Memory shapes judgments: Tracing how memory biases judgments by inducing the retrieval of exemplars

When making judgments (e.g., about the quality of job candidates) decision makers should ignore salient, but unrepresentative information (e.g., the person’s name). However, research suggests that salient information influences judgments, possibly because memories of past encounters with similar information are integrated into the judgment. We studied eye movements to trace the link between the retrieval of past instances and their influence on judgments. Participants were more likely to look at screen locations where exemplars matching items on a name attribute had appeared, suggesting the retrieval of exemplars. Eye movements to exemplar locations predicted judgments, explaining why names influenced judgments. The results provide insights into how exemplars are integrated into the judgment process when assessing memory retrieval online

How social information affects information search and choice in probabilistic inferences

When making decisions, people are often exposed to relevant information stemming from qualitatively different sources. For instance, when making a choice between two alternatives people can rely on the advice of other people (i.e., social information) or search for factual information about the alternatives (i.e., non-social information). Prior research in categorization has shown that social information is given special attention when both social and non-social information is available, even when the social information has no additional informational value. The goal of the current work is to investigate whether framing information as social or non-social also influences information search and choice in probabilistic inferences. In a first study, we found that framing cues (i.e., the information used to make a decision) with medium validity as social increased the probability that they were searched for compared to a task where the same cues were framed as non-social information, but did not change the strategy people relied on. A second and a third study showed that framing a cue with high validity as social information facilitated learning to rely on a non-compensatory decision strategy. Overall, the results suggest that social in comparison to non-social information is given more attention and is learned faster than non-social information

Eye movements as a tool to investigate exemplar retrieval in judgments

The retrieval of past instances stored in memory can guide inferential choices and judgments. Yet, little process-level evidence exists that would allow a similar conclusion for preferential judgments. Recent research suggests that eye movements can trace information search in memory. During retrieval, people gaze at spatial locations associated with relevant information, even if the information is no longer present (the so-called ‘looking-at-nothing’ behavior). We examined eye movements based on the looking-at-nothing behavior to explore memory retrieval in inferential and preferential judgments. In Experiment 1, participants assessed their preference for smoothies with different ingredients, while the other half gauged another person’s preference. In Experiment 2, all participants made preferential judgments with or without instructions to respond as consistently as possible. People looked at exemplar locations in both inferential and preferential judgments, and both with and without consistency instructions. Eye movements to similar training exemplars predicted test judgments but not eye movements to dissimilar exemplars. These results suggest that people retrieve exemplar information in preferential judgments but that retrieval processes are not the sole determinant of judgments

Adaptive behavior in optimal sequential search

Sequential decision making-making a decision where available options are encountered successively-is a hallmark of everyday life. Such decisions require deciding to accept or reject an alternative without knowing potential future options. Prior work focused on understanding choice behavior by developing decision models that capture human choices in such tasks. We investigated people's adaptive behavior in changing environments in light of their cognitive strategies. We present two studies in which we modified (a) outcome variance and (b) the time horizon and provide empirical evidence that people adapt to both context manipulations. Furthermore, we apply a recently developed threshold model of optimal stopping to our data to disentangle different cognitive processes involved in optimal stopping behavior. The results from Study 1 show that participants adaptively scaled the values of the sampling distribution to its variance, suggesting that the value of an option is perceived in relative rather than absolute terms. The results from Study 2 suggest that increasing the time horizon decreases the initial acceptance level, but less strongly than would be optimal. Furthermore, for longer sequences, participants more weakly adjusted this acceptance threshold over time than for shorter sequences. Further correlations between individual estimates in each condition indicate that individual differences between the participants' thresholds remain fairly stable between the conditions, pointing toward an additive effect of our manipulations

Category learning in context:Modelling an assimilation process in self-regulated category learning

Category learning, a fundamental cognitive ability, is significantly influenced by variability. In this research, we propose a model describing how people adjust information search in self-regulated category learning to the level of category variability. Participants in the self-regulated category learning task sampled from two categories until they felt confident in categorizing novel objects. Our model assumes an influence of the variability of the focal and counter category on sampling by considering a within-category and between-category processes. In both processes, variability is quantified using an information-theoretic measure. Within this model, we test if a between-category process can be better conceptualized as either a contrasting or an assimilation process. The comparison of both processes support a between-category assimilation process, where the sample size adjusts to the counter category's variability. This novel focus sheds light on between-category dynamics, providing valuable insights into the mechanisms of category learning

Category learning in context:Modelling an assimilation process in self-regulated category learning

Category learning, a fundamental cognitive ability, is significantly influenced by variability. In this research, we propose a model describing how people adjust information search in self-regulated category learning to the level of category variability. Participants in the self-regulated category learning task sampled from two categories until they felt confident in categorizing novel objects. Our model assumes an influence of the variability of the focal and counter category on sampling by considering a within-category and between-category processes. In both processes, variability is quantified using an information-theoretic measure. Within this model, we test if a between-category process can be better conceptualized as either a contrasting or an assimilation process. The comparison of both processes support a between-category assimilation process, where the sample size adjusts to the counter category's variability. This novel focus sheds light on between-category dynamics, providing valuable insights into the mechanisms of category learning

Change and status quo in decisions with defaults: The effect of incidental emotions depends on the type of default

Affective states can change how people react to measures aimed at influencing their decisions such as providing a default option. Previous research has shown that when defaults maintain the status quo positive mood increases reliance on the default and negative mood decreases it. Similarly, it has been demonstrated that positive mood enhances the preference for inaction. We extend this research by investigating how mood states influence reliance on the default if the default leads to a change, thus pitting preference for status quo against a preference for inaction. Specifically, we tested in an online study how happiness and sadness influenced reliance on two types of default (1) a default maintaining status quo and (2) a default inducing change. Our results suggest that the effect of emotions depends on the type of default: people in a happy mood were more likely than sad people to follow a default when it maintained status quo but less likely to follow a default when it introduced change. These results are in line with mood maintenance theory

Testing Learning Mechanisms of Rule-Based Judgment

Weighing the importance of different pieces of information is a key determinant of making accurate judgments. In social judgment theory, these weighting processes have been successfully described with linear models. How people learn to make judgments has received less attention. Although the hitherto proposed delta learning rule can perfectly learn to solve linear problems, reanalyzing a previous experiment showed that it does not adequately describe human learning. To provide a more accurate description of learning processes we amended the delta learning rule with three learning mechanisms-a decay, an attentional learning mechanism, and a capacity limitation. An additional study tested the different learning mechanisms in predicting learning in linear judgment tasks. In this study, participants first learned to predict a continuous criterion based on four cues. To test the three learning mechanisms rigorously against each other, we changed the importance of the cues after 200 trials so that the mechanisms make different predictions with regard to how fast people adapt to the new environment. On average, judgment accuracy improved from Trial 1 to Trial 200, dropped when the task environment changed, but improved again until the end of the task. The capacity-restricted learning model, restricting how much people update the cue weights on a single trial, best described and predicted the learning curve of the majority of participants. Taken together, these results suggest that considering cognitive constraints within learning models may help to understand how humans learn when making inferences.</p

Testing learning mechanisms of rule-based judgment

Weighing the importance of different pieces of information is a key determinant of making accurate judgments. In social judgment theory, these weighting processes have been successfully described with linear models. How people learn to make judgments has received less attention. Although the hitherto proposed delta learning rule can perfectly learn to solve linear problems, reanalyzing a previous experiment showed that it does not adequately describe human learning. To provide a more accurate description of learning processes we amended the delta learning rule with three learning mechanisms—a decay, an attentional learning mechanism, and a capacity limitation. An additional study tested the different learning mechanisms in predicting learning in linear judgment tasks. In this study, participants first learned to predict a continuous criterion based on four cues. To test the three learning mechanisms rigorously against each other, we changed the importance of the cues after 200 trials so that the mechanisms make different predictions with regard to how fast people adapt to the new environment. On average, judgment accuracy improved from Trial 1 to Trial 200, dropped when the task environment changed, but improved again until the end of the task. The capacity-restricted learning model, restricting how much people update the cue weights on a single trial, best described and predicted the learning curve of the majority of participants. Taken together, these results suggest that considering cognitive constraints within learning models may help to understand how humans learn when making inferences

Ecological Rationality: A Framework for Understanding and Aiding the Aging Decision Maker

The notion of ecological rationality sees human rationality as the result of the adaptive fit between the human mind and the environment. Ecological rationality focuses the study of decision making on two key questions: First, what are the environmental regularities to which people’s decision strategies are matched, and how frequently do these regularities occur in natural environments? Second, how well can people adapt their use of specific strategies to particular environmental regularities? Research on aging suggests a number of changes in cognitive function, for instance, deficits in learning and memory that may impact decision-making skills. However, it has been shown that simple strategies can work well in many natural environments, which suggests that age-related deficits in strategy use may not necessarily translate into reduced decision quality. Consequently, we argue that predictions about the impact of aging on decision performance depend not only on how aging affects decision-relevant capacities but also on the decision environment in which decisions are made. In sum, we propose that the concept of the ecological rationality is crucial to understanding and aiding the aging decision maker