Machine learning-based measure of cognitive complexity explains variance in rank-ordered preference - Conference paper

Aug 5, 2026

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Cognitive complexity sheds light on how decisions are formed, from small, everyday selections to consequential judgments. The study introduces an approach for inferring the complexity of processes underlying preference and choice. It operationalizes complexity by quantifying how participant-generated descriptive words for consumer products differ from a natural-language model, with greater divergence indicating higher complexity. Preliminary results connect cognitive complexity to preference rankings, capturing distinct variance and clarifying how preference becomes observable through choice, while also valuing participant-generated features for modeling choice.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Preference, valuation, and decision making

Feature space and mental representations

Aim of the present study and methodology

UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title Machine learning-based measure of cognitive complexity explains variance in rank-ordered preference Permalink [https://escholarship.org/uc/item/79g3t154](https://escholarship.org/uc/item/79g3t154) Journal Proceedings of the Annual Meeting of the Cognitive Science Society, 45(45) Authors Hakimi, Shabnam Chen, Yan-Ying Van, Monica Pet al. Publication Date 2023 Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California Machine learning-based measure of cognitive complexity explains variance in rank-ordered preferences Shabnam Hakimi, Yan-Ying Chen, Monica Van, Scott Carter, Emily Sumner, Nayeli Bravo, Kalani Murakami, Yanxia Zhang, Charlene Wu, & Matthew Klenk {shabnam.hakimi, yan-ying.chen, monica.van.ctr, scott.carter, emily.sumner, nayeli.bravo.ctr, kalani.murakami.ctr, yanxia.zhang, charlene.wu, & [matt.klenk](matt.klenk}@tri.global)[}](matt.klenk}@tri.global)[@tri.global](matt.klenk}@tri.global) Toyota Research Institute Los Altos, CA 94022 USA Abstract Cognitive complexity can provide insight into how people make decisions, ranging from the most minor to the most impactful. Here, we present a novel approach to inferring the complexity of processes associated with preference and decision making. We measured the complexity of participant-generated descriptive features of consumer products and the relationship to preference rankings. In order to measure cognitive complexity over a sparse set of features, we developed a natural language processing approach that compared the descriptive words generated by participants to those generated by a machine learning model; words that were more distinct from those generated by the model were rated more complex. We show preliminary evidence that cognitive complexity is related to preference for products, explaining unique variance in rankings and also capturing anew facet of the process through which preference is revealed through choice. We also show the value of participantgenerated features for understanding choice processes. Keywords: cognitive complexity; decision making; preference; mental models; natural language processing Introduction Water or cola? Cola or water? Even simple choices made while standing in a grocery store aisle are deceptively simple. The moment the bottle of water and not the cola ends up in your hand is realized through a process that involves evaluating the space of possible actions, assigning value to them, choosing an action, and evaluating its outcome for use in future decisions (Rangel, Camerer, & Montague, 2008) . Here, you must represent the value of choosing the water bottle orthe cola bottle, considering the attributes of each beverage. Preferences over these features are a critical part of the valuation process. The mechanism through which preference emerges and influences valuation is much debated (e.g., constructed v. discovered preference; (Slovic, 1995) v. (Plott, 1996)), reflecting epistemic differences across and within disciplines as broad as economics, psychology, and philosophy. The way preference is defined has meaningful implications, affecting not only how we understand an individual’s choice of cola over water, but also in the modeling and prediction of group decisions. On top of this, people’s stated preferences are not always indicative of their true preferences or desires. People are prone to framing effects (Tversky & Kahneman, 1985; Chang, 2008), serial position effects (Murdock Jr, 1962; Deese & Kaufman, 1957; Bar-Hillel, Peer, & Acquisti, and peer pressure. Such phenomena can make it difficult to infer what is important to people using standard preference elicitation methods. Here, we take a parsimonious view of preference, defining it as motive bias, or more concretely, bias over a space of stimulus features that has the potential for a

FAQs

How does the paper measure cognitive complexity in preference-related decisions?

It compares participant-generated descriptive features with words produced by a natural-language processing model, rating as more complex those that are more distinct from the model’s outputs.

Why are rank-ordered preference tasks central to the study?

The work decomposes rank-ordered preference into parts to capture idiosyncrasies in mental representations and to move toward more ecologically valid choice scenarios.

What evidence is reported about the relationship between cognitive complexity and preferences?

The study presents preliminary evidence that cognitive complexity relates to preference for products by explaining unique variance in rankings and revealing an additional facet of how preference is expressed through choice.

Aug 5, 2026

Upload by Aurora