Base-rate Effect Research Articles

Criterion shifting in an unfamiliar face-matching task: Effects of base rates, payoffs, and perceptual discriminability.

Criterion shifting in an unfamiliar face-matching task: Effects of base rates, payoffs, and perceptual discriminability.

Effect of a context shift on the inverse base-rate effect

The Inverse Base Rate Effect (IBRE) is a non-rational behavioural phenomenon in predictive learning. In the IBRE, participants learn that a stimulus compound AB leads to one outcome and that another compound AC leads to a different outcome. Importantly, AB and its outcome are presented three times as often as AC (and its outcome). On test, when asked which outcome to expect on presentation of the novel compound BC, participants preferentially select the rarer outcome, previously associated with AC. This is irrational because, objectively, the common outcome is more likely. Usually, the IBRE is attributed to greater attention paid to cue C than to cue B, and so is an excellent test for attentional learning models. The current experiment tested a simple account of attentional learning proposed by Le Pelley, Mitchell, Beesley, George, and Wills (2016) where attention paid to a stimulus is determined by its associative strength. This account struggles to capture the IBRE, but a potential solution presented by Le Pelley et al. (2016) appeals to the role of experimental context. In the present paper, we derived four predictions from the context explanation concerning the effect of changing to a novel experimental context at test, and examined these predictions empirically. Only one of the predictions, concerning the effect of a context shift on responding to a novel cue, was supported. These results fail to support both the context explanation suggested by Le Pelley et al. (2016) and the current leading account of the IBRE, EXIT (Kruschke, 2001b), but provide avenues for further research. Keywords: Inverse base-rate effect, EXIT, predictive learning, categorization

Neural correlates of the inverse base rate effect.

The inverse base rate effect (IBRE) is a nonrational behavioral phenomenon in predictive learning. Canonically, participants learn that the AB stimulus compound leads to one outcome and that AC leads to another outcome, with AB being presented three times as often as AC. When subsequently presented with BC, the outcome associated with AC is preferentially selected, in opposition to the underlying base rates of the outcomes. The current leading explanation is based on error‐driven learning. A key component of this account is prediction error, a concept previously linked to a number of brain areas including the anterior cingulate, the striatum, and the dorsolateral prefrontal cortex. The present work is the first fMRI study to directly examine the IBRE. Activations were noted in brain areas linked to prediction error, including the caudate body, the anterior cingulate, the ventromedial prefrontal cortex, and the right dorsolateral prefrontal cortex. Analyzing the difference in activations for singular key stimuli (B and C), as well as frequency matched controls, supports the predictions made by the error‐driven learning account.

The unintended effects of health information base rates on health risk estimates and behavioral intentions

Social marketers and health advertisers often use statistical health information to craft customized messages for specific consumer segments. However, the composition of these segments can vary greatly, inherently resulting in different base rate percentages about the same health issue (e.g. ‘51.9% of all COVID 19-related deaths in the U.S. occurred among Caucasians’ vs. ‘16.6% of all COVID 19-related deaths in the U.S. occurred among Hispanics’) that can potentially lead to different responses from consumers. Therefore, this research examines how individuals process, and respond to, manipulated base rates. Study 1 demonstrates that higher (vs. lower) base rates increase individuals’ healthy behavioural intentions by elevating their risk perceptions. Study 2 uncovers a more complex serial process underlying the impact of base rates on intentions to follow recommended behaviours in health messages. Importantly, we demonstrate a critical effect reversal such that higher base rates have an unintended negative impact on individuals’ compliance intentions when involvement is lower (vs. higher). Overall, our findings show how the use of base rates in health messages can serve as a ‘double-edged sword’.

Do behavioral base rates impact associated moral judgments?

In a series of studies, we ask whether and to what extent the base rate of a behavior influences associated moral judgment. Previous research aimed at answering different but related questions are suggestive of such an effect. However, these other investigations involve injunctive norms and special reference groups which are inappropriate for an examination of the effects of base rates per se. Across five studies, we find that, when properly isolated, base rates do indeed influence moral judgment, but they do so with only very small effect sizes. In another study, we test the possibility that the very limited influence of base rates on moral judgment could be a result of a general phenomenon such as the fundamental attribution error, which is not specific to moral judgment. The results suggest that moral judgment may be uniquely resilient to the influence of base rates. In a final pair of studies, we test secondary hypotheses that injunctive norms and special reference groups would inflate any influence on moral judgments relative to base rates alone. The results supported those hypotheses.

Hearing hooves, thinking zebras: A review of the inverse base-rate effect.

People often fail to use base-rate information appropriately in decision-making. This is evident in the inverse base-rate effect, a phenomenon in which people tend to predict a rare outcome for a new and ambiguous combination of cues. While the effect was first reported in 1988, it has recently seen a renewed interest from researchers concerned with learning, attention and decision-making. However, some researchers have raised concerns that the effect arises in specific circumstances and is unlikely to provide insight into general learning and decision-making processes. In this review, we critically evaluate the evidence for and against the main explanations that have been proposed to explain the effect, and identify where this evidence is currently weak. We argue that concerns about the effect are not well supported by the data. Instead, the evidence supports the conclusion that the effect is a result of general mechanisms that provides a useful opportunity to understand the processes involved in learning and decision making. We discuss gaps in our knowledge and some promising avenues for future research, including the relevance of the effect to models of attentional change in learning, an area where the phenomenon promises to contribute new insights.

Attention biases in the inverse base-rate effect persist into new learning.

The inverse base-rate effect is a tendency to predict the rarer of two outcomes when presented with cues that make conflicting predictions. Attention-based accounts of the effect appeal to prioritised attention to predictors of rare outcomes. Changes in the processing of these cues are predicted to increase the rate at which they are learned about in the future (i.e., their associability). Our previous work has shown that the development of the inverse base-rate effect is accompanied by greater overt attention to the rare predictor while participants made predictions, and during feedback, and these biases change in different ways depending on the stage of training and global base-rate differences. It is unknown whether these gaze patterns reflect the manner in which cues are prioritised for learning or are merely a consequence of learning what the cues predict. This study tested whether the associability of common and rare predictors differed, and if so, how this difference changed as a function of training length and the presence of base-rate differences in the outcomes. Experiment 1 tested cue associability using a second learning task presented after either short or long training. The results suggest an associability advantage for rare predictors that weakens with extended training and is not strongly affected by the presence of global base-rate differences. However, Experiment 2 showed a clear effect of base-rate differences on choice after very brief training, indicating that attention biases as measured by associability change are not sufficient to produce the inverse base-rate effect.

Dissecting EXIT

Kruschke’s EXIT model (Kruschke, 2001b) has been very successful in explaining a variety of learning phenomena by means of selective attention. In particular, EXIT produces learned predictiveness effects (Le Pelley and McLaren, 2003), the inverse base rate effect (Kruschke, 1996; Medin and Edelson, 1988), inattention after blocking (Beesley and Le Pelley, 2011; Kruschke and Blair, 2000), differential cue use across the stimulus space (Aha and Goldstone, 1992) and conditional learned predictiveness effects (Uengoer et al., 2013). We dissect EXIT into its component mechanisms (error-driven learning, selective attention, attentional competition, rapid attention shifts and exemplar mediation of attention) and test whether simplified versions of EXIT can explain the same experimental results as the full model. Most phenomena can be explained by either rapid attention shifts or attentional competition, without the need for combining them as in EXIT. There is little evidence for exemplar mediation of attention when people learn linearly separable category structures (e.g. Kruschke and Blair, 2000; Le Pelley and McLaren, 2003); whether or not it is needed for non-linear categories depends on stimulus representation (Aha and Goldstone, 1992; Uengoer et al., 2013). On the whole, we believe that attentional competition—embodied in a model which we dub CompAct—offers the simplest explanation for the experimental results we examine.

The impact of thin‐capitalization rules on the location of multinational firms’ foreign affiliates

AbstractThis paper examines how restrictions on the tax deductibility of interest cost affect location choices of multinational corporations (MNCs). Many countries have introduced so‐called thin‐capitalization rules (TCRs) to prevent MNCs from shifting their tax base to countries with lower tax rates. As of 2012, in our sample of 172 countries, 61 countries have implemented a TCR. Using information on nearly all new foreign investments of German MNCs, we provide a number of new and interesting insights in how TCRs affect the decision of where to locate foreign entities. In particular, stricter TCRs are found to negatively affect location choices of MNCs. Our results include estimates of own‐ and cross‐elasticities of location choice and also novel results on the relative importance of tax base vs. tax rate effects. We finally provide estimates for different uncoordinated as well as coordinated policy scenarios.

Learned predictiveness models predict opposite attention biases in the inverse base-rate effect.

Several attention-based models of associative learning are built upon the learned predictiveness principle, whereby learning is optimized by attending to the most predictive features and ignoring the least predictive features. Despite their functional similarity, these models differ in their formal mechanisms and thus may produce very different predictions in some circumstances. As we demonstrate, this is particularly evident in the inverse base-rate effect. Using simulations with a modified Mackintosh model and the EXIT model, we found that models based on the learned predictiveness principle can account for rare-outcome choice biases associated with the inverse base-rate effect, despite making opposite predictions for relative attention to rare versus common predictors. The models also make different predictions regarding changes in attention across training, and effects of context associations on attention to cues. Using a human causal learning task, we replicated the inverse base-rate effect and a recently reported reduction in this effect when the context is not predictive of the common outcome and used eye-tracking to test model predictions about changes in attention both prior to making a decision, and during feedback. The results support the predictions made by EXIT, where the rare predictor commands greater attention than the common predictor throughout training. In addition, patterns of attention prior to making a decision differed to those during feedback, where effects of using a partially predictive context were evident only prior to making a prediction. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Relation-Based Categorization and Category Learning as a Result From Structural Alignment. The RoleMap Model.

Relational categories are structure-based categories, defined not only by their internal properties but also by their extrinsic relations with other categories. For example, predator could not be defined without referring to hunt and prey. Even though they are commonly used, there are few models taking into account any relational information. A category learning and categorization model aiming to fill this gap is presented. Previous research addresses the hypothesis that the acquisition and the use of relational categories are underlined by structural alignment. That is why the proposed RoleMap model is based on mechanisms often studied as the analogy-making sub-processes, developed on a suitable for this cognitive architecture. RoleMap is conceived in such a way that relation-based category learning and categorization emerge while other tasks are performed. The assumption it steps on is that people constantly make structural alignments between what they experience and what they know. During these alignments various mappings and anticipations emerge. The mappings capture commonalities between the target (the representation of the current situation) and the memory, while the anticipations try to fill the missing information in the target, based on the conceptual system. Because some of the mappings are highly important, they are transformed into a distributed representation of a new concept for further use, which denotes the category learning. When some knowledge is missing in the target, meaning it is uncategorized, that knowledge is transferred from memory in the form of anticipations. The wining anticipation is transformed into a category member, denoting the act of categorization. The model’s behavior emerges from the competition between these two pressures – to categorize and to create new categories. Several groups of simulations demonstrate that the model can deal with relational categories in a context-dependent manner and to account for single-shot learning, challenging most of the existing approaches to category learning. The model also simulates previous empirical data pointing to the thematic categories and to the puzzling inverse base-rate effect. Finally, the model’s strengths and limitations are evaluated.

Model-based fMRI reveals dissimilarity processes underlying base rate neglect.

Extensive evidence suggests that people use base rate information inconsistently in decision making. A classic example is the inverse base rate effect (IBRE), whereby participants classify ambiguous stimuli sharing features of both common and rare categories as members of the rare category. Computational models of the IBRE have posited that it arises either from associative similarity-based mechanisms or from dissimilarity-based processes that may depend on higher-level inference. Here we develop a hybrid model, which posits that similarity- and dissimilarity-based evidence both contribute to the IBRE, and test it using functional magnetic resonance imaging data collected from human subjects completing an IBRE task. Consistent with our model, multivoxel pattern analysis reveals that activation patterns on ambiguous test trials contain information consistent with dissimilarity-based processing. Further, trial-by-trial activation in left rostrolateral prefrontal cortex tracks model-based predictions for dissimilarity-based processing, consistent with theories positing a role for higher-level symbolic processing in the IBRE.

I know the rule, but I'll just go with my gut: is there a rational use of intuition?

ABSTRACTResearch has established that human thinking is often biased by intuitive judgement. The base-rate neglect effect provides such an example, so named because people often support their decisions in stereotypical individuating information, neglecting base-rates. Here, we test the hypothesis that reasoners acknowledge information provided by base-rates and may use individuating information in support of a “rational” decision process. Results from four experiments show that “base-rate neglecting” occurs when participants acknowledge sample distributions; participants who prefer individuating over base-rate information perceive base-rates as less diagnostic and are more confident in their individuating-based responses; and that posterior probabilities (assigned after all relevant information is considered) predict more individuating-based responses for individuating-preference participants (suggesting a rational process). However, data also show a deeper form of base-rate neglect: even when some participants report to prefer base-rate information, define individuating information as non-diagnostic, and their posterior probabilities suggest otherwise, they still provide individuating-based responses.

Cognitive control and midline theta adjust across multiple timescales

Cognitive control of attention in conflict situations is a basic skill that is vital for goal-oriented behavior. Behavioral evidence shows that conflict control occurs over successive trials as well as longer time scales of trial blocks, but the relation among time scales as well as their neural mechanisms are unclear. This study used measures of behavior, EEG, and a simple quantitative model to test the hypothesis that conflict control at the block level is not exclusively driven by control adjustments over successive trials. Young adults performed an auditory Simon task, and the base rate of compatible vs. incompatible trials was manipulated in separate blocks (25, 50, 75% compatible). EEG data were analyzed using independent component analysis (ICA) to define cortical mechanisms of any base rate and trial-by-trial sequence effects. Reaction time measures had both sequence and base rate effects. Two fronto-medial ICA components indexed sequence and base rate effects, with specific profiles for evoked potentials and oscillations in the theta and alpha frequency bands. Predictive modeling showed that sequence effects accounted for a minority of the variance on behavioral and ICA measures (all < 36%). The results strongly suggest that the base rate manipulation affected behavior and many neural measures beyond the influence of sequence effects.

Detection and Selection Decisions with Conditional Feedback: Interaction of Task Uncertainty and Base Rate

AbstractEffects of task uncertainty on decision thresholds in a multiple cue decision task were examined under two types of feedback and three base rate conditions. In most such decision experiments, participants receive feedback after every trial (full feedback) with a single (usually .5) base rate. Our experiment explored conditional (decision‐contingent) feedback, in a task representing a detection problem (passenger screening) in which the decision maker receives no feedback unless the decision is positive (search the passenger). Increased uncertainty made all dependent measures worse. Task uncertainty had detrimental effects on both judgment and decision making, and interacted with effects of feedback and base rate. Decision performance was better with full feedback than with conditional feedback, but not by much. There may be no single unifying explanation for results of our base rate manipulation. Conditional feedback generally resulted in fewer positive decisions than full feedback, but not in the low (.1) base rate condition. Results provide partial support for constructivist encoding and for accuracy maximization with moderate and high base rates, but not with a low base rate. Our results indicated lower selection rates with conditional feedback compared with full feedback in moderate and high base rate conditions, and a more exploratory strategy with higher selection rates with conditional feedback compared with full feedback when base rate was low. Copyright © 2018 John Wiley &amp; Sons, Ltd.

Statistically inaccurate and morally unfair judgements via base rate intrusion.

From a statistical standpoint, judgements about an individual are more accurate if base rates about the individual's social group are taken into account 1-4 . But from a moral standpoint, using these base rates is considered unfair and can even be illegal 5-9 . Thus, the imperative to be statistically accurate is directly at odds with the imperative to be morally fair. This conflict was resolved by creating tasks in which Bayesian rationality and moral fairness were aligned, thereby allowing social judgements to be both accurate and fair. Despite this alignment, we show that social judgements were inaccurate and unfair. Instead of appropriately setting aside social group differences, participants erroneously relied on them when making judgements about specific individuals. This bias-which we call base rate intrusion-was robust, generalized across various social groups (gender, race, nationality and age), and differed from analogous non-social judgements. Results also demonstrate how social judgements can be corrected to achieve both statistical accuracy and moral fairness. Overall, these data (total N = 5,138) highlight the pernicious effects of social base rates: under conditions that closely approximate those of everyday life 10-12 , these base rates can undermine the rationality and fairness of human judgements.

Effects of outcome and trial frequency on the inverse base-rate effect.

The inverse base-rate effect is a bias in contingency learning in which participants tend to predict a rare outcome for a conflicting set of perfectly predictive cues. Although the effect is often explained by attention biases during learning, inferential strategies at test may also contribute substantially to the effect. In three experiments, we manipulated the frequencies of outcomes and trial types to determine the critical conditions for the effect, thereby providing novel tests of the reasoning processes that could contribute to it. The rare bias was substantially reduced when the outcomes were experienced at equal rates in the presence of predictive-cue frequency differences (Exp. 2), and when the predictive cues were experienced at equal rates in the presence of outcome frequency differences (Exp. 3). We also found a consistent common-outcome bias for novel cue compounds. The results indicate the importance of both cue and outcome frequencies to the inverse base-rate effect, and reveal a combination of necessary conditions that are not well captured by appealing to inferential strategies at test. Although both attention-based and inferential theories explain some aspects of these data, no existing theory fully accounts for these effects of relative novelty.

A Cognitive Model of Argument, With Application to the Base-Rate Phenomenon and Cognitive-Experiential Self-Theory

In the 1970s, Hample developed a successful model of intrapersonal argument. Loosely based on the law of total probability, the model used a normatively correct standard to predict people’s adherence to persuasive claims. That original research used single-item measures that could not be assessed for internal consistency. The present study estimates the reliabilities of the appropriate measures so that corrections for attenuation can be made. In addition, the study exploits the base-rate fallacy to encourage formation of bad premises and perhaps bad reasoning. Results show that Hample’s original model is indeed accurate and that his original results understated people’s rationality in persuasive situations. The base-rate effect was confined to improper premises; given people’s premises, they continued to reason rationally. The model was equally accurate for high experientials and high rationals, in Epstein’s terminology.

PH-dependence of organic matter solubility: Base type effects on dissolved organic C, N, P, and S in soils with contrasting mineralogy

Dissolved organic matter (DOM) is an important substrate for soil biological processes. Raising pH is known to cause release of DOM but less is known of the impact of different bases on the quantity and quality of DOM in soils. Three acid (pH5.3–5.7) soils of contrasting mineralogy were used to examine how the quantity and quality of DOM changes when soil pH is raised using lime (Ca(OH)2) or KOH (chemical analog of NH4OH, a product of urea hydrolysis). Dissolved organic carbon (DOC) increased as the base addition rate increased, with the response to KOH being up to 9 times larger than to Ca(OH)2. The proportion of carbohydrate-C (hexose- and pentose-C) in DOM did not show consistent effects of base rate or type and was similar for the three soils (35% of DOC, on average). Dissolved phenol-C increased as base addition rate increased. Only monomeric phenols were solubilized in Ca(OH)2-treated soils whereas polyphenols were solubilized in increasing amounts as the KOH rate was raised. Although the quantity of DOM differed considerably between treatments, its bioavailability (45% on average; measured using a 7-day bioassay at 20°C) was similar across treatments and soil types, with good correspondence between carbohydrate-C and bioavailable C. Large base type effects on organically bound nutrients (dissolved organic N, P, and S) were also observed (KOH≫Ca(OH)2). A distinguishing feature of DOM released by KOH was the presence of large amounts of complexed Al and Fe. We concluded that KOH (and, by extension, NH4OH) may render stable organic matter accessible to microbes by liberating it from the protective influence of soil minerals. Our results emphasize the importance of choosing an appropriate base when examining pH effects on DOM and associated biological processes.

Base Rates, Deception Detection, and Deception Theory: A Reply to Burgoon (2015)

This essay continues a debate about the relative scientific merits of the Park-Levine Model (PLM) and Interpersonal Deception Theory (IDT) with regard to accuracy in deception detection. Key points of disagreement include (a) the degree to which message recipients are sensitive to sender veracity and (b) the extent to which interactivity moderates the veracity and base-rate effects specified by PLM. According to PLM, people are truth-biased, truth-bias makes people insensitive to deception, and as a consequence, base rates affect accuracy regardless of interactivity. IDT, in direct contrast, holds that people are sensitive to the veracity of other's communication and that interactivity is a key moderator. Consistent with PLM, data repeatedly show that people are insensitive to other's veracity and that this insensitivity is general across media affordances including interactivity. This rejoinder uses Burgoon's (2015) own interactive data to demonstrate the empirical superiority of PLM over IDT.