Why contextual preference reversals maximize expected value

A robust Bayesian test for identifying context effects in multiattribute decision-making

Research on multiattribute decision-making has repeatedly shown that people's preferences for options depend on the set of other options they are presented with, that is, the choice context. As a result, recent years have seen the development of a number of psychological theories explaining context effects. However, much less attention has been given to the statistical analyses of context effects. Traditionally, context effects are measured as a change in preference for a target option across two different choice sets (the so-called relative choice share of the target, or RST). We first show that the frequently used definition of the RST measure has some weaknesses and should be replaced by a more appropriate definition that we provide. We then show through a large-scale simulation that the RST measure as previously defined can lead to biased inferences. As an alternative, we suggest a Bayesian approach to estimating an accurate RST measure that is robust to various circumstances. We applied the two approaches to the data of five published studies (total participants, N = 738), some of which used the biased approach. Additionally, we introduce the absolute choice share of the target (or AST) as the appropriate measure for the attraction effect. Our approach is an example of evaluating and proposing proper statistical tests for axiomatic principles of decision-making. After applying the AST and the robust RST to published studies, we found qualitatively different results in at least one-fourth of the cases. These results highlight the importance of utilizing robust statistical tests as a foundation for the development of new psychological theories.

The impact of choice discriminability and outcome valence on visual decision making under risk

Much of human activity involves perceptual or perceptuo-motor choice between options with uncertain outcomes. Previous research suggests that decisions in these contexts can be near-optimal in some circumstances but can also be significantly biased. Here we investigate how biases might depend on: i) discriminability of available choice outcomes, adjusted by manipulating the Expected Value (EV) function curvature; ii) outcome valence, which changes the tendency for risk seeking/aversive behaviour in cognitive decision making. In three experiments, participants set the size of a catcher in order to catch a dot moving on a random walk (with varying levels of predictability) after it emerged from behind an occluder. Catching and missing the dot were associated with scoring a variable number of outcome points depending on catcher size. In experiment 1 outcomes were most discriminable (high EV curvature) and catcher size settings were near-optimal. In experiments 2 and 3 outcomes were harder to discriminate (low EV curvature) and there was a significant bias to set the catcher size too small. Unlike cognitive decision making, the valence manipulation had little effect. Subsequent analyses suggest observed biases might reflect participants moving settings towards the region with highest EV curvature, where feedback is most informative. These data suggest that: i) unlike cognitive decisions, in this task choices are largely insensitive to outcome valence; ii) EV curvature is potentially an important factor when interpreting performance in such tasks; iii) Choice may be biased towards high EV curvature regions, consistent with value being placed on exploration to increase information return.

Establishing the laws of preferential choice behavior

Mathematical and computational decision models are powerful tools for studying choice behavior, and hundreds of distinct decision models have been proposed over the long interdisciplinary history of decision making research. The existence of so many models has led to theoretical fragmentation and redundancy, obscuring key insights into choice behavior, and preventing consensus about the essential properties of preferential choice. We provide a synthesis of formal models of risky, multiattribute, and intertemporal choice, three important domains in decision making. We identify recurring insights discovered by scholars of different generations and different disciplines across these three domains, and use these insights to classify over 150 existing models as involving various combinations of eight key mathematical and computational properties. These properties capture the main avenues of theoretical development in decision making research and can be used to understand the similarities and differences between decision models, aiding both theoretical analyses and empirical tests.

A rational reinterpretation of dual-process theories

Highly influential "dual-process" accounts of human cognition postulate the coexistence of a slow accurate system with a fast error-prone system. But why would there be just two systems rather than, say, one or 93? Here, we argue that a dual-process architecture might reflect a rational tradeoff between the cognitive flexibility afforded by multiple systems and the time and effort required to choose between them. We investigate what the optimal set and number of cognitive systems would be depending on the structure of the environment. We find that the optimal number of systems depends on the variability of the environment and the difficulty of deciding when which system should be used. Furthermore, we find that there is a plausible range of conditions under which it is optimal to be equipped with a fast system that performs no deliberation ("System 1") and a slow system that achieves a higher expected accuracy through deliberation ("System 2"). Our findings thereby suggest a rational reinterpretation of dual-process theories.

Heuristics contribute to sensorimotor decision-making under risk

Research in psychophysics argues that incentivized sensorimotor decisions (such as deciding where to reach to get a reward) maximize expected gain, suggesting that these decisions may be impervious to cognitive biases and heuristics. We tested this hypothesis in two experiments, directly comparing the predictive accuracy of an optimal model and plausible suboptimal models. We obtained strong evidence that people deviated from the optimal strategy by excessively avoiding loss regions when the potential loss was zero and failing to shift far enough away from loss regions when potential losses outweighed the potential gains. Although allowing nonlinear distortions of value and probability information improved the fit of value-maximizing models, behavior was best described by a model encapsulating a simple heuristic strategy. This suggests that visuomotor decisions are likely influenced by biases and heuristics observed in more classical economic decision-making tasks.

The evolutionary origin of Bayesian heuristics and finite memory

Bayes' rule is a fundamental principle that has been applied across multiple disciplines. However, few studies have addressed its origin as a cognitive strategy or the underlying basis for generalization from a small sample. Using a simple binary choice model subject to natural selection, we derive Bayesian inference as an adaptive behavior under certain stochastic environments. Such behavior emerges purely through the forces of evolution, despite the fact that our population consists of mindless individuals without any ability to reason, act strategically, or accurately encode or infer environmental states probabilistically. In addition, three specific environments favor the emergence of finite memory—those that are Markov, nonstationary, and environments where sampling contains too little or too much information about local conditions. These results provide an explanation for several known phenomena in human cognition, including deviations from the optimal Bayesian strategy and finite memory beyond resource constraints.

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Evolution leads to behaviors that resemble Bayesian inference

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Finite memory arises in nonstationary environments beyond resource constraints

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Intelligence is what advances fitness from the evolutionary lens

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This may provide insights for the quest toward artificial general intelligence

Professional discrepancies of doctors and lawyers in episodic memory: Modulations of professional morality and warning

Past investigations have consistently demonstrated the robust stereotype-consistent effect in the circumstance of source memory but not always in item memory, including the case of professional stereotype. However, it remains unclear whether the effect still occurs in professional stereotype when considering the attributes of negative (or bad) or positive (or good); besides, it has not been concerned about how does warning work in remembering the professional stereotypical stimuli. The current experiments aimed to address these issues by adopting descriptive sentences as stimuli, which were related or unrelated to doctors and lawyers, and with different professional moral valences (negative, neutral, or positive). Item memory and source memory were tested successively. Experiment 1 without the explicit warning confirmed the reliable stereotype-consistent effect solely in source memory; the modulation of professional morality on memory behaved differently between doctor and lawyer, that is, negativity bias versus positivity bias. When giving an explicit warning (Experiment 2), the stereotype-consistent effect attenuated in the lawyer case, and the occurrence of negativity bias was sensitive to the memory task. Thus, our findings further reinforce the dual-process model; both professional morality and warning work in memory of professional stereotype, depending upon the nature of the profession, the concerned memory task, and also the presence of warning. Implications are made for future research to consider more perspectives.

Similarity, Attraction, and Compromise Effects: Original Findings, Recent Empirical Observations, and Computational Cognitive Process Models

Preference reversals—a decision maker prefers A over B in one situation but B over A in another—demonstrate that human behavior violates invariance assumptions of (utility-based) rational choice theories. In the field of multi-alternative multi-attribute decision-making research, 3 preference reversals received special attention: similarity, attraction, and compromise effects. The 3 so-called context effects are changes in (relative) choice probabilities for 2 choice alternatives after a third “decoy” option is added to the set. Despite their simplicity, the effects demonstrate that choice probabilities in multi-alternative decision making are contingent on the local context, that is, on the choice set under consideration. Because of their simplicity, on the other hand, similarity, attraction, and compromise effects have been successfully examined in numerous studies to date, and they have become of increasing interest for differentiating between computational cognitive process models of multi-alternative multi-attribute decision making. However, the stimulus arrangement for producing the effects seems to vary between studies, which becomes challenging when model accounts are compared. The purpose of this review is to present various paradigms in a coherent way and describe various model accounts based on a common structure.

The autonomic nervous system and the brainstem: A fundamental role or the background actors for consciousness generation? Hypothesis, evidence, and future directions for rehabilitation and theoretical approaches

INTRODUCTION

One of the hardest challenges of the third century is to develop theories that could joint different results for a global explanation of human consciousness. Some important theories have been proposed, trying to explain the emergence of consciousness as the result of different progressive changes in the elaboration of information during brain processing, giving particular attention to the thalamocortical system.

METHODS

In this article, a summary review of results that highlighted as cerebral cortex could not be so fundamental for consciousness generation is proposed. In detail, three topics were analyzed: (a) studies using experimental approach (manipulating stimuli or brain areas), such as decorticated animals or subliminal presentation of stimuli; (b) studies using anatomo-clinical method (conscious inferenced from observed behaviors); and (c) data from neurostimulation of subcortical areas or of the autonomic nervous system.

RESULTS

We sketch two speculative hypothesis relative, firstly, to the possible independence from cortical areas of the on/off mechanism for consciousness generation and, secondly, to the possible role of information variability generated by the bottom-up exchange of information among neural systems as a switch for consciousness.

CONCLUSIONS

A broad range of evidence regarding the functional role of the brainstem and autonomic nervous system is reviewed for its bearing on a future hypothesis regarding the generation of consciousness experience.

Optimal policy for multi-alternative decisions

Everyday decisions frequently require choosing among multiple alternatives. Yet the optimal policy for such decisions is unknown. Here we derive the normative policy for general multi-alternative decisions. This strategy requires evidence accumulation to nonlinear, time-dependent bounds that trigger choices. A geometric symmetry in those boundaries allows the optimal strategy to be implemented by a simple neural circuit involving normalization with fixed decision bounds and an urgency signal. The model captures several key features of the response of decision-making neurons as well as the increase in reaction time as a function of the number of alternatives, known as Hick's law. In addition, we show that in the presence of divisive normalization and internal variability, our model can account for several so-called 'irrational' behaviors, such as the similarity effect as well as the violation of both the independence of irrelevant alternatives principle and the regularity principle.

VAMP (Voting Agent Model of Preferences): A computational model of individual multi-attribute choice

This paper proposes an original account of decision anomalies and a computational alternative to existing dynamic models of multi-attribute choice. To date, most models attempting to account for the "Big Three" decision anomalies (similarity, attraction, and compromise effects) are variants of evidence accumulation models, or rational Bayesian analysis. This paper provides an existence proof of a new approach in the form of a multi-agent system based on the principles of voting geometry. Assuming there are a number of neural systems (agents) within an individual's brain, the Big Three decision anomalies can arise as a natural consequence of aggregating preferences across these agents. We operationalize these principles in VAMP, (Voting Agent Model of Preferences), and compare its performance to existing computational models as well as to empirical data. This provides a fundamentally different lens for understanding decision anomalies in multi-attribute choice.

Variable time preference

We re-examine behavioral patterns of intertemporal choice with recognition that time preferences may be inherently variable, focusing in particular on the explanatory power of an exponential discounting model with variable discount factors - the variable exponential model. We provide analytical results showing that this model can generate systematically different choice patterns from an exponential discounting model with a fixed discount factor. The variable exponential model accounts for the common behavioral pattern of decreasing impatience, which is typically attributed to hyperbolic discounting. The variable exponential model also generates violations of strong stochastic transitivity in choices involving intertemporal dominance. We present the results of two experiments designed to evaluate the variable exponential model in terms of quantitative fit to individual-level choice data. Data from these experiments reveal that allowing for a variable discount factor significantly improves the fit of the exponential model, and that a variable exponential model provides a better account of individual-level choice probabilities than hyperbolic discounting models. In a third experiment we find evidence of strong stochastic transitivity violations when intertemporal dominance is involved, in accordance with the variable exponential model. Overall, our analytical and experimental results indicate that exponential discounting can explain intertemporal choice behavior that was supposed to be beyond its descriptive scope if the discount factor is permitted to vary at random. Our results also highlight the importance of allowing for different sources of randomness in choice modeling.

Value-based attentional capture affects multi-alternative decision making

Humans and other animals often violate economic principles when choosing between multiple alternatives, but the underlying neurocognitive mechanisms remain elusive. A robust finding is that adding a third option can alter the relative preference for the original alternatives, but studies disagree on whether the third option's value decreases or increases accuracy. To shed light on this controversy, we used and extended the paradigm of one study reporting a positive effect. However, our four experiments with 147 human participants and a reanalysis of the original data revealed that the positive effect is neither replicable nor reproducible. In contrast, our behavioral and eye-tracking results are best explained by assuming that the third option's value captures attention and thereby impedes accuracy. We propose a computational model that accounts for the complex interplay of value, attention, and choice. Our theory explains how choice sets and environments influence the neurocognitive processes of multi-alternative decision making.

A re-examination of "bias" in human randomness perception

Human randomness perception is commonly described as biased. This is because when generating random sequences humans tend to systematically under- and overrepresent certain subsequences relative to the number expected from an unbiased random process. In a purely theoretical analysis we have previously suggested that common misperceptions of randomness may actually reflect genuine aspects of the statistical environment, once cognitive constraints are taken into account which impact on how that environment is actually experienced (Hahn & Warren, Psychological Review, 2009). In the present study we undertake an empirical test of this account, comparing human-generated against unbiased process-generated binary sequences in two experiments. We suggest that comparing human and theoretically unbiased sequences using metrics reflecting the constraints imposed on human experience provides a more meaningful picture of lay people's ability to perceive randomness. Finally, we propose a simple generative model of human random sequence generation inspired by the Hahn and Warren account. Taken together our results question the notion of bias in human randomness perception. (PsycINFO Database Record

The role of (bounded) optimization in theory testing and prediction

We argue that a radically increased emphasis on (bounded) optimality can contribute to cognitive science by supporting prediction. Bounded optimality (computational rationality), an idea that borrowed from artificial intelligence, supports a priori behavioral prediction from constrained generative models of cognition. Bounded optimality thereby addresses serious failings with the logic and testing of descriptive models of perception and action.

A unifying Bayesian account of contextual effects in value-based choice

Empirical evidence suggests the incentive value of an option is affected by other options available during choice and by options presented in the past. These contextual effects are hard to reconcile with classical theories and have inspired accounts where contextual influences play a crucial role. However, each account only addresses one or the other of the empirical findings and a unifying perspective has been elusive. Here, we offer a unifying theory of context effects on incentive value attribution and choice based on normative Bayesian principles. This formulation assumes that incentive value corresponds to a precision-weighted prediction error, where predictions are based upon expectations about reward. We show that this scheme explains a wide range of contextual effects, such as those elicited by other options available during choice (or within-choice context effects). These include both conditions in which choice requires an integration of multiple attributes and conditions where a multi-attribute integration is not necessary. Moreover, the same scheme explains context effects elicited by options presented in the past or between-choice context effects. Our formulation encompasses a wide range of contextual influences (comprising both within- and between-choice effects) by calling on Bayesian principles, without invoking ad-hoc assumptions. This helps clarify the contextual nature of incentive value and choice behaviour and may offer insights into psychopathologies characterized by dysfunctional decision-making, such as addiction and pathological gambling.

People With Autism Spectrum Conditions Make More Consistent Decisions

People with autism spectrum conditions (ASC) show reduced sensitivity to contextual stimuli in many perceptual and cognitive tasks. We investigated whether this also applies to decision making by examining adult participants’ choices between pairs of consumer products that were presented with a third, less desirable “decoy” option. Participants’ preferences between the items in a given pair frequently switched when the third item in the set was changed, but this tendency was reduced among individuals with ASC, which indicated that their choices were more consistent and conventionally rational than those of control participants. A comparison of people who were drawn from the general population and who varied in their levels of autistic traits revealed a weaker version of the same effect. The reduced context sensitivity was not due to differences in noisy responding, and although the ASC group took longer to make their decisions, this did not account for the enhanced consistency of their choices. The results extend the characterization of autistic cognition as relatively context insensitive to a new domain, and have practical implications for socioeconomic behavior.

Robust sampling of decision information during perceptual choice

Humans move their eyes to gather information about the visual world. However, saccadic sampling has largely been explored in paradigms that involve searching for a lone target in a cluttered array or natural scene. Here, we investigated the policy that humans use to overtly sample information in a perceptual decision task that required information from across multiple spatial locations to be combined. Participants viewed a spatial array of numbers and judged whether the average was greater or smaller than a reference value. Participants preferentially sampled items that were less diagnostic of the correct answer ("inlying" elements; that is, elements closer to the reference value). This preference to sample inlying items was linked to decisions, enhancing the tendency to give more weight to inlying elements in the final choice ("robust averaging"). These findings contrast with a large body of evidence indicating that gaze is directed preferentially to deviant information during natural scene viewing and visual search, and suggest that humans may sample information "robustly" with their eyes during perceptual decision-making.

Who "believes" in the Gambler's Fallacy and why?

Humans possess a remarkable ability to discriminate structure from randomness in the environment. However, this ability appears to be systematically biased. This is nowhere more evident than in the Gambler's Fallacy (GF)-the mistaken belief that observing an increasingly long sequence of "heads" from an unbiased coin makes the occurrence of "tails" on the next trial ever more likely. Although the GF appears to provide evidence of "cognitive bias," a recent theoretical account (Hahn & Warren, 2009) has suggested the GF might be understandable if constraints on actual experience of random sources (such as attention and short term memory) are taken into account. Here we test this experiential account by exposing participants to 200 outcomes from a genuinely random (p = .5) Bernoulli process. All participants saw the same overall sequence; however, we manipulated experience across groups such that the sequence was divided into chunks of length 100, 10, or 5. Both before and after the exposure, participants (a) generated random sequences and (b) judged the randomness of presented sequences. In contrast to other accounts in the literature, the experiential account suggests that this manipulation will lead to systematic differences in postexposure behavior. Our data were strongly in line with this prediction and provide support for a general account of randomness perception in which biases are actually apt reflections of environmental statistics under experiential constraints. This suggests that deeper insight into human cognition may be gained if, instead of dismissing apparent biases as failings, we assume humans are rational under constraints. (PsycINFO Database Record

The Effect of Expected Value on Attraction Effect Preference Reversals

The attraction effect shows that adding a third alternative to a choice set can alter preference between the original two options. For over 30 years, this simple demonstration of context dependence has been taken as strong evidence against a class of parsimonious value‐maximising models that evaluate alternatives independently from one another. Significantly, however, in previous demonstrations of the attraction effect alternatives are approximately equally valuable, so there was little consequence to the decision maker irrespective of which alternative was selected. Here we vary the difference in expected value between alternatives and provide the first demonstration that, although extinguished with large differences, this theoretically important effect persists when choice between alternatives has a consequence. We use this result to clarify the implications of the attraction effect, arguing that although it robustly violates the assumptions of value‐maximising models, it does not eliminate the possibility that human decision making is optimal. © 2016 The Authors Journal of Behavioral Decision Making Published by John Wiley & Sons Ltd.