Information integration in multiple cue judgment: a division of labor hypothesis

Analogies for modeling belief dynamics

Belief dynamics has an important role in shaping our responses to natural and societal phenomena, ranging from climate change and pandemics to immigration and conflicts. Researchers often base their models of belief dynamics on analogies to other systems and processes, such as epidemics or ferromagnetism. Similar to other analogies, analogies for belief dynamics can help scientists notice and study properties of belief systems that they would not have noticed otherwise (conceptual mileage). However, forgetting the origins of an analogy may lead to some less appropriate inferences about belief dynamics (conceptual baggage). Here, we review various analogies for modeling belief dynamics, discuss their mileage and baggage, and offer recommendations for using analogies in model development.

What is the airspeed velocity of an unladen swallow? modeling numerical judgments of realistic stimuli

Research on processes of multiple-cue judgments usually uses artificial stimuli with predefined cue structures, such as artificial bugs with four binary features like back color, belly color, gland size, and spot shape. One reason for using artifical stimuli is that the cognitive models used in this area need known cues and cue values. This limitation makes it difficult to apply the models to research questions with complex naturalistic stimuli with unknown cue structure. In two studies, building on early categorization research, we demonstrate how cues and cue values of complex naturalistic stimuli can be extracted from pairwise similarity ratings with a multidimensional scaling analysis. These extracted cues can then be used in a state-of-the-art hierarchical Bayesian model of numerical judgments. In the first study, we show that predefined cue structures of artificial stimuli are well recovered by an MDS analysis of similarity judgments and that using these MDS-based attributes as cues in a cognitive model of judgment data from an existing experiment leads to the same inferences as when the original cue values were used. In the second study, we use the same procedure to replicate previous findings from multiple-cue judgment literature using complex naturalistic stimuli.

Is numerical information always beneficial? Verbal and numerical cue-integration in additive and non-additive tasks

When people use rule-based integration of abstracted cues to make multiple-cue judgments they tend to default to linear additive integration of the cues, which may interfere with efficient learning in non-additive tasks. We hypothesize that this effect becomes especially pronounced when cues are presented numerically rather than verbally, because numbers elicit expectations about a task with a simple numerical solution that can be appropriately addressed by linear and additive integration. This predicts that, relative to a verbal format, a numerical format should be advantageous for learning in additive tasks, but detrimental for learning in non-additive tasks. In two experiments, we find support for the hypothesis that a verbal format can improve learning in non-additive tasks. The division-of-labor between cognitive processes observed in previous research (Juslin et al., 2008), with cue abstraction in additive tasks and exemplar memory in non-additive tasks, was only present in conditions with numeric information and may therefore in part be driven by the use of numeric formats. This illustrates how surface characteristic of stimuli can elicit different priors about the nature of the variables and the generative model that produced the cues and the criterion. We fitted cue-abstraction and exemplar algorithms by PNP-modeling (Sundh et al., 2021). At the end of training both cue abstraction and exemplar memory processes primarily involved exact analytic processes marred by occasional error, rather than the noisy and approximate intuitive processes typically assumed in previous studies - specifically, cue abstraction was primarily implemented by number crunching and exemplar memory by rote memorization.

Different types of uncertainty in multisensory perceptual decision making

Efficient decision-making requires accounting for sources of uncertainty (noise, or variability). Many studies have shown how the nervous system is able to account for perceptual uncertainty (noise, variability) that arises from limitations in its own abilities to encode perceptual stimuli. However, many other sources of uncertainty exist, reflecting for example variability in the behaviour of other agents or physical processes. Here we review previous studies on decision making under uncertainty as a function of the different types of uncertainty that the nervous system encounters, showing that noise that is intrinsic to the perceptual system can often be accounted for near-optimally (i.e. not statistically different from optimally), whereas accounting for other types of uncertainty can be much more challenging. As an example, we present a study in which participants made decisions about multisensory stimuli with both intrinsic (perceptual) and extrinsic (environmental) uncertainty and show that the nervous system accounts for these differently when making decisions: they account for internal uncertainty but under-account for external. Human perceptual systems may be well equipped to account for intrinsic (perceptual) uncertainty because, in principle, they have access to this. Accounting for external uncertainty is more challenging because this uncertainty must be learned. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

More than two intuitions

We consider an underdeveloped feature of De Neys's model. Decisions with multiple intuitions per option are neither trivial to explain nor rare. These decision scenarios are crucial for an assessment of the model's generalizability and adequacy. Besides monitoring absolute differences in intuition strength, the mind might add the strengths of intuitions per choice option, leading to competing and testable hypotheses.

Sequential information processing in persuasion

We present a theory of sequential information processing in persuasion (SIP). It extends assumptions of the heuristic-systematic model, in particular the idea that information encountered early in a persuasion situation may affect the processing of subsequent information. SIP also builds on the abstraction from content-related dichotomies in accord with the parametric unimodel of social judgment. SIP features one constitutional axiom and three main postulates: (A) Persuasion is the sequential processing of information that is relevant to judgment formation. (1) Inferences drawn from initial information may bias the processing of subsequent information if they are either activated rules or valence expectations that are relevant to the subsequent information. (2) Inferences drawn from initial information are resistant to change. Thus, the interpretation of subsequent information is assimilated to inferences drawn from the initial information. Or, if assimilation is impossible, contrast effects occur. (3) The overall effect of a persuasion attempt corresponds to the recipient’s judgment at the moment the processing of information is terminated. We illustrate how our predictions for assimilation and contrast effects may be tested by presenting results from an experiment (N = 216) in which we presented exactly the same arguments but varied the processing sequence. We discuss theoretical and applied implications of sequence effects for persuasion phenomena, as well as challenges for further research developing and testing the theory.

Biased confabulation in risky choice

When people make risky decisions based on past experience, they must rely on memory. The nature of the memory representations that support these decisions is not yet well understood. A key question concerns the extent to which people recall specific past episodes or whether they have learned a more abstract rule from their past experience. To address this question, we examined the precision of the memories used in risky decisions-from-experience. In three pre-registered experiments, we presented people with risky options, where the outcomes were drawn from continuous ranges (e.g., 100-190 or 500-590), and then assessed their memories for the outcomes experienced. In two preferential tasks, people were more risk seeking for high-value than low-value options, choosing as though they overweighted the outcomes from more extreme ranges. Moreover, in two preferential tasks and a parallel evaluation task, people were very poor at recalling the exact outcomes encountered, but rather confabulated outcomes that were consistent with the outcomes they had seen and were biased towards the more extreme ranges encountered. This common pattern suggests that the observed decision bias in the preferential task reflects a basic cognitive process to overweight extreme outcomes in memory. These results highlight the importance of the edges of the distribution in providing the encoding context for memory recall. They also suggest that episodic memory influences decision-making through gist memory and not through direct recall of specific instances.

Comparing attribute-based and memory-based preferential choice

Common theories of multiattribute preferential choice predict that people choose options that have on average better attribute values than alternative options. However, following an alternative memory-based view on preferences people might sometimes prefer options that are more similar to memorized options that were experienced positively in the past. In two incentivized preferential choice experiments (N = 32, N = 28), we empirically compare these theoretical accounts, finding support for the memory-based value theory. Computational modeling using predictive model comparison showed that only a few participants could be described by a model that uses sums of subjectively weighted attribute values when experience was available. Most participants’ choices resembled the predictions of the memory-based model, according to which preferences are based on the similarity between novel and old memorized options. Further, people whose experience consisted of direct sensory exposure, like tasting a portion of food, were also those with higher likelihoods of a memory-based process, compared to people whose exposure was indirect. These results highlight the central role of memory and experience in preferential choices and add to the growing evidence for memory and similarity-based processes in the domain of human preferences.

Exemplar-based judgment or direct recall: On a problematic procedure for estimating parameters in exemplar models of quantitative judgment

Exemplar models are often used in research on multiple-cue judgments to describe the underlying process of participants' responses. In these experiments, participants are repeatedly presented with the same exemplars (e.g., poisonous bugs) and instructed to memorize these exemplars and their corresponding criterion values (e.g., the toxicity of a bug). We propose that there are two possible outcomes when participants judge one of the already learned exemplars in some later block of the experiment. They either have memorized the exemplar and their respective criterion value and are thus able to recall the exact value, or they have not learned the exemplar and thus have to judge its criterion value, as if it was a new stimulus. We argue that psychologically, the judgments of participants in a multiple-cue judgment experiment are a mixture of these two qualitatively distinct cognitive processes: judgment and recall. However, the cognitive modeling procedure usually applied does not make any distinction between these processes and the data generated by them. We investigated potential effects of disregarding the distinction between these two processes on the parameter recovery and the model fit of one exemplar model. We present results of a simulation as well as the reanalysis of five experimental data sets showing that the current combination of experimental design and modeling procedure can bias parameter estimates, impair their validity, and negatively affect the fit and predictive performance of the model. We also present a latent-mixture extension of the original model as a possible solution to these issues.

Judgment errors in naturalistic numerical estimation

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

Self-incremental learning vector quantization with human cognitive biases

Human beings have adaptively rational cognitive biases for efficiently acquiring concepts from small-sized datasets. With such inductive biases, humans can generalize concepts by learning a small number of samples. By incorporating human cognitive biases into learning vector quantization (LVQ), a prototype-based online machine learning method, we developed self-incremental LVQ (SILVQ) methods that can be easily interpreted. We first describe a method to automatically adjust the learning rate that incorporates human cognitive biases. Second, SILVQ, which self-increases the prototypes based on the method for automatically adjusting the learning rate, is described. The performance levels of the proposed methods are evaluated in experiments employing four real and two artificial datasets. Compared with the original learning vector quantization algorithms, our methods not only effectively remove the need for parameter tuning, but also achieve higher accuracy from learning small numbers of instances. In the cases of larger numbers of instances, SILVQ can still achieve an accuracy that is equal to or better than those of existing representative LVQ algorithms. Furthermore, SILVQ can learn linearly inseparable conceptual structures with the required and sufficient number of prototypes without overfitting.

Precise/not precise (PNP): A Brunswikian model that uses judgment error distributions to identify cognitive processes

In 1956, Brunswik proposed a definition of what he called intuitive and analytic cognitive processes, not in terms of verbally specified properties, but operationally based on the observable error distributions. In the decades since, the diagnostic value of error distributions has generally been overlooked, arguably because of a long tradition to consider the error as exogenous (and irrelevant) to the process. Based on Brunswik’s ideas, we develop the precise/not precise (PNP) model, using a mixture distribution to model the proportion of error-perturbed versus error-free executions of an algorithm, to determine if Brunswik’s claims can be replicated and extended. In Experiment 1, we demonstrate that the PNP model recovers Brunswik’s distinction between perceptual and conceptual tasks. In Experiment 2, we show that also in symbolic tasks that involve no perceptual noise, the PNP model identifies both types of processes based on the error distributions. In Experiment 3, we apply the PNP model to confirm the often-assumed “quasi-rational” nature of the rule-based processes involved in multiple-cue judgment. The results demonstrate that the PNP model reliably identifies the two cognitive processes proposed by Brunswik, and often recovers the parameters of the process more effectively than a standard regression model with homogeneous Gaussian error, suggesting that the standard Gaussian assumption incorrectly specifies the error distribution in many tasks. We discuss the untapped potentials of using error distributions to identify cognitive processes and how the PNP model relates to, and can enlighten, debates on intuition and analysis in dual-systems theories.

Improving judgment accuracy by sequential adjustment

Judging an object’s value based on relevant cues can be challenging. We propose a simple method to improve judgment accuracy: Instead of estimating a value after seeing all available cues simultaneously, individuals view cues sequentially, one after another, making and adjusting their estimate at each step. The sequential procedure may alleviate computational difficulties in cue integration, leading to higher judgment accuracy. We tested this hypothesis in two real-world tasks in which participants judged either the price of diamonds or the fuel economy of cars. Two studies with professional jewelers and car salespeople show that most participants indeed judged more accurately with a sequential than with a simultaneous procedure. Another two studies with college students further support this finding and show additionally that the sequential procedure could raise the judgment accuracy of inexperienced students to the same level as that of professionals judging with the simultaneous procedure.

Building Memory Representations for Exemplar-Based Judgment: A Role for Ventral Precuneus

The brain networks underlying human multiple-cue judgment, the judgment of a continuous criterion based on multiple cues, have been examined in a few recent studies, and the ventral precuneus has been found to be a key region. Specifically, activation differences in ventral precuneus (as measured with functional magnetic resonance imaging, fMRI) has been linked to an exemplar-based judgment process, where judgments are based on memory for previous similar cases. Ventral precuneus is implicated in various episodic memory processes, notably such that increased activity during learning in this region as well as in the ventromedial prefrontal cortex (vmPFC) and the medial temporal lobes (MTL) have been linked to retrieval success. The present study used fMRI during a multiple-cue judgment task to gain novel neurocognitive evidence informative for the link between learning-related activity changes in ventral precuneus and exemplar-based judgment. Participants (N = 27) spontaneously learned to make judgments during fMRI, in a multiple-cue judgment task specifically designed to induce exemplar-based processing. Contrasting brain activity during late learning to early learning revealed higher activity in ventral precuneus, the bilateral MTL, and the vmPFC. Activity in the ventral precuneus and the vmPFC was found to parametrically increase between each judgment event, and activity levels in the ventral precuneus predicted performance after learning. These results are interpreted such that the ventral precuneus supports the aspects of exemplar-based processes that are related to episodic memory, tentatively by building, storing, and being implicated in retrieving memory representations for judgment.

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.

A Similarity-Based Process for Human Judgment in the Parietal Cortex

One important distinction in psychology is between inferences based on associative memory and inferences based on analysis and rules. Much previous empirical work conceive of associative and analytical processes as two exclusive ways of addressing a judgment task, where only one process is selected and engaged at a time, in an either-or fashion. However, related work indicate that the processes are better understood as being in interplay and simultaneously engaged. Based on computational modeling and brain imaging of spontaneously adopted judgment strategies together with analyses of brain activity elicited in tasks where participants were explicitly instructed to perform similarity-based associative judgments or rule-based judgments (n = 74), we identified brain regions related to the two types of processes. We observed considerable overlap in activity patterns. The precuneus was activated for both types of judgments, and its activity predicted how well a similarity-based model fit the judgments. Activity in the superior frontal gyrus predicted the fit of a rule-based judgment model. The results suggest the precuneus as a key node for similarity-based judgments, engaged both when overt responses are guided by similarity-based and rule-based processes. These results are interpreted such that similarity-based processes are engaged in parallel to rule-based-processes, a finding with direct implications for cognitive theories of judgment.

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.

Compound risk judgment in tasks with both idiosyncratic and systematic risk: The "Robust Beauty" of additive probability integration

In this study, we explore how people integrate risks of assets in a simulated financial market into a judgment of the conjunctive risk that all assets decrease in value, both when assets are independent and when there is a systematic risk present affecting all assets. Simulations indicate that while mental calculation according to naïve application of probability theory is best when the assets are independent, additive or exemplar-based algorithms perform better when systematic risk is high. Considering that people tend to intuitively approach compound probability tasks using additive heuristics, we expected the participants to find it easiest to master tasks with high systematic risk - the most complex tasks from the standpoint of probability theory - while they should shift to probability theory or exemplar memory with independence between the assets. The results from 3 experiments confirm that participants shift between strategies depending on the task, starting off with the default of additive integration. In contrast to results in similar multiple cue judgment tasks, there is little evidence for use of exemplar memory. The additive heuristics also appear to be surprisingly context-sensitive, with limited generalization across formally very similar tasks.

Justifying the judgment process affects neither judgment accuracy, nor strategy use

Decision quality is often evaluated based on whether decision makers can adequately explain the decision process. Accountability often improves judgment quality because decision makers weigh and integrate information more thoroughly, but it could also hurt judgment processes by disrupting retrieval of previously encountered cases. We investigated to what degree process accountability motivates decision makers to shift from retrieval of past exemplars to rule-based integration processes. This shift may hinder accurate judgments in retrieval-based configural judgment tasks (Experiment 1) but may improve accuracy in elemental judgment tasks requiring weighing and integrating information (Experiment 2). In randomly selected trials, participants had to justify their judgments. Process accountability neither changed how accurately people made a judgment, nor the judgment strategies. Justifying the judgment process only decreased confidence in trials involving a justification. Overall, these results imply that process accountability may affect judgment quality less than expected.

Measuring the relative contributions of rule-based and exemplar-based processes in judgment: Validation of a simple model

Judgments and decisions can rely on rules to integrate cue information or on the retrieval of similar exemplars from memory. Research on exemplar-based processes in judgment has discovered several task variables influencing the dominant mode of processing. This research often aggregates data across participants or classifies them as using either exemplar-based or cue-based processing. It has been argued for theoretical and empirical reasons that both kinds of processes might operate together or in parallel. Hence, a classification of strategies may be a severe oversimplification that also sacrifices statistical power to detect task effects. We present a simple measurement tool combining both processing modes. The simple model contains a mixture parameter quantifying the relative contribution of both kinds of processes in a judgment and decision task. In three experiments, we validate the measurement model by demonstrating that instructions and task variables affect the mixture parameter in predictable ways, both in memory-based and screen-based judgments.

Learning from observation, feedback, and intervention in linear and non-linear task environments

This multiple-cue judgment study investigates whether we can manipulate the judgment strategy and increase accuracy in linear and non-linear cue-criterion environments just by changing the training mode. Three experiments show that accuracy in simple linear additive task environments are improved with feedback training and intervention training, while accuracy in complex multiplicative tasks are improved with observational training. The observed interaction effect suggests that the training mode invites different strategies that are adjusted as a function of experience to the demands from the underlying cue-criterion structure. Thus, feedback and the intervention training modes invite cue abstraction, an effortful but successful strategy in combination with simple linear task structures, and observational training invites exemplar memory processes, a simple but successful strategy in combination with complex non-linear task structures. The study discusses adaptive cognition and the implication of the different training modes across a life span and for clinical populations.

Learning from experience in nonlinear environments: Evidence from a competition scenario

We test people's ability to learn to estimate a criterion (probability of success in a competition scenario) that requires aggregating information in a nonlinear manner. The learning environments faced by experimental participants are kind in that they are characterized by immediate, accurate feedback involving either naturalistic outcomes (information on winning and/or ranking) or the normatively correct probabilities. We find no evidence of learning from the former and modest learning from the latter, except that a group of participants endowed with a memory aid performed substantially better. However, when the task is restructured such that information should be aggregated in a linear fashion, participants learn to make more accurate assessments. Our experiments highlight the important role played by prior beliefs in learning tasks, the default status of linear aggregation in many inferential judgments, and the difficulty of learning in nonlinear environments even in the presence of veridical feedback.

Is there something special with probabilities?--insight vs. computational ability in multiple risk combination

While a wealth of evidence suggests that humans tend to rely on additive cue combination to make controlled judgments, many of the normative rules for probability combination require multiplicative combination. In this article, the authors combine the experimental paradigms on probability reasoning and multiple-cue judgment to allow a comparison between formally identical tasks that involve probability vs. other task contents. The purpose was to investigate if people have cognitive algorithms for the combination, specifically, of probability, affording multiplicative combination in the context of probability. Three experiments suggest that, although people show some signs of a qualitative understanding of the combination rules that are specific to probability, in all but the simplest cases they lack the cognitive algorithms needed for multiplication, but instead use a variety of additive heuristics to approximate the normative combination. Although these heuristics are surprisingly accurate, normative combination is not consistently achieved until the problems are framed in an additive way.

Neural substrates of similarity and rule-based strategies in judgment

Making accurate judgments is a core human competence and a prerequisite for success in many areas of life. Plenty of evidence exists that people can employ different judgment strategies to solve identical judgment problems. In categorization, it has been demonstrated that similarity-based and rule-based strategies are associated with activity in different brain regions. Building on this research, the present work tests whether solving two identical judgment problems recruits different neural substrates depending on people's judgment strategies. Combining cognitive modeling of judgment strategies at the behavioral level with functional magnetic resonance imaging (fMRI), we compare brain activity when using two archetypal judgment strategies: a similarity-based exemplar strategy and a rule-based heuristic strategy. Using an exemplar-based strategy should recruit areas involved in long-term memory processes to a larger extent than a heuristic strategy. In contrast, using a heuristic strategy should recruit areas involved in the application of rules to a larger extent than an exemplar-based strategy. Largely consistent with our hypotheses, we found that using an exemplar-based strategy led to relatively higher BOLD activity in the anterior prefrontal and inferior parietal cortex, presumably related to retrieval and selective attention processes. In contrast, using a heuristic strategy led to relatively higher activity in areas in the dorsolateral prefrontal and the temporal-parietal cortex associated with cognitive control and information integration. Thus, even when people solve identical judgment problems, different neural substrates can be recruited depending on the judgment strategy involved.

Where did that come from?--Identifying the source of a sample

People's ability to summarize their knowledge of an observed numerical variable has been extensively studied. However, many real-life situations require people to go beyond summary statistics and infer which process or distribution has generated a sample. The present study investigates the extent to which people can make such inferences when the experienced variable is continuous and when they have had previous experience with instances of the variable. It also tests specific predictions derived from three possible cognitive processes of how inferences about a generating distribution are made. The results indicate that participants are efficient and flexible intuitive statisticians, requiring only as little as four observations in a sample to successfully infer which distribution it came from. Further, the results indicate that the cognitive process supporting the inference uses statistical properties of both an experienced distribution and a presented test sample, as suggested by the Naïve Sampling Model (NSM).

Are all data created equal?--Exploring some boundary conditions for a lazy intuitive statistician

The study investigated potential effects of the presentation order of numeric information on retrospective subjective judgments of descriptive statistics of this information. The studies were theoretically motivated by the assumption in the naïve sampling model of independence between temporal encoding order of data in long-term memory and retrieval probability (i.e. as implied by a "random sampling" from memory metaphor). In Experiment 1, participants experienced Arabic numbers that varied in distribution shape/variability between the first and the second half of the information sequence. Results showed no effects of order on judgments of mean, variability or distribution shape. To strengthen the interpretation of these results, Experiment 2 used a repeated judgment procedure, with an initial judgment occurring prior to the change in distribution shape of the information half-way through data presentation. The results of Experiment 2 were in line with those from Experiment 1, and in addition showed that the act of making explicit judgments did not impair accuracy of later judgments, as would be suggested by an anchoring and insufficient adjustment strategy. Overall, the results indicated that participants were very responsive to the properties of the data while at the same time being more or less immune to order effects. The results were interpreted as being in line with the naïve sampling models in which values are stored as exemplars and sampled randomly from long-term memory.

Haunted by a Doppelgänger

Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

Why does cue polarity information provide benefits in inference problems? The role of strategy selection and knowledge of cue importance

Knowledge about cue polarity (i.e., the sign of a cue-criterion relation) seems to boost performance in a wide range of inference tasks. Knowledge about cue polarity information may enhance performance by increasing (1) the reliance on rule- relative to similarity-based strategies, and (2) explicit knowledge about the relative importance of cues. We investigated the relative contribution of these two mechanisms in a multiple-cue judgment task and a categorization task, which typically differ in the inference strategies they elicit and potentially the explicit task knowledge available to participants. In both tasks participants preferred rule-based relative to similarity-based strategies and had more knowledge about cue importance when cue polarity information was provided. Strategy selection was not related to increases in performance in the categorization task and could only partly explain increases in performance in the judgment task. In contrast, explicit knowledge about the importance of cues was related to better performance in both categorization and judgment independently of the strategy used. In sum, our results suggest that the benefits of receiving cue polarity information may span across tasks, such multiple-cue judgment and categorization, primarily by enhancing knowledge of relative cue importance.

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.

Reducing cognitive biases in probabilistic reasoning by the use of logarithm formats

Research on probability judgment has traditionally emphasized that people are susceptible to biases because they rely on "variable substitution": the assessment of normative variables is replaced by assessment of heuristic, subjective variables. A recent proposal is that many of these biases may rather derive from constraints on cognitive integration, where the capacity-limited and sequential nature of controlled judgment promotes linear additive integration, in contrast to many integration rules of probability theory (Juslin, Nilsson, & Winman, 2009). A key implication by this theory is that it should be possible to improve peoples' probabilistic reasoning by changing probability problems into logarithm formats that require additive rather than multiplicative integration. Three experiments demonstrate that recasting tasks in a way that allows people to arrive at the answers by additive integration decreases cognitive biases, and while people can rapidly learn to produce the correct answers in an additive formats, they have great difficulty doing so with a multiplicative format.

Cue integration vs. exemplar-based reasoning in multi-attribute decisions from memory: A matter of cue representation

Inferences about target variables can be achieved by deliberate integration of probabilistic cues or by retrieving similar cue-patterns (exemplars) from memory. In tasks with cue information presented in on-screen displays, rule-based strategies tend to dominate unless the abstraction of cue-target relations is unfeasible. This dominance has also been demonstrated — surprisingly — in experiments that demanded the retrieval of cue values from memory (M. Persson & J. Rieskamp, 2009). In three modified replications involving a fictitious disease, binary cue values were represented either by alternative symptoms (e.g., fever vs. hypothermia) or by symptom presence vs. absence (e.g., fever vs. no fever). The former representation might hinder cue abstraction. The cues were predictive of the severity of the disease, and participants had to infer in each trial who of two patients was sicker. Both experiments replicated the rule-dominance with present-absent cues but yielded higher percentages of exemplar-based strategies with alternative cues. The experiments demonstrate that a change in cue representation may induce a dramatic shift from rule-based to exemplar-based reasoning in formally identical tasks.

Integration of perceptual information in word access

According to experiential theories of language comprehension, perceptual information plays an essential role when word meanings are accessed. We conducted four experiments to investigate how different types of perceptual information such as colour and shape are combined during word access. One possibility is that the colour and shape of a word's referent are activated independently from one another and are combined in an additive manner. Alternatively, words might activate perceptual representations via a multiplicative integration of colour and shape. Experiment 1 established that participants follow a multiplicative similarity rule when they judge the similarity of schematic pictures to actual fruits and vegetables. In Experiments 2 to 4, participants performed a classification task, a lexical decision task, or a word-naming task on names of fruits and vegetables that were superimposed on a background picture. Responses were facilitated only when both colour and shape of the picture matched the word's referents. Response times were associated negatively with mean similarity ratings and the consistency of these ratings obtained in the first experiment. These results suggest a multiplicative integration of different types of perceptual information during word access.

The Effectiveness of Feedback in Multiple-Cue Probability Learning

How effective are different types of feedback in helping us to learn multiple contingencies? This article attempts to resolve a paradox whereby, in comparison to simple outcome feedback, additional feedback either fails to enhance or is actually detrimental to performance in nonmetric multiple-cue probability learning (MCPL), while in contrast the majority of studies of metric MCPL reveal improvements at least with some forms of feedback. In three experiments we demonstrate that if feedback assists participants to infer cue polarity then it can in fact be effective in nonmetric MCPL. Participants appeared to use cue polarity information to adopt a linear judgement strategy, even though the environment was nonlinear. The results reconcile the paradoxical contrast between metric and nonmetric MCPL and support previous findings of people's tendency to assume linearity and additivity in probabilistic cue learning.