Algorithms of Adaptation in Inductive Inference

Abstract

We investigate the idea that human concept inference utilizes local incremental search within a compositional mental theory space. To explore this, we study judgments in a challenging task, where participants actively gather evidence about a symbolic rule governing the behavior of a simulated environment. Participants construct mini-experiments before making generalizations and explicit guesses about the hidden rule. They then collect additional evidence themselves (Experiment 1) or observe evidence gathered by someone else (Experiment 2) before revising their own generalizations and guesses. In each case, we focus on the relationship between participants’ initial and revised guesses about the hidden rule concept. We find an order effect whereby revised guesses are anchored to idiosyncratic elements of the earlier guesses. To explain this pattern, we develop a family of process accounts that combine program induction ideas with local (MCMC-like) adaptation mechanisms. A particularly local variant of this adaptive account captures participants’ revisions better than a range of alternatives. We take this as suggestive that people deal with the inherent complexity of concept inference partly through use of local adaptive search in a latent compositional theory space.

The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.

“…The key idea of MCMC is that, in its simplest form, it represents only a single hypothesis at a time, and probabilistically transitions between hypotheses in proportion to their posterior probabilities. The local transitions induce a serial dependence between samples, akin to the local transitions in human hypothesis generation (Bramley et al, 2017; Fränken et al, 2021).…”

Group coordination catalyzes individual and cultural intelligence

“…For example, individuals are able to account for shared knowledge (Whalen, Griffiths, & Buchsbaum, 2018;Fränken, Theodoropoulos, & Bramley, 2021;Brennan, Galati, & Kuhlen, 2010), modulate generalization based on whether demonstrations were accidental or pedagogical (Gweon, Tenenbaum, & Schulz, 2010), and distinguish context-specific information from more generalizable information, effectively learning from people with different goals (Witt, Toyokawa, Lala, Gaissmaier, & Wu, 2023) and perhaps even glean useful information from failed or imperfect solutions. Here, we argue that ToM plays a key role in facilitating group complementarity.…”