A linear threshold model for optimal stopping behavior.

In many real-life decisions, options are distributed in space and time, making it necessary to search sequentially through them, often without a chance to return to a rejected option. The optimal strategy in these tasks is to choose the first option that is above a threshold that depends on the current position in the sequence. The implicit decision-making strategies by humans vary but largely diverge from this optimal strategy. The reasons for this divergence remain unknown. We present a model of human stopping decisions in sequential decision-making tasks based on a linear threshold heuristic. The first two studies demonstrate that the linear threshold model accounts better for sequential decision making than existing models. Moreover, we show that the model accurately predicts participants' search behavior in different environments. In the third study, we confirm that the model generalizes to a real-world problem, thus providing an important step toward understanding human sequential decision making.

Ambivalence in decision making: An eye tracking study.

An intuition of ambivalence in cognition is particularly strong for complex decisions, for which the merits and demerits of different options are roughly equal but hard to compare. We examined information search in an experimental paradigm which tasked participants with an ambivalent question, while monitoring attentional dynamics concerning the information relevant to each option in different Areas of Interest (AOIs). We developed two dynamical models for describing eye tracking curves, for each response separately. The models incorporated a drift mechanism towards the various options, as in standard drift diffusion theory. In addition, they included a mechanism for intrinsic oscillation, which competed with the drift process and undermined eventual stabilization of the dynamics. The two models varied in the range of drift processes postulated. Higher support was observed for the simpler model, which only included drifts from an uncertainty state to either of two certainty states. In addition, model parameters could be weakly related to the eventual decision, complementing our knowledge of the way eye tracking structure relates to decision (notably the gaze cascade effect).

No wrong decisions in an all-wrong situation. A qualitative study on the lived experiences of families of children with diffuse intrinsic pontine glioma.

BACKGROUND: Diffuse intrinsic pontine glioma (DIPG) is a rare, but lethal pediatric brain tumor with a median survival of less than 1 year. Existing treatment may prolong life and control symptoms, but may cause toxicity and side effects. In order to improve child- and family-centered care, we aimed to better understand the treatment decision-making experiences of parents, as studies on this topic are currently lacking. PROCEDURE: The data for this study came from 24 semistructured interviews with parents whose children were diagnosed with DIPG in two children's hospitals in Switzerland and died between 2000 and 2016. Analysis of the dataset was done using reflexive thematic analysis. RESULTS: For most parents, the decision for or against treatment was relatively straightforward given the fatality of the tumor and the absence of treatment protocols. Most of them had no regrets about their decision for or against treatment. The most distressing factor for them was observing their child's gradual loss of independence and informing them about the inescapability of death. To counter this powerlessness, many parents opted for complementary or alternative medicine in order to "do something." Many parents reported psychological problems in the aftermath of their child's death and coping strategies between mothers and fathers often differed. CONCLUSION: The challenges of DIPG are unique and explain why parental and shared decision-making is different in DIPG compared to other cancer diagnoses. Considering that treatment decisions shape parents' grief trajectory, clinicians should reassure parents by framing treatment decisions in terms of family's deeply held values and goals.

Selecting decision strategies: the differential role of affect.

Many theories on cognition assume that people adapt their decision strategies depending on the situation they face. To test if and how affect guides the selection of decision strategies, we conducted an online study (N = 166), where different mood states were induced through video clips. Results indicate that mood influenced the use of decision strategies. Negative mood, in particular anger, facilitated the use of non-compensatory strategies, whereas positive mood promoted compensatory decision rules. These results are in line with the idea that positive mood broadens the focus of attention and thus increases the use of compensatory decision strategies that take many pieces of information into account, whereas negative mood narrows the focus of attention and thus fosters non-compensatory strategies that rely on a selective use of information. The results further indicate that gaining a deeper theoretical understanding of the cognitive mechanisms that govern decision processes requires taking emotions into account.

Deliberation's blindsight: how cognitive load can improve judgments.

Multitasking poses a major challenge in modern work environments by putting the worker under cognitive load. Performance decrements often occur when people are under high cognitive load because they switch to less demanding--and often less accurate--cognitive strategies. Although cognitive load disturbs performance over a wide range of tasks, it may also carry benefits. In the experiments reported here, we showed that judgment performance can increase under cognitive load. Participants solved a multiple-cue judgment task in which high performance could be achieved by using a similarity-based judgment strategy but not by using a more demanding rule-based judgment strategy. Accordingly, cognitive load induced a shift to a similarity-based judgment strategy, which consequently led to more accurate judgments. By contrast, shifting to a similarity-based strategy harmed judgments in a task best solved by using a rule-based strategy. These results show how important it is to consider the cognitive strategies people rely on to understand how people perform in demanding work environments.

Never Gonna Give You Up Even When It Is Suboptimal.

Previous research showed that animals adopt different foraging strategies in different environment settings. However, research on whether humans adapt their foraging strategies to the foraging environment has shown little evidence of a change in strategies. This study aims to investigate whether humans will adapt their foraging strategies when performance differences between strategies are large and why participants may fixate on a single strategy. We conducted two foraging experiments and identified the strategies used by the participants. Most participants used the Give-Up Time (GUT) strategy regardless of the environment they encountered. GUT was used even in environments where other strategies such as the Fixed-Time strategy or the Fixed-Number strategy performed better. Using computer simulations, we further examined the conditions under which the GUT strategy will perform well compared to the other strategies. We found that even though the GUT strategy is not always the best strategy, it performs consistently on a satisfactory level and had an advantage when variance in the quality of patches was introduced. The consistently good performance of the GUT strategy could thus explain participants' lack of strategy switching.

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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Prior experience of variability influences generalisation of unspecified categories.

Category variability or diversity is an important factor influencing generalisation. However, expectations of category variability may not only depend on the variability of encountered category members, but may also be shaped by prior experiences with similar categories. In this study, we investigated whether we could influence category generalisation by inducing different category representations in an A/Non-A categorisation task: Participants either learned about a homogeneous category Non-A or a diverse category Non-A during a priming phase. To better understand the transfer process, we varied the nature of the learning phase from implicit transfer to explicit instructions that actively requested participants to use their prior experiences. We found that while with a homogeneous Non-A representation, generalisation of the A and Non-A categories was equal, the generalisation of category Non-A widened after a priming phase with a diverse representation. In a second experiment, we found that the widening of generalisation of category Non-A occurred when the exemplars in this category were themselves diverse (feature-diverse condition) but not when the category contained distinct exemplars (exemplar-diverse condition). These results suggests that categorisation is influenced by previous categorisation experiences possibly altering the representation of a category. Furthermore, the study gives a hint what kind of heterogeneity is needed to observe the commonly reported broader generalisation of diverse categories. The finding has implications not only to understand the influence of prior experiences on category learning, but any cognitive process that hinges on generalisation.

Testing three coping strategies for time pressure in categorizations and similarity judgments.

This article compares three psychological mechanisms to make multi-attribute inferences under time pressure in the domains of categorization and similarity judgments. Specifically, we test if people under time pressure attend to fewer object features (attention focus), if they respond less precisely (lower choice sensitivity), or if they simplify a psychological similarity function (simplified similarity). The simpler psychological similarity considers the number of matching features but ignores the actual feature value differences. We conducted three experiments (two of them preregistered) in which we manipulated time pressure: one was a categorization task, which was designed based on optimal experimental design principles, and the other two involved a similarity judgment task. Computational cognitive modeling following an exemplar-similarity framework showed that the behavior of most participants under time pressure is in line with a lower choice sensitivity, this means less precise response selection, especially when people make similarity judgments. We find that the variability of participants' behavior increases with time pressure, to a point where participants are unlikely to make inferences anymore but instead start choosing readily available response options repeatedly. These findings are consistent with related research in other cognitive domains, such as risky choices, and add to growing evidence that time pressure and other forms of cognitive load do not necessarily alter core cognitive processes themselves but rather affect the precision of response selection.

When the eyes have it and when not: How multiple sources of activation combine to guide eye movements during multiattribute decision making.

Memory plays a major but underexplored role in judgment and decision making (JDM). Studying eye movements-especially how people look at empty spatial locations when retrieving from memory information previously associated with those locations-provides useful information about how memory influences JDM. This so-called looking-at-nothing behavior is thought to reflect memory-driven allocation of attention. However, eye movements are also guided toward salient visual stimuli, such as test items presented on a screen. It is unclear how these multiple sources of activation combine to guide looking-at-nothing in JDM. We investigated this question in two experiments in which participants solved multiattribute categorization tasks using an exemplar-based decision strategy. In the first experiment, we tested how the occurrence and the strength of looking-at-nothing vary with the presentation format and the amount of training participants received. Looking-at-nothing occurred during categorizations when test-item information was presented auditorily and visually, but for the latter only after visual information was removed from the screen. It occurred both when training items were learned by heart and when they were presented 10 times on the screen. A second experiment revealed that an explicit instruction to imagine retrieval-relevant information during categorizations increased looking-at-nothing but did not change the decision-making process. The results shed light on the interaction between eye movements and attention to information in memory during JDM that can be explained in light of a shared priority map in memory. A detailed understanding of this interaction forms the basis for using eye movements to study memory processes in JDM. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A cognitive category-learning model of rule abstraction, attention learning, and contextual modulation.

We introduce the Category Abstraction Learning (CAL) model, a cognitive framework formally describing category learning built on similarity-based generalization, dissimilarity-based abstraction, two attention learning mechanisms, error-driven knowledge structuring, and stimulus memorization. Our hypotheses draw on an array of empirical and theoretical insights connecting reinforcement and category learning. The key novelty of the model is its explanation of how rules are learned from scratch based on three central assumptions. (a) Category rules emerge from two processes of stimulus generalization (similarity) and its direct inverse (category contrast) on independent dimensions. (b) Two attention mechanisms guide learning by focusing on rules, or on the contexts in which they produce errors. (c) Knowing about these contexts inhibits executing the rule, without correcting it, and consequently leads to applying partial rules in different situations. The model is designed to capture both systematic and individual differences in a broad range of learning paradigms. We illustrate the model's explanatory scope by simulating several benchmarks, including the classic Six Problems, the 5-4 problem, and linear separability. Beyond the common approach of predicting average response probabilities, we also propose explanations for more recently studied phenomena that challenge existing learning accounts, regarding task instructions, individual differences in rule extrapolation in three different tasks, individual attention shifts to stimulus features during learning, and other phenomena. We discuss CAL's relation to different models, and its potential to measure the cognitive processes regarding attention, abstraction, error detection, and memorization from multiple psychological perspectives. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

When easy comes hard: the development of adaptive strategy selection.

Can children learn to select the right strategy for a given problem? In one experiment, 9- to 10-year-olds (N = 50), 11- to 12-year-olds (N = 50), and adults (N = 50) made probabilistic inferences. Participants encountered environments favoring either an information-intensive strategy that integrates all available information or an information-frugal strategy that relies only on the most valid pieces of information. Nine- to 10-year-olds but not older children or adults had more difficulties learning to select an information-frugal strategy than an information-intensive strategy. This counterintuitive finding is explained by children's less developed ability to selectively attend to relevant information, an ability that seems to develop during late childhood. The results suggest that whether a strategy can be considered "easy" depends on the development of specific cognitive abilities.

Stress-related changes in financial risk taking: Considering joint effects of cortisol and affect.

Many decisions under risk and uncertainty are made under physical or emotional stress. A recent meta-analysis suggested that stress reliably influences risk taking but did not find a relation between single measures of stress such as cortisol and risk taking. One reason for the conflicting findings could be that the influence of stress on risk taking depends not only on physiological but also on psychological stress responses, in particular affective valence. We tested this hypothesis in an exploratory empirical study: Seventy participants worked on a financial risk-taking task. In half of the participants acute stress was induced with a cold pressor task. For all participants we measured cortisol and α-amylase levels, blood pressure, subjective arousal, and affective valence before and after the task. The stress induction increased participants' levels of cortisol, subjective arousal, and systolic blood pressure but did not directly influence negative affect or risky decision making. Examining the interplay between physiological and psychological stress responses, a moderation analysis revealed an interaction between stress induction and affect valence: Negative affect predicted an increase in risk-seeking decision making in the stress condition, but not in the control group. A similar moderation was found with cortisol reactivity, that is, negative affect predicted an increase in risk-seeking decision making in participants with high cortisol reactivity but not in participants with low cortisol reactivity. These results suggest that the effect of stress on risky decision making depends on the interplay of affective valence and cortisol reactivity.

The influence of reward magnitude on stimulus memory and stimulus generalization in categorization decisions.

Reward magnitude is a central concept in most theories of preferential decision making and learning. However, it is unknown whether variable rewards also influence cognitive processes when learning how to make accurate decisions (e.g., sorting healthy and unhealthy food differing in appeal). To test this, we conducted 3 studies. Participants learned to classify objects with 3 feature dimensions into two categories before solving a transfer task with novel objects. During learning, we rewarded all correct decisions, but specific category exemplars yielded a 10 times higher reward (high vs. low). Counterintuitively, categorization performance did not increase for high-reward stimuli, compared with an equal-reward baseline condition. Instead, performance decreased reliably for low-reward stimuli. To analyze the influence of reward magnitude on category generalization, we implemented an exemplar-categorization model and a cue-weighting model using a Bayesian modeling approach. We tested whether reward magnitude affects (a) the availability of exemplars in memory, (b) their psychological similarity to the stimulus, or (c) attention to stimulus features. In all studies, the evidence favored the hypothesis that reward magnitude affects the similarity gradients of high-reward exemplars compared with the equal-reward baseline. The results from additional reward-judgment tasks (Studies 2 and 3) strongly suggest that the cognitive processes of reward-value generalization parallel those of category generalization. Overall, the studies provide insights highlighting the need for integrating reward- and category-learning theories. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Competitive retrieval strategy causes multimodal response distributions in multiple-cue judgments.

Research on quantitative judgments from multiple cues suggests that judgments are simultaneously influenced by previously abstracted knowledge about cue-criterion relations and memories of past instances (or exemplars). Yet extant judgment theories leave 2 questions unanswered: (a) How are past exemplars and abstracted cue knowledge combined to form a judgment? (b) Are all past exemplars retrieved from memory to form the judgment (integrative retrieval) or is the judgment based on one exemplar (competitive retrieval)? To address these questions we propose and test a new model, CX-COM (combining Cue abstraction with eXemplar memory assuming COMpetitive memory retrieval). In a first step, CX-COM recalls only a single exemplar from memory. In a second step, the initially retrieved judgment is adjusted based on abstracted cue knowledge. Qualitatively, we show that CX-COM naturally captures judgment patterns that have been previously attributed to multiple strategies. Next, we tested CX-COM quantitatively in 2 experiments and found that it accounts well for people's judgment behavior. In the second experiment we additionally tested 2 qualitative predictions of CX-COM: The existence of multimodal response distributions within participants and systematic variability in judgments depending on the distance between similar exemplars in memory. The empirical results confirm CX-COM's assumptions. In sum, the evidence suggests that CX-COM is a viable new model for quantitative judgments and shows the importance of considering judgment variability in addition to average responses in judgment research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Models of quantitative estimations: rule-based and exemplar-based processes compared.

The cognitive processes underlying quantitative estimations vary. Past research has identified task-contingent changes between rule-based and exemplar-based processes (P. Juslin, L. Karlsson, & H. Olsson, 2008). B. von Helversen and J. Rieskamp (2008), however, proposed a simple rule-based model-the mapping model-that outperformed the exemplar model in a task thought to promote exemplar-based processing. This raised questions about the assumptions of rule-based versus exemplar-based models that underlie the notion of task contingency of cognitive processes. Rule-based models, such as the mapping model, assume the abstraction of explicit task knowledge. In contrast, exemplar models should profit if storage and activation of the exemplars is facilitated. Two studies tested the importance of the two models' assumptions. When knowledge about cues existed, the rule-based mapping model predicted quantitative estimations best. In contrast, when knowledge about the cues was difficult to gain, participants' estimations were best described by an exemplar model. The results emphasize the task contingency of cognitive processes.

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.

The mapping model: a cognitive theory of quantitative estimation.

How do people make quantitative estimations, such as estimating a car's selling price? Traditionally, linear-regression-type models have been used to answer this question. These models assume that people weight and integrate all information available to estimate a criterion. The authors propose an alternative cognitive theory for quantitative estimation. The mapping model, inspired by the work of N. R. Brown and R. S. Siegler (1993) on metrics and mappings, offers a heuristic approach to decision making. The authors test this model against established alternative models of estimation, namely, linear regression, an exemplar model, and a simple estimation heuristic. With 4 experimental studies the authors compare the models under different environmental conditions. The mapping model proves to be a valid model to predict people's estimates.

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.

Tracing the path of forgetting in rule abstraction and exemplar retrieval.

People often forget acquired knowledge over time such as names of former classmates. Which knowledge people can access, however, may modify the judgement process and affect judgement accuracy. Specifically, we hypothesised that judgements based on retrieving past exemplars from long-term memory may be more vulnerable to forgetting than remembering rules that relate the cues to the criterion. Experiment 1 systematically tracked the individual course of forgetting from initial learning to later tests (immediate, 1 day, and 1 week) in a linear judgement task facilitating rule-based strategies and a multiplicative judgement task facilitating exemplar-based strategies. Practising the acquired judgement strategy in repeated tests helped participants to consistently apply the learnt judgement strategy and retain a high judgement accuracy even after a week. Yet, whereas a long retention interval did not affect judgements in the linear task, a long retention interval impaired judgements in the multiplicative task. If practice was restricted as in Experiment 2, judgement accuracy suffered in both tasks. In addition, after a week without practice, participants tried to reconstruct their judgements by applying rules in the multiplicative task. These results emphasise that the extent to which decision makers can still retrieve previously learned knowledge limits their ability to make accurate judgements and that the preferred strategies change over time if the opportunity for practice is limited.