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.

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.

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.

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).

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.

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).

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).

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.

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).

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.

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.

Age-related within-task adaptations in sequential decision making: Considering cognitive and motivational factors.

Many decisions require sequentially searching through the available alternatives. In these tasks, older adults have been shown to perform worse than younger adults, but the reasons why age differences occur are still unclear. In the present research, we tackle this question by investigating which strategies older and younger adults adopt and how these strategies relate to individual differences in cognitive (mental speed, working memory capacity) and motivational (need for cognitive closure) variables. To achieve this goal, we conducted two studies in which older and younger adults performed a computerized sequential choice task. Study 1 indicated that older adults changed their decision-making strategies throughout the task by reducing the number of options they considered. This change in strategy did not decrease performance because searching less allowed older adults to choose more promising options. In the second study we manipulated whether a long or short search was optimal. In the beginning older adults performed worse than younger adults independent of whether short or long search was adaptive. However, in the second half of the task we found age differences in performance when long search was required, but not when short search was required. In both studies whether or not older adults changed their strategy depended on their need for cognitive closure, suggesting that avoiding cognitive closure facilitates adaptive flexibility. Together, the two studies provide evidence for compensatory strategy adaptations among older adults completing sequential choice tasks. (PsycINFO Database Record

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.

How social and non-social information influence classification decisions: A computational modelling approach.

Social information such as observing others can improve performance in decision making. In particular, social information has been shown to be useful when finding the best solution on one's own is difficult, costly, or dangerous. However, past research suggests that when making decisions people do not always consider other people's behaviour when it is at odds with their own experiences. Furthermore, the cognitive processes guiding the integration of social information with individual experiences are still under debate. Here, we conducted two experiments to test whether information about other persons' behaviour influenced people's decisions in a classification task. Furthermore, we examined how social information is integrated with individual learning experiences by testing different computational models. Our results show that social information had a small but reliable influence on people's classifications. The best computational model suggests that in categorization people first make up their own mind based on the non-social information, which is then updated by the social information.

Similar task features shape judgment and categorization processes.

The distinction between similarity-based and rule-based strategies has instigated a large body of research in categorization and judgment. Within both domains, the task characteristics guiding strategy shifts are increasingly well documented. Across domains, past research has observed shifts from rule-based strategies in judgment to similarity-based strategies in categorization, but limited these comparisons to 1 prototypical environment, a linear task structure, and a restricted set of strategies. To systematically compare the 2 domains, we considered several instantiations of rule-based and similarity-based strategies and examined strategy choice across different types of judgment and categorization tasks. Between participants, we varied task characteristics from a 1-dimensional linear to a multidimensional linear and to 2 multidimensional nonlinear tasks. Irrespective of domain, strategies considered, or model comparison technique used, we find that more participants relied on similarity-based strategies when the functional relationship between the cues and the criterion was nonlinear. Shifts from rule-based strategies in judgment to similarity-based strategies in categorization, however, were rare and most pronounced in 1-dimensional environments. These results support the hypothesis that the cognitive strategies people select to solve a judgment or categorization task depend less on the domain but more on the complexity of the task. (PsycINFO Database Record

Search and the Aging Mind: The Promise and Limits of the Cognitive Control Hypothesis of Age Differences in Search.

Search is a prerequisite for successful performance in a broad range of tasks ranging from making decisions between consumer goods to memory retrieval. How does aging impact search processes in such disparate situations? Aging is associated with structural and neuromodulatory brain changes that underlie cognitive control processes, which in turn have been proposed as a domain-general mechanism controlling search in external environments as well as memory. We review the aging literature to evaluate the cognitive control hypothesis that suggests that age-related change in cognitive control underlies age differences in both external and internal search. We also consider the limits of the cognitive control hypothesis and propose additional mechanisms such as changes in strategy use and affect that may be necessary to understand how aging affects search.

Eye movements reveal memory processes during similarity- and rule-based decision making.

Recent research suggests that when people retrieve information from memory they tend to fixate on the location where the information had appeared during encoding. We used this phenomenon to investigate if different information is activated in memory when people use a rule- versus a similarity-based decision strategy. In two studies, participants first memorized multiple pieces of information about various job candidates (exemplars). In subsequent test trials they judged the suitability of new candidates that varied in their similarity to the previously learned exemplars. Results show that when using similarity, but not when using a rule, participants fixated longer on the previous location of exemplars that resembled the new candidates than on the location of dissimilar exemplars. This suggests that people using similarity retrieve previously learned exemplars, whereas people using a rule do not. The study illustrates that eye movements can provide new insights into the memory processes underlying decision making.