Getting aligned on representational alignment

Biological and artificial information processing systems form representations that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the extent to which the representations formed by these diverse systems agree? Do similarities in representations then translate into similar behavior? How can a system's representations be modified to better match those of another system? These questions pertaining to the study of representational alignment are at the heart of some of the most active research areas in cognitive science, neuroscience, and machine learning. For example, cognitive scientists measure the representational alignment of multiple individuals to identify shared cognitive priors, neuroscientists align fMRI responses from multiple individuals into a shared representational space for group-level analyses, and ML researchers distill knowledge from teacher models into student models by increasing their alignment. Unfortunately, there is limited knowledge transfer between research communities interested in representational alignment, so progress in one field often ends up being rediscovered independently in another. Thus, greater cross-field communication would be advantageous. To improve communication between these fields, we propose a unifying framework that can serve as a common language between researchers studying representational alignment. We survey the literature from all three fields and demonstrate how prior work fits into this framework. Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields. We hope that our work can catalyze cross-disciplinary collaboration and accelerate progress for all communities studying and developing information processing systems. We note that this is a working paper and encourage readers to reach out with their suggestions for future revisions.Comment: Working paper, changes to be made in upcoming revision

Deciding to be Authentic: Intuition is Favored Over Deliberation for Self-Reflective Decisions

People think they ought to make some decisions on the basis of deliberative analysis, and others on the basis of intuitive, gut feelings. What accounts for this variation in people’s preferences for intuition versus deliberation? We propose that intuition might be prescribed for some decisions because people’s folk theory of decision-making accords a special role to authenticity, where authenticity is uniquely associated with intuitive choice. Two pre-registered experiments find evidence in favor of this claim. In Experiment 1 (N=631), we find that decisions made on the basis of intuition (vs. deliberation) are more likely to be judged authentic, especially in domains where authenticity is plausibly valued. In Experiment 2 (N=177), we find that people are more likely to prescribe intuition as a basis for choice when the value of authenticity is heightened experimentally. These effects hold beyond previously recognized influences, such as computational costs, presumed efficacy, objectivity, complexity, and expertise

Mechanisms of Belief Persistence in the Face of Societal Disagreement

People have a remarkable ability to remain steadfast in their beliefs in the face of large-scale disagreement. This has important consequences (e.g., societal polarization), yet its psychological underpinnings are poorly understood. In this paper, we answer foundational questions regarding belief persistence, from its prevalence to variability. Across two Experiments (N = 356, N = 354), we find that participants are aware of societal disagreement about controversial issues, yet overwhelmingly (~85%) do not question their views if asked to reflect on this disagreement. Both studies provide evidence that explanations for persistence vary across domains, with epistemic and meta-epistemic explanations among the most prevalent

Can Humans Do Less-Than-One-Shot Learning?

Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly how small? In this paper, we introduce a novel experimental paradigm that allows us to examine classification in an extremely data-scarce setting, asking whether humans can learn more categories than they have exemplars (i.e., can humans do "less-than-one shot'' learning?). An experiment conducted using this paradigm reveals that people are capable of learning in such settings, and provides several insights into underlying mechanisms. First, people can accurately infer and represent high-dimensional feature spaces from very little data. Second, having inferred the relevant spaces, people use a form of prototype-based categorization (as opposed to exemplar-based) to make categorical inferences. Finally, systematic, machine-learnable patterns in responses indicate that people may have efficient inductive biases for dealing with this class of data-scarce problems