Canada

We see the external world as consisting not only of objects and their parts, but also of relations that hold between them. Visual analogy, which depends on similarities between relations, provides a clear example of how perception supports reasoning. Here we report an experiment in which we quantitatively measured the human ability to find analogical mappings between parts of different objects, where the objects to be compared were drawn either from the same category (e.g., images of two mammals, such as a dog and a horse), or from two dissimilar categories (e.g., a chair image mapped to a cat image). Humans showed systematic mapping patterns, but with greater variability in mapping responses when objects were drawn from dissimilar categories. We simulated the human response of analogical mapping using a computational model of mapping between 3D objects, visiPAM (visual Probabilistic Analogical Mapping). VisiPAM takes point-cloud representations of two 3D objects as inputs, and outputs the mapping between analogous parts of the two objects. VisiPAM consists of a visual module that constructs structural representations of individual objects, and a reasoning module that identifies a probabilistic mapping between parts of the two 3D objects. Model simulations not only capture the qualitative pattern of human mapping performance cross conditions, but also approach human-level reliability in solving visual analogy problems.

CogSci 2022

From Vision to Reasoning: Probabilistic Analogical Mapping Between 3D Objects

technical paper

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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. 





Third party punishment (TPP), or altruistic punishment, is specifically human prosocial behavior. TPP denotes the administration of a sanction to a transgressor by an individual that is not affected by the transgression. In some evolutionary accounts, TPP is considered crucial for the stability of cooperation and solidarity in larger groups formed by genetically unrelated individuals. Belief in free will (BFW), on the other hand, is the idea that humans have control over their behavior. BFW is a human universal notion that, in some studies, has been found to be supportive of prosocial behavior. In our study, we examined the effect of BFW on TPP under high and low affect scenarios through optical brain imaging (fNIRS). We hypothesized that in low affect cases, there would be a positive correlation between the strength of the BFW and the severity of the punishment inflicted. Obtained results and related statistical analyses indicate that participants with higher degree of BFW have more neural activation in their right dorsolateral prefrontal cortex (DLPFC) (hbo and hbt measures) in high affect scenarios, whereas the participants with lower degree of BFW have higher levels of neural activation in the medial PFC (hbo and hbt measures) in low affect scenarios. These empirical findings are in line with the research findings in the relevant academic literature and support the hypothesis that the degree of BFW influences punishment decisions.

The Neural Correlates of the Effect of Belief in Free Will on Third-Party Punishment: An Optical Brain Imaging (fNIRS) Study

Dual character concepts have recently received significant attention from experimental philosophers. They are usually defined as possessing two independent criteria for categorization, each associated with its own sense. One of these criteria is descriptive, while the other is normative. Almost all examples discussed in the literature involve a positively evaluated normative element (e.g., the concept of scientist is associated with the pursuit of empirical truth, something that we value). But can dual character concepts also involve morally bad elements? We report the first evidence suggesting that they can. Our study contrasted pairs of positive and negative concepts (e.g., friend/enemy, joy/sadness) and neutral concepts (e.g., baker). We found that negatively valenced concepts such as traitor, enemy, superficial, or sadness have a dual character. We also observed that positive concepts were significantly more likely to have a dual character than negative ones, but the effect size of this difference was small.

Are There Bad Dual Character Concepts?

Morality is a critical aspect of life––it influences how we think, design systems, and even the stories we tell. Looking to the popularity of true crime stories and characters like Dexter Morgan, it seems that our preferences are toward exploring moral ambiguity and moral badness. Across two experiments, we examine what moral ambiguity is and what kinds of moral information spark curiosity and explanation-seeking. In Experiment 1, we manipulate moral ambiguity to mean someone with conflicting moral character, and we predict those individuals will trigger curiosity more than morally consistent people. Results suggest that both morally ambiguous and immoral minds pique curiosity for explanations. In Experiment 2, we find that when ambiguity is instead operationalized as what is typical or average, we are curious about morally deviant things. This research points to critical differences in the kinds of moral minds we are curious to learn more about.


What is moral ambiguity and when does it trigger curiosity?

Artificial Intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems – enabling people and organisations to form judgements of others at scale. While this capability has many useful applications – e.g., matching romantic partners who are aligned in their moral principles, it also raises many ethical questions. For example, there is widespread concern about the use of social credit systems in the political domain. In this project, we approach this topic from a psychological perspective. With experimental evidence, we show that the acceptability of moral scoring by AI depends on its perceived accuracy, and that perceived accuracy is compromised by people's tendency to see themselves as morally peculiar, and thus less characterizable by AI. That is, we suggest that people overestimate the peculiarity of their moral profile, believe that AI will neglect this peculiarity, and resist for this reason the introduction of moral scoring by AI.


The Quantified Moral Self

What is the relationship between trust and perceived agency? The present study experimentally investigated the effect of people’s perception of a robot’s compliance (and resistance) to social norms on their evaluation of a robot’s perceived agency, performance trust, and moral trust. Participants reported a norm-conforming robot to have higher perceived agency and a greater sense of trust than a robot that violated social norms. 
We also found that perceived agency, regardless of how much a robot followed norms, was correlated with trust. We interpret this finding as evidence that as people see a robot as having agency, they trust it more

Perceived Agency Changes Performance and Moral Trust in Robots

A key property of human cognition is its ability to generate novel predictions about unfamiliar situations by completing a partially-specified relation or an analogy. Here, we present a computational model capable of producing generative inferences from relations and analogs. This model, BART-Gen, operates on explicit representations of relations learned by BART (Bayesian Analogy with Relational Transformations), to achieve two related forms of generative inference: reasoning from a single relation, and reasoning from an analog. In the first form, a reasoner completes a partially-specified instance of a stated relation (e.g., robin is a type of ____). In the second, a reasoner completes a target analog based on a stated source analog (e.g., sedan:car :: robin:____). We compare the performance of BART-Gen with that of BERT, a popular model for Natural Language Processing (NLP) that is trained on sentence completion tasks and that does not rely on explicit representations of relations. Across simulations and human experiments, we show that BART-Gen produces more human-like responses for generative inferences from relations and analogs than does the NLP model. These results demonstrate the essential role of explicit relation representations in human generative reasoning.

Generative Inferences in Relational and Analogical Reasoning: A Comparison of Computational Models

The capacity to generate recursive sequences is argued to be a behavioral marker of rich, algorithmic cognition, like that found perhaps distinctively in humans. However, the precise mechanisms underlying recursive sequence generation remain mysterious. We investigate three potential building blocks of recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We develop a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to a center-embedded sequence generation task. We further test whether recursive rule discovery depends upon relational information (either perceptual or conceptual) present in the task stimuli. The presence of relational information facilitates hierarchical reasoning that drives the generation of recursive sequences across various depths of center-embedding. Without relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important building block of recursive pattern processing and can be deployed across embedding depths and relational domains.

Building Blocks of Recursive Pattern Processing in Human Adults

Demonstratives---simple referential devices like this and that---are linguistic universals, but their meaning varies cross-linguistically. In languages like English and Italian, demonstratives are thought to encode the referent's distance from the producer (e.g., that one means ``the one far away from me"), while in others, like Portuguese and Spanish, they encode relative distance from both producer and receiver (e.g., aquel means ``the one far away from both of us"). Here we propose that demonstratives are also sensitive to the receiver's focus of attention, hence requiring a deeper form of social cognition than previously thought. We provide initial empirical and computational evidence for this idea, suggesting that producers use demonstratives to redirect the receiver's attention towards the intended referent, rather than only to indicate its physical distance.

Language universals rely on social cognition: Computational models of the use of this and that to redirect the receiver’s attention

“Each” and “every” can be used to express the same truth-conditions but differ in their contexts of use. We adopt a particular psycho-semantic proposal about the meanings of these universal quantifiers: “each” has a meaning that interfaces with the psychological system for representing object-files whereas “every” has a meaning that interfaces with the psychological system for representing ensembles. In five experiments (n=798 total) we demonstrate that this mentalistic account correctly predicts newly-observed constraints on how “each” and “every” are pragmatically used. More generally, these results demonstrate that canonical patterns of language use are affected in predictable ways by fine-grained differences in semantic representations and the cognitive systems to which those representations connect. By treating the output of semantics as mental representations that are more finely articulated than truth-conditions—and by taking seriously the relationship between linguistic meanings and non-linguistic cognitive systems—we can explain otherwise puzzling patterns of language use.

A Mentalistic Semantics Explains “Each” and “Every” Quantifier Use

How do humans produce and comprehend language in pragmatic ways? A variety of models of pragmatic inferences have been proposed, and these models are typically evaluated on their ability to account for human inferences in reference game experiments. However, these experiments are not tailored to target theoretical differences between models or clearly tease apart model predictions. We propose an optimal experiment design approach to systematically construct reference games that can optimally differentiate between models of human pragmatic reasoning. We demonstrate this approach and apply it to four models that have been debated in the literature: Grammar-based, Iterated Best Response (IBR), Rational Speech Act (RSA), and a recent variant of RSA grounded in rate-distortion theory (RD-RSA). Using these optimal reference game experiments, we find empirical evidence favoring iterated rationality models over the grammar-based model, as well as support for the relevance of rate-distortion theory to human pragmatic inferences. These results suggest that our optimal reference game design framework may help adjudicate between computational theories of pragmatic reasoning.

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From Vision to Reasoning: Probabilistic Analogical Mapping Between 3D Objects

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