Cue Values Research Articles

Children adapt their information search to goal and statistical structure of a problem.

To be efficient, problem solvers need to be adaptive, tailoring their information search to the specific problem at hand. Across two studies, we investigated the emergence and early development of children's ability to adapt their information search to a given goal (Studies 1 and 2) and to the statistical structure of the problem space (Study 2) to maximize effectiveness. In Study 1, 3-6-year-olds (n = 105) decided which of two cues to look up, the arms or the legs of two monsters, to predict the winner of a throwing or jumping challenge, knowing that monsters with long arms were good throwers and those with long legs were good jumpers. Children's ability to adaptively select relevant information and tailor their search to the goal increased with age, surpassing chance level between the ages of 4 and 5. Study 2 (7-14-year-olds and adults, n = 175) demonstrated this competence in a more complex task, additionally investigating whether children tailor their search to the statistical structure, that is, the distribution of cue values, in their search environment (e.g., how common long legs are). The results suggest high reliability in ignoring irrelevant cues (confirming the results from Study 1) and developmental patterns in children's preferential treatment of cues of differing statistical frequency. Together, these studies contribute to the literature on information search adaptiveness by tracing for the first time the emergence and developmental trajectory of children's ability to tailor predecisional search to the changing goals and environmental resources of the problem at hand. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Methadone maintenance treatment alters couplings of default mode and salience networks in individuals with heroin use disorder: A longitudinal self-controlled resting-state fMRI study.

Methadone maintenance treatment (MMT) is a common treatment for heroin use disorder (HUD). Although individuals with HUD have been reported to show impaired coupling among the salience network (SN), executive control network (ECN), and default mode network (DMN), the effects of MMT on the coupling among three large-scale networks in individuals with HUD remains unclear. Thirty-seven individuals with HUD undergoing MMT and 57 healthy controls were recruited. The longitudinal one-year follow-up study aimed to evaluate the effects of methadone on anxiety, depression, withdrawal symptoms and craving and number of relapse, and brain function (SN, DMN and bilateral ECN) in relation to heroin dependence. The changes in psychological characteristics and the coupling among large-scale networks after 1 year of MMT were analyzed. The associations between the changes in coupling among large-scale networks and psychological characteristics and the methadone dose were also examined. After 1 year of MMT, individuals with HUD showed a reduction in the withdrawal symptom score. The number of relapses was negatively correlated with the methadone dose over 1 year. The functional connectivity between the medial prefrontal cortex (mPFC) and the left middle temporal gyrus (MTG; both key nodes of the DMN) was increased, and the connectivities between the mPFC and the anterior insular and middle frontal gyrus (key nodes of the SN) were also increased. The mPFC-left MTG connectivity was negatively correlated with the withdrawal symptom score. Long-term MMT enhanced the connectivity within the DMN which might be related to reduced withdrawal symptoms, and that between the DMN and SN which might be related to increase in salience values of heroin cues in individuals with HUD. Long-term MMT may be a double-edged sword in treatment for HUD.

The predictors of COVID-19 preventive health behaviors among adolescents: the role of health belief model and health literacy.

The present study aimed to evaluate the predictive role of the Health Belief Model (HBM) constructs and health literacy (HL) in shaping the coronavirus disease 2019 (COVID-19) preventive health behaviors (PHBs) among adolescents. This cross-sectional study was conducted with 503 adolescent girls and boys, randomly selected via cluster sampling. For this purpose, the data were collected online through four research tools, including the demographic-clinical characteristics information questionnaire, the COVID-19 PHB Scale based on HBM, and the Health Literacy Scale for COVID-19. The data analysis was then performed by regression analysis along with the structural equation modeling (SEM), considering the significance level of 0.05. The regression analysis results demonstrated that following the one-unit increase in the values of self-efficacy and cues to action, the COVID-19 PHBs elevated by 0.063 and 0.078 units, respectively. In addition, the COVID-19 PHBs subsided by 0.018 with the rise in the value of perceived barriers (P < 0.001). According to the path analysis, the direct path from the COVID-19-related HL to the COVID-19 PHBs (B = 0.097, β = 0.087, 95% confidence interval [CI] = 0.005 to 0.189) was significant. Furthermore, the indirect path from the COVID-19-related HL to the COVID-19 PHBs through perceived susceptibility (B = 0.017, β = 0.015, 95% CI = 0.001 to 0.032), perceived barriers (B = 0.029, β = 0.026, 95% CI = 0.004 to 0.055), self-efficacy (B = 0.094, β = 0.084, 95% CI = 0.031 to 0.156), and cues to action (B = 0.153, β = 0.137, 95% CI = 0.092 to 0.215) was significant. In keeping with the study results, it is essential to take some effective measures to boost the HBM constructs and HL among adolescents to improve their PHBs during pandemics such as COVID-19.

Bright Voice Quality and Fundamental Frequency Variation in Non-binary Speakers

1) To investigate if vocal variation produced by assigned-female-at-birth (AFAB) non-binary people differed from vocal variation produced by cisgender (cis) participants. Cue values produced by non-binary participants were predicted to differ from those values produced by cisgender participants. 2) To determine if previous subjective assessments of bright voice quality in AFAB non-binary participants were quantifiable, and if so, if non-binary and cisgender participants differed in their voice quality production. A quantitative comparative research design. Phonetic and statistical analyses of continuous speech samples produced by AFAB non-binary and cisgender participants. Vocal cues were mean fundamental frequency (F0) and bright voice quality, measured by cepstral peak prominence-smoothed and spectral slope, with speaker gender as the predictor. At the group level, non-binary participants produced intermediate F0 values - significantly lower than the cis women's and significantly higher than the cis men's. Individually, the majority of non-binary participants produced mean F0 in this intermediate range. Non-binary participants produced significantly less negative spectral slope and higher cepstral peak prominence-smoothed, indicative of a brighter, more resonant voice quality. Individual-level results indicated that vocal training and vocal tract physiology did not fully account for the results found. Participants' agency, particularly their motivation to alter vocal output to avoid being misgendered, has an effect on the AFAB non-binary participants' F0 production and potentially their voice quality. The majority of AFAB non-binary participants uniquely produced the cue combination of intermediate F0 and bright voice quality.

Supporting Health and Medical Decision Making: Findings and Insights from Fuzzy-Trace Theory.

Fuzzy-trace theory (FTT) supports practical approaches to improving health and medicine.FTT differs in important respects from other theories of decision making, which has implications for how to help patients, providers, and health communicators.Gist mental representations emphasize categorical distinctions, reflect understanding in context, and help cue values relevant to health and patient care.Understanding the science behind theory is crucial for evidence-based medicine.

An Interactive Approach to Emotional Expression Through Musical Cues

Previous literature suggests that structural and expressive cues affect the emotion expressed in music. However, only a few systematic explorations of cues have been done, usually focussing on a few cues or a limited amount of predetermined arbitrary cue values. This paper presents three experiments investigating the effect of six cues and their combinations on the music's perceived emotional expression. Twenty-eight musical pieces were created with the aim of providing flexible, ecologically valid, unfamiliar, new stimuli. In Experiment 1, 96 participants assessed which emotions were expressed in the pieces using Likert scale ratings. In Experiment 2, a subset of the stimuli was modified by participants (N = 42) via six available cues (tempo, mode, articulation, pitch, dynamics, and brightness) to convey seven emotions (anger, sadness, fear, joy, surprise, calmness, and power), addressing the main aim of exploring the impact of cue levels to expressions. Experiment 3 investigated how well the variations of the original stimuli created by participants in Experiment 2 expressed their intended emotion. Participants (N = 91) rated them alongside the seven original pieces, allowing the exploration of similarities and differences between the two sets of related pieces. An overall pattern of cue combinations was identified for each emotion. Some findings corroborate previous studies: mode and tempo were the most impactful cues in shaping emotions, and sadness and joy were amongst the most accurately recognised emotions. Novel findings include soft dynamics being used to convey anger, and dynamics and brightness being the least informative cues. These findings provide further motivation to investigate the effect of cues on emotions in music as combinations of multiple cues rather than as individual cues, as one cue might not give enough information to portray a specific emotion. The new findings and discrepancies are discussed in relation to current theories of music and emotions.

Remote measurement of spectral weighting in sound localization using virtual reality headsets

Unequal weighting of binaural information across frequency can reduce sensitivity in the presence of competing but uninformative cues (“binaural interference”), a potentially serious problem for listeners who use combined electric and acoustic (EAS) hearing. Here, we used virtual-reality techniques to measure spectral weighting functions (SWF) during localization of simulated EAS stimuli [see van Ginkel et al., JASA 145, 2445 (2019)]: low-frequency “acoustic” noise bands and high-frequency “electric” click trains. This study was conducted remotely at each participants’ location, using sanitized and calibrated research equipment (Oculus Quest 2 headsets and Sennheiser HD 280 Pro headphones) delivered by lab personnel. Sounds were presented with random binaural-cue jitter across seven components on each trial. SWFs were computed by statistical regression of localization responses onto binaural cue values. SWFs confirmed the dominance of 400–1000 Hz components reported previously [Stecker, Folkerts, &amp; Stecker 2019(A), JASA 145:1720]. Introducing a gap between noise and click-train components increased weight on neighboring components, consistent with reduced interference in such conditions [van Ginkel et al., 2019]. Finally, presenting noises and click trains from competing locations reduced weight on non-target components for most but not all participants, a potential marker of susceptibility to binaural interference. [Work supported by NIH R01-DC016643, NIH T35-008757.]

How is the hypothesis space represented? Evidence from young children's active search and predictions in a multiple-cue inference task.

To successfully navigate an uncertain world, one has to learn the relationship between cues (e.g., wind speed, atmospheric pressure) and outcomes (e.g., rain). When learning, it is possible to actively manipulate the cue values to test hypotheses about this relationship directly. Across two studies, we investigated how 5- to 7-year-olds actively learned cue-outcome relationships, and what their behavior revealed about how they represented the hypothesis space. Children learned how two cues (color and shape) predicted some monsters' relative speed, by selecting which monster pairs to see racing. We compared two computational models in their ability to capture children's behavior: a cue-abstraction model, which organizes the hypothesis space based on abstracted cue-outcome relationships, and a permutation-based model, which represents the hypothesis space based on the relative speed of individual monsters. The results of Study 1 (26 five-year-olds, 14 female and 25 six-year-olds, 15 female; predominantly White, fluent in English) provided the first evidence that 5- and 6-year-olds can use cue-abstraction hypothesis space representations when provided with scaffolding. However, Study 2 (65 five-year-olds, 33 female; 67 six-year-olds, 33 female; 68 seven-year-olds, 33 female; predominantly White, fluent in German) showed that young children were best described by the permutation-based model, and that only 7-year-olds, when provided with memory aids, were best captured by the cue-abstraction model. Overall, our results highlight the guiding role of the hypothesis space for active search and learning, suggesting that these two phases might trigger different representations, and indicating a developmental shift in how children represent the hypothesis space. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Estimation of Biomass Increase and CUE at a Young Temperate Scots Pine Stand Concerning Drought Occurrence by Combining Eddy Covariance and Biometric Methods

The accurate estimation of an increase in forest stand biomass has remained a challenge. Traditionally, in situ measurements are done by inventorying a number of trees and their biometric parameters such as diameter at the breast height (DBH) and height; sometimes these are complemented by carbon (C) content studies. Here we present the estimation of net primary productivity (NPP) over a two years period (2019–2020) at a 25-year-old Scots pine stand. Research was based on allometric equations made by direct biomass analysis (tree extraction) and carbon content estimations in individual components of sampled trees, combined with a series of stem diameter increments recorded by a network of band dendrometers. Site-specific allometric equations were obtained using two different approaches: using the whole tree biomass vs DBH (M1), and total dry biomass-derived as a sum of the results from individual tree components’ biomass vs DBH (M2). Moreover, equations for similar forest stands from the literature were used for comparison. Gross primary productivity (GPP) estimated from the eddy-covariance measurements allowed the calculation of carbon use efficiency (CUE = NPP/GPP). The two investigated years differed in terms of the sum and patterns of precipitation distribution, with a moderately dry year of 2019 that followed the extremely dry 2018, and the relatively average year of 2020. As expected, a higher increase in biomass was recorded in 2020 compared to 2019, as determined by both allometric equations based on in situ and literature data. For the former approach, annual NPP estimates reached ca. 2.0–2.1 t C ha−1 in 2019 and 2.6–2.7 t C ha−1 in 2020 depending on the “in situ equations” (M1-M2) used, while literature-derived equations for the same site resulted in NPP values ca. 20–30% lower. CUE was higher in 2020, which resulted from a higher NPP total than in 2019, with lower summer and spring GPP in 2020. However, the CUE values were lower than those reported in the literature for comparable temperate forest stands. A thorough analysis of the low CUE value would require a full interpretation of interrelated physiological responses to extreme conditions.

Non-climatic component provoked substantial spatiotemporal changes of carbon and water use efficiency on the Mongolian Plateau

Understanding the joint impact of anthropologic and climatic changes on ecosystem function and dynamics is among the frontiers in global environmental change studies. Carbon and water balances are especially crucial to the sustainable ecosystem and functional returns in sensitive regions such as the Mongolian Plateau. In this study, the significance of non-climatic component (NCC) on carbon and water use efficiency (CUE and WUE) is quantified among the ecosystem types on the Mongolian Plateau. We mapped the spatial gradients of carbon/water balance and delineated the hotspots of NCC-driven CUE and WUE for 2000–2013 using gross and net primary production (GPP and NPP) and evapotranspiration (ET) products derived from the MODIS databases. Significantly higher CUE and WUE values were found in Mongolia (MG) than in Inner Mongolia (IM) due to both climatic forcing (CF) and NCC. NCC was found to dominate the changes in CUE and WUE in the steppes on the plateau by over 16% and 22%, respectively, but with spatially uneven distributions. NCC-driven WUE values were much higher than those driven by CF. The hotspots for NCC-driven CUE did not overlap with those of WUE, with CUE hotspots concentrated in the east of MG and northeast of IM; WUE hotspots were found in the central and Khangai regions of MG and eastern regions of IM. The NCC-driven CUE area in MG was from population growth and the industrial shares in gross domestic product, while the NCC-driven WUE area was due to livestock growth in MG but driven by the growth of cultivated lands in IM. In sum, we conclude that NCC provoked substantial spatiotemporal changes on carbon and water use. CF and NCC effects on carbon and water balance varied in space, by ecosystem type, and between the two political entities.

Binaural spatial adaptation as a mechanism for asymmetric trading of interaural time and level differences.

A classic paradigm used to quantify the perceptual weighting of binaural spatial cues requires a listener to adjust the value of one cue, while the complementary cue is held constant. Adjustments are made until the auditory percept appears centered in the head, and the values of both cues are recorded as a trading relation (TR), most commonly in μs interaural time difference per dB interaural level difference. Interestingly, existing literature has shown that TRs differ according to the cue being adjusted. The current study investigated whether cue-specific adaptation, which might arise due to the continuous, alternating presentation of signals during adjustment tasks, could account for this poorly understood phenomenon. Three experiments measured TRs via adjustment and via lateralization of single targets in virtual reality (VR). Targets were 500 Hz pure tones preceded by silence or by adapting trains that held one of the cues constant. VR removed visual anchors and provided an intuitive response technique during lateralization. The pattern of results suggests that adaptation can account for cue-dependent TRs. In addition, VR seems to be a viable tool for psychophysical tasks.

A graph-theoretic approach to identifying acoustic cues for speech sound categorization

Human speech contains a wide variety of acoustic cues that listeners must map onto distinct phoneme categories. The large amount of information contained in these cues contributes to listeners' remarkable ability to accurately recognize speech across a variety of contexts. However, these cues vary across talkers, both in terms of how specific cue values map onto different phonemes and in terms of which cues individual talkers use most consistently to signal specific phonological contrasts. This creates a challenge for models that aim to characterize the information used to recognize speech. How do we balance the need to account for variability in speech sounds across a wide range of talkers with the need to avoid overspecifying which acoustic cues describe the mapping from speech sounds onto phonological distinctions? We present an approach using tools from graph theory that addresses this issue by creating networks describing connections between individual talkers and acoustic cues and by identifying subgraphs within these networks. This allows us to reduce the space of possible acoustic cues that signal a given phoneme to a subset that still accounts for variability across talkers, simplifying the model and providing insights into which cues are most relevant for specific phonemes. Classifiers trained on the subset of cue dimensions identified in the subgraphs provide fits to listeners' categorization that are similar to those obtained for classifiers trained on all cue dimensions, demonstrating that the subgraphs capture the cues necessary to categorize speech sounds.

Threat Prediction from Schemas as a Source of Bias in Pain Perception.

Our sensory impressions of pain are generally thought to represent the noxious properties of an agent but can be influenced by the predicted level of threat. Predictions can be sourced from higher-order cognitive processes, such as schemas, but the extent to which schemas can influence pain perception relative to bottom-up sensory inputs and the underlying neural underpinnings of such a phenomenon are unclear. Here, we investigate how threat predictions generated from learning a cognitive schema lead to inaccurate sensory impressions of the pain stimulus. Healthy male and female participants first detected a linear association between cue values and stimulus intensity and rated pain to reflect the linear schema when compared with uncued heat stimuli. The effect of bias on pain ratings was reduced when prediction errors (PEs) increased, but pain perception was only partially updated when measured against stepped increases in PEs. Cognitive, striatal, and sensory regions graded their responses to changes in predicted threat despite the PEs (p < 0.05, corrected). Individuals with more catastrophic thinking about pain and with low mindfulness were significantly more reliant on the schema than on the sensory evidence from the pain stimulus. These behavioral differences mapped to variability in responses of the striatum and ventromedial prefrontal cortex. Thus, this study demonstrates a significant role of higher-order schemas in pain perception and indicates that pain perception is biased more toward predictions and less toward nociceptive inputs in individuals who report less mindfulness and more fear of pain.SIGNIFICANCE STATEMENT This study demonstrates that threat predictions generated from cognitive schemas continue to influence pain perception despite increasing prediction errors arising in pain pathways. Individuals first formed a cognitive schema of linearity in the relationship between the cued threat value and the stimulus intensity. Subsequently, the linearity was reduced gradually, and participants partially updated their evaluations of pain in relation to the stepped increases in prediction errors. Individuals who continued to rate pain based more on the predicted threat than on changes in nociceptive inputs reported high pain catastrophizing and less mindful-awareness scores. These two affects mapped to activity in the ventral and dorsal striatum, respectively. These findings direct us to a significant role of top-down processes in pain perception.

Cue integration in metamemory judgements is strategic

People base judgements about their own memory processes on probabilistic cues such as the characteristics of study materials and study conditions. While research has largely focused on how single cues affect metamemory judgements, a recent study by Undorf, Söllner, and Bröder found that multiple cues affected people's predictions of their future memory performance (judgements of learning, JOLs). The present research tested whether this finding was indeed due to strategic integration of multiple cues in JOLs or, alternatively, resulted from people's reliance on a single unified feeling of ease. In Experiments 1 and 2, we simultaneously varied concreteness and emotionality of word pairs and solicited (a) pre-study JOLs that could be based only on the manipulated cues and (b) immediate JOLs that could be based both on the manipulated cues and on a feeling of ease. The results revealed similar amounts of cue integration in pre-study JOLs and immediate JOLs, regardless of whether cues varied in two easily distinguishable levels (Experiment 1) or on a continuum (Experiment 2). This suggested that people strategically integrated multiple cues in their immediate JOLs. Experiment 3 provided further evidence for this conclusion by showing that false explicit information about cue values affected immediate JOLs over and above actual cue values. Hence, we conclude that cue integration in JOLs involves strategic processes.

Neurons in primary auditory cortex represent sound source location in a cue-invariant manner

Auditory cortex is required for sound localisation, but how neural firing in auditory cortex underlies our perception of sound sources in space remains unclear. Specifically, whether neurons in auditory cortex represent spatial cues or an integrated representation of auditory space across cues is not known. Here, we measured the spatial receptive fields of neurons in primary auditory cortex (A1) while ferrets performed a relative localisation task. Manipulating the availability of binaural and spectral localisation cues had little impact on ferrets’ performance, or on neural spatial tuning. A subpopulation of neurons encoded spatial position consistently across localisation cue type. Furthermore, neural firing pattern decoders outperformed two-channel model decoders using population activity. Together, these observations suggest that A1 encodes the location of sound sources, as opposed to spatial cue values.

A unifying computational model of decision making.

Decision making has long been of interest as a descriptive phenomenon in psychology and as a generative one in artificial intelligence. Research ranges from general, descriptive models of heuristic decision making to detailed studies of decision parameters. This paper introduces a model that formalizes and integrates several descriptive models so that it can serve both as a framework for psychological models and as an algorithm for computational decision making. We set special focus on the instantiation of this model with respect to aggregating cue values by reviewing some methods from the field of computational social choice. To show its applicability in the context of artificial intelligence we present a case study of computational problem solving.

Heuristic Information Acquisition and Restriction Rules for Decision Support

The research question addressed by this study was: What information should be presented to or hidden from decision makers in order to facilitate high performance in decision tasks? Previous research on information search is limited because of its focus on analytic information acquisition methods; analytic because of the focus on maximizing expected utility; acquisition because of the focus on what information should be added or searched for. Implementing these methods requires reliable assessments of probabilities, cue weights, and cue values and does not provide suggestions on how to restrict or remove information. In this work, we present four heuristics, or simple rules, for acquiring and restricting information that only require an understanding of the distribution of known and unknown information (information imbalance and complete attribute pairs). The rules were tested on a range of analytic and heuristic decision strategies within two-option decision tasks across 15 real-world environments. Though the rules are transparent and easy to communicate (create a balance of information between options and within cues) and require little information to perform, the simulation results show that the rules were generally effective across all environments. For almost every combination of rule and strategy, the heuristic restriction rules were shown to be more likely to increase rather than decrease accuracy. In every combination, the heuristic acquisition rules were shown to increase accuracy more than acquiring information that did not adhere to the rules. Further statistical and mathematical analysis showed that rules are mediated by strategies’ full information accuracy and estimates of missing information.

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

Real-life decision-making often involves combining multiple probabilistic sources of information under finite time and cognitive resources. To mitigate these pressures, people “satisfice”, foregoing a full evaluation of all available evidence to focus on a subset of cues that allow for fast and “good-enough” decisions. Although this form of decision-making likely mediates many of our everyday choices, very little is known about the way in which the neural encoding of cue information changes when we satisfice under time pressure. Here, we combined human functional magnetic resonance imaging (fMRI) with a probabilistic classification task to characterize neural substrates of multi-cue decision-making under low (1500 ms) and high (500 ms) time pressure. Using variational Bayesian inference, we analyzed participants’ choices to track and quantify cue usage under each experimental condition, which was then applied to model the fMRI data. Under low time pressure, participants performed near-optimally, appropriately integrating all available cues to guide choices. Both cortical (prefrontal and parietal cortex) and subcortical (hippocampal and striatal) regions encoded individual cue weights, and activity linearly tracked trial-by-trial variations in the amount of evidence and decision uncertainty. Under increased time pressure, participants adaptively shifted to using a satisficing strategy by discounting the least informative cue in their decision process. This strategic change in decision-making was associated with an increased involvement of the dopaminergic midbrain, striatum, thalamus, and cerebellum in representing and integrating cue values. We conclude that satisficing the probabilistic inference process under time pressure leads to a cortical-to-subcortical shift in the neural drivers of decisions.

Piece-rates and tournaments: Implications for learning in a cognitively challenging task

We compare the impact of piece-rate and tournament payment schemes on learning in a cognitively challenging task. In each one of multiple rounds, subjects are shown two cue values, Cue A and Cue B, and asked to predict the value of a third variable X, which is a noisy function of the two cue values. The subjects’ aim is to predict the value of X as accurately as possible. Our metric of performance is the absolute error, i.e., the absolute distance between the actual and predicted values of X. We implement four treatments which are based on two different payment schemes: (1) piece rates, where subjects are paid linearly on the basis of their own absolute errors and (2) a two-person winner-take-all-tournament, where subjects are paired and the one with a smaller absolute error earns a fixed payoff, while the other earns nothing. We find that it is only in the tournament payment scheme, and particularly in a more complex version of the task, that subjects show significant evidence of learning over time, in that their predictions get closer to the actual value of X. This learning process is driven by the all-or-nothing nature of the payoff structure in tournaments.

Do People Order Cues by Retrieval Fluency when Making Probabilistic Inferences?

AbstractIn multiple‐cue probabilistic inferences, people infer alternatives' unknown values on decision criteria, using alternatives' attributes as cues. Some inferential strategies, like take‐the‐best, assume that people consider relevant cues sequentially in order of decreasing validity. This assumption has been deemed cognitively implausible by some, who suggest memory retrieval principles to guide cue order. We test whether memory‐based inferences are better described by a model considering cues in order of validity or in order of memory retrieval. In an experiment, we manipulated the frequency with which cues appeared in a learning phase, increasing retrieval fluency of cue values related to the more frequently appearing cue. In a subsequent decision phase, participants made a series of two‐alternative decisions based on the learned cue values. We compared two sequential sampling models, which differed in whether cues are sampled in order of subjective cue validity or in order of retrieval fluency. To model retrieval order of cues in the fluency sampling model, we used the declarative memory theory embedded in the ACT‐R cognitive architecture. Most participants' decisions were best described by the model sampling cues in order of memory retrieval. Only a minority of participants were classified as sampling cues by validity. Our result suggests that retrieval fluency is the primary driver of cue order in inferences from memory, irrespective of the cues' validities. Copyright © 2017 John Wiley &amp; Sons, Ltd.