Predicting implicit attitudes with natural language data.

Large-scale language datasets and advances in natural language processing offer opportunities for studying people's cognitions and behaviors. We show how representations derived from language can be combined with laboratory-based word norms to predict implicit attitudes for diverse concepts. Our approach achieves substantially higher correlations than existing methods. We also show that our approach is more predictive of implicit attitudes than are explicit attitudes, and that it captures variance in implicit attitudes that is largely unexplained by explicit attitudes. Overall, our results shed light on how implicit attitudes can be measured by combining standard psychological data with large-scale language data. In doing so, we pave the way for highly accurate computational modeling of what people think and feel about the world around them.

Computational mechanisms for context-based behavioral interventions: A large-scale analysis.

Choice context influences decision processes and is one of the primary determinants of what people choose. This insight has been used by academics and practitioners to study decision biases and to design behavioral interventions to influence and improve choices. We analyzed the effects of context-based behavioral interventions on the computational mechanisms underlying decision-making. We collected data from two large laboratory studies involving 19 prominent behavioral interventions, and we modeled the influence of each intervention using a leading computational model of choice in psychology and neuroscience. This allowed us to parametrize the biases induced by each intervention, to interpret these biases in terms of underlying decision mechanisms and their properties, to quantify similarities between interventions, and to predict how different interventions alter key choice outcomes. In doing so, we offer researchers and practitioners a theoretically principled approach to understanding and manipulating choice context in decision-making.

Complex economic decisions from simple neurocognitive processes: the role of interactive attention.

Neurocognitive theories of value-based choice propose that people additively accumulate choice attributes when making decisions. These theories cannot explain the emergence of complex multiplicative preferences such as those assumed by prospect theory and other economic models. We investigate an interactive attention mechanism, according to which attention to attributes (like payoffs) depends on other attributes (like probabilities) attended to previously. We formalize this mechanism using a Markov attention model combined with an accumulator decision process, and test our model on eye-tracking and mouse-tracking data in risky choice. Our tests show that interactive attention is necessary to make good choices, that most participants display interactive attention and that allowing for interactive attention in accumulation-based decision models improves their predictions. By equipping established decision models with sophisticated attentional dynamics, we extend these models to describe complex economic choice, and in the process, we unify two prominent theoretical approaches to studying value-based decision making.

Information acquisition and decision strategies in intertemporal choice.

Intertemporal decision models describe choices between outcomes with different delays. While these models mainly focus on predicting choices, they make implicit assumptions about how people acquire and process information. A link between information processing and choice model predictions is necessary for a complete mechanistic account of decision making. We establish this link by fitting 18 intertemporal choice models to experimental datasets with both choice and information acquisition data. First, we show that choice models have highly correlated fits: people that behave according to one model also behave according to other models that make similar information processing assumptions. Second, we develop and fit an attention model to information acquisition data. Critically, the attention model parameters predict which type of intertemporal choice models best describes a participant's choices. Overall, our results relate attentional processes to models of intertemporal choice, providing a stepping stone towards a complete mechanistic account of intertemporal decision making.

Computational Methods for Predicting and Understanding Food Judgment.

People make subjective judgments about the healthiness of different foods every day, and these judgments in turn influence their food choices and health outcomes. Despite the importance of such judgments, there are few quantitative theories about their psychological underpinnings. This article introduces a novel computational approach that can approximate people's knowledge representations for thousands of common foods. We used these representations to predict how both lay decision-makers (the general population) and experts judge the healthiness of individual foods. We also applied our method to predict the impact of behavioral interventions, such as the provision of front-of-pack nutrient and calorie information. Across multiple studies with data from 846 adults, our models achieved very high accuracy rates (r2 = .65-.77) and significantly outperformed competing models based on factual nutritional content. These results illustrate how new computational methods applied to established psychological theory can be used to better predict, understand, and influence health behavior.

The More Who Die, the Less We Care: Evidence from Natural Language Analysis of Online News Articles and Social Media Posts.

Considerable amount of laboratory and survey-based research finds that people show disproportional compassionate and affective response to the scope of human mortality risk. According to research on "psychic numbing," it is often the case that the more who die, the less we care. In the present article, we examine the extent of this phenomenon in verbal behavior, using large corpora of natural language to quantify the affective reactions to loss of life. We analyze valence, arousal, and specific emotional content of over 100,000 mentions of death in news articles and social media posts, and find that language shows an increase in valence (i.e., decreased negative affect) and a decrease in arousal when describing mortality of larger numbers of people. These patterns are most clearly reflected in specific emotions of joy and (in a reverse fashion) of fear and anger. Our results showcase a novel methodology for studying affective decision making, and highlight the robustness and real-world relevance of psychic numbing. They also offer new insights regarding the psychological underpinnings of psychic numbing, as well as possible interventions for reducing psychic numbing and overcoming social and psychological barriers to action in the face of the world's most serious threats.

Psychological mechanisms of loss aversion: A drift-diffusion decomposition.

Decision makers often reject mixed gambles offering equal probabilities of a larger gain and a smaller loss. This important phenomenon, referred to as loss aversion, is typically explained by prospect theory, which proposes that decision makers give losses higher utility weights than gains. In this paper we consider alternative psychological mechanisms capable of explaining loss aversion, such as a fixed utility bias favoring rejection, as well as a bias favoring rejection prior to gamble valuation. We use a drift diffusion model of decision making to conceptually distinguish, formally define, and empirically measure these mechanisms. In two preregistered experiments, we show that the pre-valuation bias provides a very large contribution to model fits, predicts key response time patterns, reflects prior expectations regarding gamble desirability, and can be manipulated independently of the valuation process. Our results indicate that loss aversion is the result of multiple different psychological mechanisms, and that the pre-valuation bias is a fundamental determinant of this well-known behavioral tendency. These results have important implications for how we model behavior in risky choice tasks, and how we interpret its relationship with various psychological, clinical, and neurobiological variables.

The spatial arrangement method of measuring similarity can capture high-dimensional semantic structures.

Psychologists collect similarity data to study a variety of phenomena including categorization, generalization and discrimination, and representation itself. However, collecting similarity judgments between all pairs of items in a set is expensive, spurring development of techniques like the Spatial Arrangement Method (SpAM; Goldstone, Behavior Research Methods, Instruments, & Computers, 26, 381-386, 1994), wherein participants place items on a two-dimensional plane such that proximity reflects perceived similarity. While SpAM greatly hastens similarity measurement, and has been successfully used for lower-dimensional, perceptual stimuli, its suitability for higher-dimensional, conceptual stimuli is less understood. In study 1, we evaluated the ability of SpAM to capture the semantic structure of eight different categories composed of 20-30 words each. First, SpAM distances correlated strongly (r = .71) with pairwise similarity judgments, although below SpAM and pairwise judgment split-half reliabilities (r's > .9). Second, a cross-validation exercise with multidimensional scaling fits at increasing latent dimensionalities suggested that aggregated SpAM data favored higher (> 2) dimensional solutions for seven of the eight categories explored here. Third, split-half reliability of SpAM dissimilarities was high (Pearson r = .90), while the average correlation between pairs of participants was low (r = .15), suggesting that when different participants focus on different pairs of stimulus dimensions, reliable high-dimensional aggregate similarity data is recoverable. In study 2, we show that SpAM can recover the Big Five factor space of personality trait adjectives, and that cross-validation favors a four- or five-dimension solution on this dataset. We conclude that SpAM is an accurate and reliable method of measuring similarity for high-dimensional items like words. We publicly release our data for researchers.

Preference accumulation as a process model of desirability ratings.

In desirability rating tasks, decision makers evaluate objects on a continuous response scale. Despite their prominence, full process models of these rating tasks have not been developed. We investigated whether a preference accumulation process, a process often used to model discrete choice, might explain ratings as well. According to our model, attributes from each option are sampled and evaluated stochastically. The evaluations are integrated over time, forming a preference. Preferences for options compete with each other, and accumulated preferences can decay. The model makes precise predictions regarding the statistical distribution of desirability ratings, as well as their dependence on deliberation time and on context. We test and confirm these predictions in two experimental studies. Additionally, quantitative model fits indicate that participants are better described by our proposed model, relative to a model without dynamism, competition, or stochastic attribute sampling. Our results show that the descriptive power of models of preference accumulation extends beyond discrete choice, and that the assumptions of this framework accurately characterize the core cognitive processes at play in the construction of preference and the evaluation of objects.

Variable time preference.

We re-examine behavioral patterns of intertemporal choice with recognition that time preferences may be inherently variable, focusing in particular on the explanatory power of an exponential discounting model with variable discount factors - the variable exponential model. We provide analytical results showing that this model can generate systematically different choice patterns from an exponential discounting model with a fixed discount factor. The variable exponential model accounts for the common behavioral pattern of decreasing impatience, which is typically attributed to hyperbolic discounting. The variable exponential model also generates violations of strong stochastic transitivity in choices involving intertemporal dominance. We present the results of two experiments designed to evaluate the variable exponential model in terms of quantitative fit to individual-level choice data. Data from these experiments reveal that allowing for a variable discount factor significantly improves the fit of the exponential model, and that a variable exponential model provides a better account of individual-level choice probabilities than hyperbolic discounting models. In a third experiment we find evidence of strong stochastic transitivity violations when intertemporal dominance is involved, in accordance with the variable exponential model. Overall, our analytical and experimental results indicate that exponential discounting can explain intertemporal choice behavior that was supposed to be beyond its descriptive scope if the discount factor is permitted to vary at random. Our results also highlight the importance of allowing for different sources of randomness in choice modeling.

Association and response accuracy in the wild.

We studied contestant accuracy and error in a popular television quiz show, "Jeopardy!" Using vector-based knowledge representations obtained from distributional models of semantic memory, we computed the strength of association between clues and responses in over 5,000 televised games. Such representations have been shown to play a key role in memory and judgment, and consistent with this work, we find that contestants are more likely to provide correct responses when these responses are strongly associated with their clues, and more likely to provide incorrect responses when correct responses are weakly or negatively associated with their clues. This effect is stronger for easier questions with low monetary values, and for questions in which contestants compete to respond quickly. Our results show how distributional models of semantic memory can be used to predict human behavior in naturalistic high-level judgment tasks with skilled participants and significant monetary and social incentives.

Text-mining as a methodology to assess eating disorder-relevant factors: Comparing mentions of fitness tracking technology across online communities.

OBJECTIVE: Text-mining offers a technique to identify and extract information from a large corpus of textual data. As an example, this study presents the application of text-mining to assess and compare interest in fitness tracking technology across eating disorder and health-related online communities. METHOD: A list of fitness tracking technology terms was developed, and communities (i.e., 'subreddits') on a large online discussion platform (Reddit) were compared regarding the frequency with which these terms occurred. The corpus used in this study comprised all comments posted between May 2015 and January 2018 (inclusive) on six subreddits-three eating disorder-related, and three relating to either fitness, weight-management, or nutrition. All comments relating to the same 'thread' (i.e., conversation) were concatenated, and formed the cases used in this study (N = 377,276). RESULTS: Within the eating disorder-related subreddits, the findings indicated that a 'pro-eating disorder' subreddit, which is less recovery focused than the other eating disorder subreddits, had the highest frequency of fitness tracker terms. Across all subreddits, the weight-management subreddit had the highest frequency of the fitness tracker terms' occurrence, and MyFitnessPal was the most frequently mentioned fitness tracker. DISCUSSION: The technique exemplified here can potentially be used to assess group differences to identify at-risk populations, generate and explore clinically relevant research questions in populations who are difficult to recruit, and scope an area for which there is little extant literature. The technique also facilitates methodological triangulation of research findings obtained through more 'traditional' techniques, such as surveys or interviews.

The dynamics of deferred decision.

Decision makers are often unable to choose between the options that they are offered. In these settings they typically defer their decision, that is, delay the decision to a later point in time or avoid the decision altogether. In this paper, we outline eight behavioral findings regarding the causes and consequences of choice deferral that cognitive theories of decision making should be able to capture. We show that these findings can be accounted for by a deferral-based time limit applied to existing sequential sampling models of preferential choice. Our approach to modeling deferral as a time limit in a sequential sampling model also makes a number of novel predictions regarding the interactions between choice probabilities, deferral probabilities, and decision times, and we confirm these predictions in an experiment. Choice deferral is a key feature of everyday decision making, and our paper illustrates how established theoretical approaches can be used to understand the cognitive underpinnings of this important behavioral phenomenon.

Exploring variability in risk taking with large language models.

What are the sources of individual-level differences in risk taking, and how do they depend on the domain or situation in which the decision is being made? Psychologists currently answer such questions with psychometric methods, which analyze correlations across participant responses in survey data sets. In this article, we analyze the preferences that give rise to these correlations. Our approach uses (a) large language models (LLMs) to quantify everyday risky behaviors in terms of the attributes or reasons that may describe those behaviors, and (b) decision models to map these attributes and reasons onto participant responses. We show that LLM-based decision models can explain observed correlations between behaviors in terms of the reasons different behaviors elicit and explain observed correlations between individuals in terms of the weights different individuals place on reasons, thereby providing a decision theoretic foundation for psychometric findings. Since LLMs can generate quantitative representations for nearly any naturalistic decision, they can be used to make accurate out-of-sample predictions for hundreds of everyday behaviors, predict the reasons why people may or may not want to engage in these behaviors, and interpret these reasons in terms of core psychological constructs. Our approach has important theoretical and practical implications for the study of heterogeneity in everyday behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Limitations to optimal search in naturalistic active learning.

Optimality in active learning is under intense debate in numerous disciplines. We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Participants in our task can ask questions and learn about hundreds of everyday items but must retrieve queried items from memory. To maximize information gain, participants need to retrieve sequences of dissimilar items. In eight experiments (N = 795), we find that participants are unable to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality in question asking) is unaffected by task instructions and incentives, though participants can identify efficient query sequences when given a choice between query sequences. Overall, our results indicate that participants can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes may place critical restrictions on people's ability to ask good questions in naturalistic active learning tasks. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Memory modeling of counterfactual generation.

We use a computational model of memory search to study how people generate counterfactual outcomes in response to an established target outcome. Hierarchical Bayesian model fitting to data from six experiments reveals that counterfactual outcomes that are perceived as more desirable and more likely to occur are also more likely to come to mind and are generated earlier than other outcomes. Additionally, core memory mechanisms such as semantic clustering and word frequency biases have a strong influence on retrieval dynamics in counterfactual thinking. Finally, we find that the set of counterfactuals that come to mind can be manipulated by modifying the total number of counterfactuals that participants are prompted to generate, and our model can predict these effects. Overall, our findings demonstrate how computational memory search models can be integrated with current theories of counterfactual thinking to provide novel insights into the process of generating counterfactual thoughts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A deep learning approach to personality assessment: Generalizing across items and expanding the reach of survey-based research.

Traditional methods of personality assessment, and survey-based research in general, cannot make inferences about new items that have not been surveyed previously. This limits the amount of information that can be obtained from a given survey. In this article, we tackle this problem by leveraging recent advances in statistical natural language processing. Specifically, we extract "embedding" representations of questionnaire items from deep neural networks, trained on large-scale English language data. These embeddings allow us to construct a high-dimensional space of items, in which linguistically similar items are located near each other. We combine item embeddings with machine learning algorithms to extrapolate participant ratings of personality items to completely new items that have not been rated by any participants. The accuracy of our approach is on par with incentivized human judges given an identical task, indicating that it predicts ratings of new personality items as accurately as people do. Our approach is also capable of identifying psychological constructs associated with questionnaire items and can accurately cluster items into their constructs based only on their language content. Overall, our results show how representations of linguistic personality descriptors obtained from deep language models can be used to model and predict a large variety of traits, scales, and constructs. In doing so, they showcase a new scalable and cost-effective method for psychological measurement. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Inductive reasoning in minds and machines.

Induction-the ability to generalize from existing knowledge-is the cornerstone of intelligence. Cognitive models of human induction are largely limited to toy problems and cannot make quantitative predictions for the thousands of different induction arguments that have been studied by researchers, or to the countless induction arguments that could be encountered in everyday life. Leading large language models (LLMs) go beyond toy problems but fail to mimic observed patterns of human induction. In this article, we combine rich knowledge representations obtained from LLMs with theories of human inductive reasoning developed by cognitive psychologists. We show that this integrative approach can capture several benchmark empirical findings on human induction and generate human-like responses to natural language arguments with thousands of common categories and properties. These findings shed light on the cognitive mechanisms at play in human induction and show how existing theories in psychology and cognitive science can be integrated with new methods in artificial intelligence, to successfully model high-level human cognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Free association in a neural network.

Free association among words is a fundamental and ubiquitous memory task. Although distributed semantics (DS) models can predict the association between pairs of words, and semantic network (SN) models can describe transition probabilities in free association data, there have been few attempts to apply established cognitive process models of memory search to free association data. Thus, researchers are currently unable to explain the dynamics of free association using memory mechanisms known to be at play in other retrieval tasks, such as free recall from lists. We address this issue using a popular neural network model of free recall, the context maintenance and retrieval (CMR) model, which we fit using stochastic gradient descent on a large data set of free association norms. Special cases of CMR mimic existing DS and SN models of free association, and we find that CMR outperforms these models on out-of-sample free association data. We also show that training CMR on free association data generates improved predictions for free recall from lists, demonstrating the value of free association for the study of many different types of memory phenomena. Overall, our analysis provides a new account of the dynamics of free association, predicts free association with increased accuracy, integrates theories of free association with established models of memory, and shows how large data sets and neural network training methods can be used to model complex cognitive processes that operate over thousands of representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Semantic determinants of memorability.

We examine why some words are more memorable than others by using predictive machine learning models applied to word recognition and recall datasets. Our approach provides more accurate out-of-sample predictions for recognition and recall than previous psychological models, and outperforms human participants in new studies of memorability prediction. Our approach's predictive power stems from its ability to capture the semantic determinants of memorability in a data-driven manner. We identify which semantic categories are important for memorability and show that, unlike features such as word frequency that influence recognition and recall differently, the memorability of semantic categories is consistent across recognition and recall. Our paper sheds light on the complex psychological drivers of memorability, and in doing so illustrates the power of machine learning methods for psychological theory development.