Naturalistic multiattribute choice

A query theory account of the attraction effect

We provide novel support for Query Theory, a reason-based decision framework, extending it to multialternative choices and applying it to the classic phenomenon known as the attraction effect. In Experiment 1 (N = 261), we generalised the two key metrics used in Query Theory from binary to multialternative choices and found that reasons supporting the target option were generated earlier and in greater quantity than those supporting the competitor, as predicted by the theory. In Experiment 2 (N = 703), we investigated the causal relationships between reasoning and choices by exogenously manipulating the order in which participants generated their reasons. As predicted, the size of the attraction effect was a function of this query order manipulation. We also introduced a bidirectional reason coding protocol to measure the valence of reasons, which confirmed support for Query Theory. We suggest the Query Theory framework can be useful for studying the high-level deliberation processes behind multialternative choices.

Value certainty and choice confidence are multidimensional constructs that guide decision-making

The degree of certainty that decision-makers have about their evaluations of available choice alternatives and their confidence about selecting the subjectively best alternative are important factors that affect current and future value-based choices. Assessments of the alternatives in a given choice set are rarely unidimensional; their values are usually derived from a combination of multiple distinct attributes. For example, the taste, texture, quantity, and nutritional content of a snack food may all be considered when determining whether to consume it. We examined how certainty about the levels of individual attributes of an option relates to certainty about the overall value of that option as a whole and/or to confidence in having chosen the subjectively best available option. We found that certainty and confidence are derived from unequally weighted combinations of attribute certainties rather than simple, equal combinations of all sources of uncertainty. Attributes that matter more in determining choice outcomes also are weighted more in metacognitive evaluations of certainty or confidence. Moreover, we found that the process of deciding between two alternatives leads to refinements in both attribute estimations and the degree of certainty in those estimates. Attributes that are more important in determining choice outcomes are refined more during the decision process in terms of both estimates and certainty. Although certainty and confidence are typically treated as unidimensional, our results indicate that they, like value estimates, are subjective, multidimensional constructs.

Successful everyday decision making: Combining attributes and associates

How do people make everyday decisions in order to achieve the most successful outcome? Decision making research typically evaluates choices according to their expected utility. However, this research largely focuses on abstract or hypothetical tasks and rarely investigates whether the outcome is successful and satisfying for the decision maker. Instead, we use an everyday decision making task in which participants describe a personally meaningful decision they are currently facing. We investigate the decision processes used to make this decision, and evaluate how successful and satisfying the outcome of the decision is for them. We examine how well analytic, attribute-based processes explain everyday decision making and predict decision outcomes, and we compare these processes to associative processes elicited through free association. We also examine the characteristics of decisions and individuals that are associated with good decision outcomes. Across three experiments we found that: 1) an analytic decision analysis of everyday decisions is not superior to simpler attribute-based processes in predicting decision outcomes; 2) contrary to research linking associative cognition to biases, free association generates valid cues that predict choice and decision outcomes as effectively as attribute-based approaches; 3) contrary to research favouring either attribute-based or associative processes, combining both attribute-based and associates best explains everyday decisions and most accurately predicts decision outcomes; and 4) individuals with a tendency to attempt analytic thinking do not make more successful everyday decisions. Instead, frequency, simplicity, and knowledge of the decision predict success. We propose that attribute-based and associative processes, in combination, both explain everyday decision making and predict successful decision outcomes.

Cognitive Modeling With Representations From Large-Scale Digital Data

Deep-learning methods can extract high-dimensional feature vectors for objects, concepts, images, and texts from large-scale digital data sets. These vectors are proxies for the mental representations that people use in everyday cognition and behavior. For this reason, they can serve as inputs into computational models of cognition, giving these models the ability to process and respond to naturalistic prompts. Over the past few years, researchers have applied this approach to topics such as similarity judgment, memory search, categorization, decision making, and conceptual knowledge. In this article, we summarize these applications, identify underlying trends, and outline directions for future research on the computational modeling of naturalistic cognition and behavior.

Distributional social semantics: Inferring word meanings from communication patterns

Distributional models of lexical semantics have proven to be powerful accounts of how word meanings are acquired from the natural language environment (Günther, Rinaldi, & Marelli, 2019; Kumar, 2020). Standard models of this type acquire the meaning of words through the learning of word co-occurrence statistics across large corpora. However, these models ignore social and communicative aspects of language processing, which is considered central to usage-based and adaptive theories of language (Tomasello, 2003; Beckner et al., 2009). Johns (2021) recently demonstrated that integrating social and communicative information into a lexical strength measure allowed for benchmark fits to be attained for lexical organization data, indicating that the social world contains important statistical information for language learning and processing. Through the analysis of the communication patterns of over 330,000 individuals on the online forum Reddit, totaling approximately 55 billion words of text, the findings of the current article demonstrates that social information about word usage allows for unique aspects of a word's meaning to be acquired, providing a new pathway for distributional model development.

Judgment errors in naturalistic numerical estimation

People estimate numerical quantities (such as the calories of foods) on a day-to-day basis. Although these estimates influence behavior and determine wellbeing, they are prone to two important types of errors. Scaling errors occur when people make mistakes reporting their beliefs about a particular numerical quantity (e.g. by inflating small numbers). Belief errors occur when people make mistakes using their knowledge of the judgment target to form their beliefs about the numerical quantity (e.g. by overweighting certain cues). In this paper, we quantitatively model numerical estimates, and in turn, scaling and belief errors, in everyday judgment tasks. Our approach is unique in using insights from semantic memory research to specify knowledge for naturalistic judgment targets, allowing our models to formally describe nuanced errors in belief not considered in prior research. In Studies 1 and 2, we find that belief error models predict participant estimates and errors with very high out-of-sample accuracy rates, significantly outperforming the predictions of scaling error models. In fact, the best-fitting belief error models can closely mimic the inverse-S shaped patterns captured by scaling error models, suggesting that the types of responses previously attributed to scaling errors can be seen as errors of belief. In Studies 3 to 8, we find that belief error models are also able to predict people's responses in semantic judgment, free association, and verbal protocol tasks related to numerical judgment, and thus provide a good account of the cognitive underpinnings of judgment.

Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings

Big data approaches to psychology have become increasing popular (Jones, 2017). Two of the main developments of this line of research is the advent of distributional models of semantics (e.g., Landauer and Dumais, 1997), which learn the meaning of words from large text corpora, and the collection of mega datasets of human behavior (e.g., The English lexicon project; Balota et al., 2007). The current article combines these two approaches, with the goal being to understand the consistency and preference that people have for word meanings. This was accomplished by mining a large amount of data from an online, crowdsourced dictionary and analyzing this data with a distributional model. Overall, it was found that even for words that are not an active part of the language environment, there is a large amount of consistency in the word meanings that different people have. Additionally, it was demonstrated that users of a language have strong preferences for word meanings, such that definitions to words that do not conform to people’s conceptions are rejected by a community of language users. The results of this article provides insights into the cultural evolution of word meanings, and sheds light on alternative methodologies that can be used to understand lexical behavior.