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The paper aims to examine participants’ open-text ‘think aloud’ explanations of their reasoning while making a judgement about an ambiguous scenario. It aims to consider this data in light of frameworks such as causal modelling, intuitive theories, coherence and the story model. Consistent with these frameworks, we find that participants bring in a large amount of world knowledge to connect ambiguous evidence to unobserved, inferred variables and, via these, to the target judgement. We attempt to represent these chains of inferences using causal diagrams and find that participants interpretations of the scenario can be lumped into one of two distinct causal models, each presenting an internally coherent ‘image’ of the ambiguous scenario. Furthermore, participants’ judgement predicts which of those two models they adhere to. We discuss the limitations and merits of this methodological approach for investigating these types of frameworks.

CogSci 2022

Exploring the Richness of Human Causal Reasoning with Think Aloud Data

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Human collaboration often involves a decision to pursue one out of multiple comparable goals, in which case it is challenging to remain committed to the same goal collectively. Philosophical theories as well as empirical evidence from developmental psychology suggest that humans, having shared intentionality as an underlying cognitive structure, may be able to form joint commitment in pursuing a collective goal without communication. By conducting experiments in a real-time cooperative hunting game that heavily relies on visual perception, we demonstrated that humans established and maintained robust cooperation with high-quality hunting, even with a large number of potential targets. Additionally, we showed that a Bayesian imagined “We” (IW) model within a joint commitment framework, could capture humans’ robustness in resisting alternative targets with relatively high quality of hunting. This poses a contrast with a Reward Sharing (RS) model that, despite performing proficiently in pursuing a single goal, mostly exhibited low-quality hunting and whose teaming fell apart as available targets increased. In a hybrid team simulation experiment, the IW model could better mimic the intentions of human hunters compared to the RS model. Together, the success of the persevered group commitment in humans suggests that shared intentionality is a pivotal element in human cooperation. Moreover, the similarity between the performance of humans and the IW model sheds light on the computational formulation of shared intentionality and further advances our understanding of the nature of cooperation.

Exploring an Imagined “We” in Human Collective Hunting: Joint Commitment within Shared Intentionality

Online communities rely on their members to understand and follow community norms, which they learn by observing others and the consequences of their behavior, seeing codes of conduct, and receiving feedback via moderation. Here, to determine the contribution of each source of learning to the preservation of a social norm, we extend cultural consensus theory, a mathematical framework for identifying the cultural consensus in a community. In particular, we extend the model to include learning from experience, centralized moderation, and decentralized moderation, three features commonly found in online communities. We then apply the extended model to data from an online community dedicated to preserving a norm related to the psychophysical scaling of intersubjective notions of beauty derived from facial aesthetics. We find that users' perceptual alignment with the norm before enculturation predicts involvement in the community and that experience in the community is an important indicator for group perceptual learning.

Learning and enforcing a cultural consensus in online communities

How altruistic behavior evolves despite its evolutionary cost is still an intriguing question. Using Neural Network and Gradient descent algorithms, we proposed a mixed computational model of fitness competition among three artificial agents (predator, altruistic prey, recipient prey), in the zero-sum game environment. We found that altruism emerged without direct reciprocity when the predator invested in altruism aiming to use the prey’s altruistic behavior as “bait” to fish for more prey. For the decisive factor of this mechanism, we demonstrated that the long-term decision-making of a predator enhanced its investment in the prey's altruistic behavior, which leads to a significant increase in altruism and fitness in altruistic prey. We interpreted our findings from economic, evolutionary, and psychological perspectives, connecting zero-sum economies, K-selection, and third-party emotional decision-making to the emergence and maintenance of altruistic behavior.

Fishing Free-Riders using Altruism: Zero-Sum Fitness Competition in Prey-Predator System

The current study investigates children’s understanding of the social dynamics of complex groups. We asked children to use relative differences in intragroup status to predict the behaviors of individuals. Specifically, who do children (ages 3 to 10, n = 120) and adults (n = 34) believe a subordinate “worker” would be loyal to (another worker or to their “boss”), and whom the worker would prefer to socialize with? Young children predicted that workers would be loyal to other workers, but as age increased so did children’s tendency to predict that workers would be loyal to bosses. Regardless of age, children and adults believed that workers would prefer to spend time with other workers. These results have important implications for how children understand and navigate nuanced power differentials within a group.

‘An ounce of loyalty’: Children’s expectations about loyalty and preference for in-group members and authority figures

A milestone in cognitive development is understanding numerals to represent the exact number of discrete items in a set (i.e., the cardinal principle). This development has received much attention, but little is known about its relation to understanding numbers as measures of continuous quantity (e.g., “six blocks long” versus “six blocks”). To investigate this, 90 children were asked to complete two tasks: a give-a-number task, to assess cardinality knowledge, and a novel give-a-line task, to assess measurement knowledge. As expected, accuracy was greater on the give-a-number task than the give-a-line task. More unexpectedly, the quality of performance on the give-a-number task was as often negatively associated with quality of performance on the give-a-line task as it was positive correlated. Specifically, when asked to create a line N-blocks long, children who gave only approximately correct answers on the give-a-number task often outperformed children who gave exactly correct answers on the same task. These findings indicate that an approximate—and purportedly less mature—understanding of number possesses the hidden strength of being more flexible and suitable for measuring length.

Children’s Cardinality Knowledge Isn’t Always Beneficial

Dogs have a unique evolutionary relationship with humans and are relied upon in a variety of working roles, yet little is known about the kinds of visual information available to them, as well as how they direct their attention within their environment. The present study, inspired by comparable work in infants, aimed to categorize the visual statistics (specifically the identity of objects) available to dogs during a common event in their daily lives, a walk. Using a head-mounted eye-tracking apparatus that was custom designed for dogs, four dogs walked on a pre-determined route outdoors under naturalistic conditions generating a total of 49,431 frames for analysis. On average, there were few individual differences between dogs. Dogs looked proportionally more to people and plants than to other object categories in their environment, like the sky which they appeared to consider as background. The results of this project provide a foundational step towards understanding how dogs’ look at and interact with their physical world, opening up avenues for future research into how they complete tasks, and learn and make decisions, both independently and with a human social partner.

Categorizing Dogs’ Real World Visual Statistics

Central to rationality is the ability to think about our beliefs, and make sure they are based on good reasons. If we have several conflicting reasons to believe something, we wait for more information before making a decision, perhaps rechecking our original reasons before proceeding. In these studies, we presented great apes and young children with conflicting evidence about the location of a reward. We found that apes double-checked the evidence for their original choice before making a final decision, revealing an awareness of their own beliefs and reasoning not hitherto documented. Young children, in contrast, were more sensitive to peer disagreement than conflicting physical evidence, illustrating the distinctively social nature of human rationality.

Great Apes and Human Children Rationally Monitor their Decisions

From birth, humans constantly make decisions about what to look at and for how long. Yet the mechanism behind such decision-making remains poorly understood. Here we present the rational action, noisy choice for habituation (RANCH) model. RANCH is a rational learning model that takes noisy perceptual samples from stimuli and makes sampling decisions based on Expected Information Gain (EIG). The model captures key patterns of looking time documented in developmental research: habituation and dishabituation. We evaluated the model with adult looking time collected from a paradigm analogous to the infant habituation paradigm. We compared RANCH with baseline models (no learning model, no perceptual noise model) and models with alternative linking hypotheses (Surprisal, KL divergence). We showed that 1) learning and perceptual noise are critical assumptions of the model, and 2) Surprisal and KL are good proxies for EIG under the current learning context.

Habituation reflects optimal exploration over noisy perceptual samples

A central question in cognitive science is how semantic information is mentally represented. Two dominant theories of semantic representation are language-based distributional semantic models (which suggest that word meaning is based on which words co-occur in language) and semantic networks based on word associations (which suggest that words are represented as a network in which words with closer meanings are more closely linked). We investigate the issue of semantic representation through the lens of core vocabulary -- the set of words that are most central in the mental lexicon -- which these two theories make different predictions about. We report on the results of an experiment that tests which measure of core vocabulary most closely aligns with human behaviour in a word-guessing game where the aim was to identify a target word given a set of semantically related words as hints. Target and hint words, which varied across trials, were generated from different core vocabulary lists corresponding to these different theories. Results revealed that the type of hint words did not affect performance, but that better performance was attained for target words derived from word associations than from natural language distributional statistics. Follow-up analyses ruled out several alternate explanations. Our results suggest that the semantic information reflected in word associations may be more involved in the efficient identification of lexical meaning.

Core words in semantic representation

Can we know a word by the company it keeps? Aspects of meaning that concern physical interactions might be particularly difficult to learn from language alone. Glenberg & Robertson (2000) found that although human comprehenders were sensitive to the distinction between afforded and nonafforded actions, distributional semantic models were not. We tested whether technological advances have made distributional models more sensitive to affordances by replicating their experiment with modern Neural Language Models (NLMs). We found that only one NLM (GPT-3) was sensitive to the affordedness of actions. Moreover, GPT-3 accounted for only one third of the effect of affordedness on human sensibility judgments. These results imply that people use processes that go beyond distributional statistics to understand linguistic expressions, and that NLP systems may need to be augmented with such capabilities.

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Exploring the Richness of Human Causal Reasoning with Think Aloud Data

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Exploring an Imagined “We” in Human Collective Hunting: Joint Commitment within Shared Intentionality

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