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Research on reference points highlights how alternatives outside the choice set can alter the perceived value of available alternatives, arguably framing the choice scenario. The present work utilizes reference points to study the effects of framing in preferential choice, using the similarity and attraction context effects as performance measures. We specifically test the predictions of Multialternative Decision by Sampling (MDbS; Noguchi & Stewart, 2018), a recent preferential choice model that can account for both reference points and context effects. In Experiment 1, consistent with predictions by MDbS, we find a standard similarity effect when no reference point is given that increases when both dimensions are framed negatively and decreases when both dimensions are framed positively. Contrary to predictions by MDbS, when the two dimensions are framed as tradeoffs, participants prefer whichever alternative performs best in the negatively framed dimension. Performance of MDbS was improved by the addition of a frame-based global attention allocation mechanism. Experiment 2 extends these results to a "by-dimension" presentation format in an attempt to bring participant behavior in line with MDbS assumptions. The empirical and modeling results replicated those of Experiment 1. Experiment 3 used the attraction effect to test the effects of framing when the best-performing alternative on each dimension was identical across target conditions, therefore reducing the potential effects of a global attention allocation mechanism. The effects of framing were indeed greatly reduced, and the performance of MDbS was markedly improved. The results extend framing to the context effects literature, provide new benchmarks for models and theories of context effects, and point to the need for a global attention mechanism., (Copyright © 2020 Elsevier B.V. All rights reserved.)

A context effect is a change in preference that occurs when alternatives are added to a choice set. Models of preferential choice that account for context effects largely assume a within-dimension comparison process. It has been shown, however, that the format in which a choice set is presented can influence comparison strategies. That is, a by-alternative or by-dimension grouping of the dimension values encourage within-alternative or within-dimension comparisons, respectively. For example, one classic context effect, the compromise effect, is strengthened by a by-dimension presentation format. Extrapolation from this result suggests that a second context effect, the similarity effect, will actually reverse when stimuli are presented in a by-dimension format. In the current study, we presented participants with a series of apartment choice sets designed to elicit the similarity effect, with either a by-alternative or by-dimension presentation format. Participants in the by-alternative condition demonstrated a standard similarity effect; however, participants in the by-dimension condition demonstrated a strong reverse similarity effect. The present data can be accounted for by Multialternative Decision Field Theory (MDFT) and the Multiattribute Linear Ballistic Accumulator (MLBA), but not Elimination by Aspects (EBA). Indeed, when some weak assumptions of within-dimension processes are met, MDFT and the MLBA predict the reverse similarity effect. These modeling results suggest that the similarity effect is governed by either forgetting and inhibition (MDFT), or attention to positive or negative differences (MLBA). These results demonstrate that flexibility in the comparison process needs to be incorporated into theories of preferential choice., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The first aim of this research is to compare computational models of multi-alternative, multi-attribute choice when attribute values are explicit. The choice predictions of utility (standard random utility & weighted valuation), heuristic (elimination-by-aspects, lexicographic, & maximum attribute value), and dynamic (multi-alternative decision field theory, MDFT, & a version of the multi-attribute linear ballistic accumulator, MLBA) models are contrasted on both preferential and risky choice data. Using both maximum likelihood and cross-validation fit measures on choice data, the utility and dynamic models are preferred over the heuristic models for risky choice, with a slight overall advantage for the MLBA for preferential choice. The response time predictions of these models (except the MDFT) are then tested. Although the MLBA accurately predicts response time distributions, it only weakly accounts for stimulus-level differences. The other models completely fail to account for stimulus-level differences. Process tracing measures, i.e., eye and mouse tracking, were also collected. None of the qualitative predictions of the models are completely supported by that data. These results suggest that the models may not appropriately represent the interaction of attention and preference formation. To overcome this potential shortcoming, the second aim of this research is to test preference-formation assumptions, independently of attention, by developing the models of attentional sampling (MAS) model family which incorporates the empirical gaze patterns into a sequential sampling framework. An MAS variant that includes attribute values, but only updates the currently viewed alternative and does not contrast values across alternatives, performs well in both experiments. Overall, the results support the dynamic models, but point to the need to incorporate a framework that more accurately reflects the relationship between attention and the preference-formation process., (Copyright © 2017 Elsevier Inc. All rights reserved.)

It is well known that people tend to perform poorly when asked to determine a posterior probability on the basis of a base rate, true positive rate, and false positive rate. The present experiments assessed the extent to which individual participants nevertheless adopt consistent strategies in these Bayesian reasoning problems, and investigated the nature of these strategies. In two experiments, one laboratory-based and one internet-based, each participant completed 36 problems with factorially manipulated probabilities. Many participants applied consistent strategies involving use of only one of the three probabilities provided in the problem, or additive combination of two of the probabilities. There was, however, substantial variability across participants in which probabilities were taken into account. In the laboratory experiment, participants' eye movements were tracked as they read the problems. There was evidence of a relationship between information use and attention to a source of information. Participants' self-assessments of their performance, however, revealed little confidence that the strategies they applied were actually correct. These results suggest that the hypothesis of base rate neglect actually underestimates people's difficulty with Bayesian reasoning, but also suggest that participants are aware of their ignorance., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Depression and cigarette smoking co-occur at high rates. However, the etiological mechanisms that contribute to this relationship remain unclear. Anhedonia and associated impairments in reward learning are key features of depression, which also have been linked to the onset and maintenance of cigarette smoking. However, few studies have investigated differences in anhedonia and reward learning among depressed smokers and depressed nonsmokers. The goal of this study was to examine putative differences in anhedonia and reward learning in depressed smokers (n=36) and depressed nonsmokers (n=44). To this end, participants completed self-report measures of anhedonia and behavioral activation (BAS reward responsiveness scores) and as well as a probabilistic reward task rooted in signal detection theory, which measures reward learning (Pizzagalli, Jahn, & O'Shea, 2005). When considering self-report measures, depressed smokers reported higher trait anhedonia and reduced BAS reward responsiveness scores compared to depressed nonsmokers. In contrast to self-report measures, nicotine-satiated depressed smokers demonstrated greater acquisition of reward-based learning compared to depressed nonsmokers as indexed by the probabilistic reward task. Findings may point to a potential mechanism underlying the frequent co-occurrence of smoking and depression. These results highlight the importance of continued investigation of the role of anhedonia and reward system functioning in the co-occurrence of depression and nicotine abuse. Results also may support the use of treatments targeting reward learning (e.g., behavioral activation) to enhance smoking cessation among individuals with depression., (Copyright © 2014. Published by Elsevier Ltd.)

Precise determination of a noisy biological oscillator's period from limited experimental data can be challenging. The common practice is to calculate a single number (a point estimate) for the period of a particular time course. Uncertainty is inherent in any statistical estimator applied to noisy data, so our confidence in such point estimates depends on the quality and quantity of the data. Ideally, a period estimation method should both produce an accurate point estimate of the period and measure the uncertainty in that point estimate. A variety of period estimation methods are known, but few assess the uncertainty of the estimates, and a measure of uncertainty is rarely reported in the experimental literature. We compare the accuracy of point estimates using six common methods, only one of which can also produce uncertainty measures. We then illustrate the advantages of a new Bayesian method for estimating period, which outperforms the other six methods in accuracy of point estimates for simulated data and also provides a measure of uncertainty. We apply this method to analyze circadian oscillations of gene expression in individual mouse fibroblast cells and compute the number of cells and sampling duration required to reduce the uncertainty in period estimates to a desired level. This analysis indicates that, due to the stochastic variability of noisy intracellular oscillators, achieving a narrow margin of error can require an impractically large number of cells. In addition, we use a hierarchical model to determine the distribution of intrinsic cell periods, thereby separating the variability due to stochastic gene expression within each cell from the variability in period across the population of cells., (Copyright © 2012 Elsevier Ltd. All rights reserved.)