution predicted by the model.


Causal inference shapes counterfactual plausibility

People are often confronted with problems whose complexity exceeds their cognitive capacities. To deal with this complexity, individuals and managers can break complex problems down into a series of subgoals. Which subgoals are most effective depends on people's cognitive constraints and the cognitive mechanisms of goal pursuit. This creates an untapped opportunity to derive practical recommendations for which subgoals managers and individuals should set from cognitive models of bounded rationality. To seize this opportunity, we apply the principle of resource rationality to formulate a mathematically precise normative theory of (self-)management by goal-setting. We leverage this theory to computationally derive optimal subgoals from a resource-rational model of human goal pursuit. Finally, we show that the resulting subgoals improve the problem-solving performance of bounded agents and human participants. This constitutes a first step towards grounding prescriptive theories of management and practical recommendations for goal-setting in computational models of the relevant psychological processes and cognitive limitations.


Toward a normative theory of (self-)management by goal-setting

The expressive power of natural languages depends on their regular compositional structure, which allows us to express and understand an infinite set of messages. However, a complete model of language evolution should also account for irregular exceptions to regular rules, common in natural languages. Historical linguistics has established a correlation between irregularity and frequency in language use, which has been attributed to preferential irregularisation of frequent items, or preferential regularisation of infrequent items. In an iterated learning experiment where participants learn and reproduce a miniature language across multiple generations, we show that this correlation can be explained by the relationship between frequency, regularity and learnability, without needing to appeal to frequency-dependent irregularisation. We find that systems of plural marking regularise across generations of transmission, but that high-frequency items remain irregular. Our results further show that the persistence of irregularity is due to high frequency overriding pressures which normally reduce learnability.


The Relationship Between Frequency and Irregularity in the Evolution of Linguistic Structure: An Experimental Study

Active Inference and its accompanying Bayesian Mechanics
(BM) are important psychological and cognitive science theories.
While there is a strong interaction between the theories
and the philosophical realm, it needs to be clarified what its
metaphysical commitments are. We tease out these commitments
while looking from the perspective of the psychology
of goals. We find that Active Inference cannot account for
the dynamic growth of goals, primarily because of its closed
generative model. We trace the reason for this through the
fixed ‘ontological constraint’ of BM to the inherent ‘logic of
machines’ in mechanistic models, grounding finally in the incompatibility
between substance metaphysics, impredicativity,
and emergence. Thus we argue that Active Inference implicitly
presupposes a Substance metaphysics, yielding the theory no
resource to model novelty, growth, and development observed
in human psychology. We briefly sketch a powerful alternative
grounded in process metaphysics to model biological and
cognitive systems.


Active Inference and Psychology of Goals: A study in Substance and Process Metaphysics

Cumulative culture requires efficient learning mechanisms that can withstand environmental change across generations. We unify two competing theories of the learning mechanism supporting cumulative culture in a common computational framework, distinguishing model-based from model-free social learning. We describe and analyze evolutionary models that explain when and why model-based and model-free social learning are each optimal, and in particular how environmental determines which strategy succeeds. Strikingly, we find that model-based social learning can succeed even in high-volatility environments. These results yield novel predictions concerning cultural variation in social learning mechanisms.


Computational principles underlying the evolution of cultural learning mechanisms

People often prefer simpler explanations (that posit the presence of fewer causes), judging these more probable than more complex alternatives. However, simplicity preferences are only mathematically justified under certain conditions. We examine one case where, mathematically, complexity preferences are justified, and test whether this corresponds to a boundary condition on simplicity preferences. Specifically, we focus on cases where causes occur frequently (rather than rarely), and where explanations specify the absence of additional causes (e.g., “Cause A and not B or C”) rather than remaining agnostic about their presence or absence (e.g., “Cause A”). Study 1 showed that, in these cases, simplicity preferences were attenuated, but not reversed. Study 2 suggests that simplicity preferences partly stemmed from failures to explicitly represent absent causes. We suggest that biases towards oversimplification may arise due to over-application of a cognitively simple version of Ockham’s razor, that is insensitive to the probability of absent causes.


The Edge of Ockham’s Razor: Examining Boundary Conditions on Preferences for Simpler Explanations

This paper presents interdisciplinary and participatory research conducted with laryngectomees with an aim to widen research paradigms for cognitive sciences. Studying daily communication in an interactive context of people living without larynx, we put special emphasis on methodology combining engaged epistemology with ethnographic fieldwork, novel in the domain of this specific perturbation. Results valuable for designers, therapists and research participants themselves demonstrate that the greater awareness is needed of i) influences of anatomical and physiological adaptations on communication and social relations, ii) multimodal and context-dependent alternative strategies of conversation, iii) the crucial role of participants’ agency. We claim that some of our unexpected observations were possible, because of choosing the research approach that extended our epistemological capacities, namely by remaining open to novel interpretive categories and perspectives. We describe how dialogue between epistemologically engaged cognitive science and ethnographic fieldwork facilitated our understanding of the problem in a specific case.

Researching Communication in Context: Engaged Epistemology and Ethnographic Fieldwork Transforms Understanding of Interactions after Laryngectomy

Can one person tell, just from observing how another person’s body moves, what that person is trying to learn? Here, we explore this question through several experiments on “epistemic action recognition”. We filmed volunteers playing a box-shaking game in which they attempted to determine either (a) the number of objects hidden in an opaque box, or (b) the shape of the objects hidden in the opaque box. Then, independent subjects watched these videos and were asked to determine which videos came from which task: Who was shaking for number and who was shaking for shape? Across 3 experiments, observers successfully determined what an actor was trying to learn about the contents of the box, based only on their observed actions (i.e., how they shook it). These results demonstrate that humans have the ability to infer epistemic intent from physical behaviors, adding a new dimension to research on action understanding.

What does learning look Like?: visual Recognition of epistemic intent

We explore whether people can infer the principle of additive commutativity in the absence of cultural support—such as formal schooling and market experience. Our prior research shows that unschooled Tsimane’ adults do not succeed on abstract symbolic and word problem commutativity tasks. In a new study, we implement a concrete commutativity task with tangible tokens, including sums with specified (e.g. 2+1 vs. 1+2) and unspecified (e.g. 2+Dax vs. Dax+2) quantities. We find significantly better performance on concrete tasks than in abstract tasks, even in Tsimane’ participants with limited levels of formal schooling and other cultural supports. This suggests that cultural support is not necessary to understand the commutative principle, but is necessary to apply it to more abstract problems.

The role of cultural support on commutativity at varying levels of abstraction

In categorization research, competing theories are typically compared by fitting their predictions to participant’s responses on a set of test items. The theory that best matches each participant's responses is identified as the strategy the participant is most likely employing. Researchers face considerable difficulty in selecting the best-fitting model due to several factors. In this study, we show the frailty of this approach. Due to pervasive model mimicry and across the similarity- and rule-based models, typical categorization task designs fail to reliably distinguish strategies. Some design modifications that might help are counter-indicated on practical grounds (e.g., carry-over effects); other possible means of improving strategy identification are also discussed.

The Categorization Task is Insufficient to Distinguish between Strategies: A Case for Partial-XOR-like Tasks

There are two hypotheses about complex pattern processing: one proposes that people are predisposed towards simple, local patterns; the other proposes that they prefer more complex hierarchical patterns. Children and adults can both learn to preferentially generate hierarchical sequences. One unanswered question is whether children and adults generate complex patterns spontaneously. We investigated the predispositions or inductive biases of human children and adults in a novel open-ended sequence generation task. We found that 3-to-6-year-old-children and adults display strong biases to generate simple patterns. Both groups overcome this inductive bias by generating more complex hierarchical patterns in subsequent learning tasks. However, children find it more difficult to override their biases. Our results suggest that people possess an inductive bias towards simple patterns that are well-organized but minimally hierarchical. With some experience, humans learn complex hierarchical patterns. These findings reveal substantial encoding flexibility for patterns in humans — a flexibility beginning in childhood.

Underline digital video library

Bayesian confirmation and commonsense notions of evidential strength

Causal inference shapes counterfactual plausibility

Toward a normative theory of (self-)management by goal-setting

The Relationship Between Frequency and Irregularity in the Evolution of Linguistic Structure: An Experimental Study

Active Inference and Psychology of Goals: A study in Substance and Process Metaphysics

Computational principles underlying the evolution of cultural learning mechanisms

The Edge of Ockham’s Razor: Examining Boundary Conditions on Preferences for Simpler Explanations

Researching Communication in Context: Engaged Epistemology and Ethnographic Fieldwork Transforms Understanding of Interactions after Laryngectomy

What does learning look Like?: visual Recognition of epistemic intent

The role of cultural support on commutativity at varying levels of abstraction

The Categorization Task is Insufficient to Distinguish between Strategies: A Case for Partial-XOR-like Tasks

Conceptual Change During Recursive Pattern Learning in Children and Adults

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