Deciding when to decide : time-variant sequential sampling models explain the emergence of value-based decisions in the human brain

The cognitive and neuronal mechanisms of perceptual decision making have been successfully linked to sequential sampling models. These models describe the decision process as a gradual accumulation of sensory evidence over time. The temporal evolution of economic choices, however, remains largely unexplored. We tested whether sequential sampling models help to understand the formation of value-based decisions in terms of behavior and brain responses. We used functional magnetic resonance imaging (fMRI) to measure brain activity while human participants performed a buying task in which they freely decided upon how and when to choose. Behavior was accurately predicted by a time-variant sequential sampling model that uses a decreasing rather than fixed decision threshold to estimate the time point of the decision. Presupplementary motor area, caudate nucleus, and anterior insula activation was associated with the accumulation of evidence over time. Furthermore, at the beginning of the decision process the fMRI signal in these regions accounted for trial-by-trial deviations from behavioral model predictions: relatively high activation preceded relatively early responses. The updating of value information was correlated with signals in the ventromedial prefrontal cortex, left and right orbitofrontal cortex, and ventral striatum but also in the primary motor cortex well before the response itself. Our results support a view of value-based decisions as emerging from sequential sampling of evidence and suggest a close link between the accumulation process and activity in the motor system when people are free to respond at any time

Neural Evidence for Adaptive Strategy Selection in Value-Based Decision-Making

In everyday life, humans often encounter complex environments in which multiple sources of information can influence their decisions. We propose that in such situations, people select and apply different strategies representing different cognitive models of the decision problem. Learning advances by evaluating the success of using a strategy and eventually by switching between strategies. To test our strategy selection model, we investigated how humans solve a dynamic learning task with complex auditory and visual information, and assessed the underlying neural mechanisms with functional magnetic resonance imaging. Using the model, we were able to capture participants' choices and to successfully attribute expected values and reward prediction errors to activations in the dopaminoceptive system (e.g., ventral striatum [VS]) as well as decision conflict to signals in the anterior cingulate cortex. The model outperformed an alternative approach that did not update decision strategies, but the relevance of information itself. Activation of sensory areas depended on whether the selected strategy made use of the respective source of information. Selection of a strategy also determined how value-related information influenced effective connectivity between sensory systems and the VS. Our results suggest that humans can structure their search for and use of relevant information by adaptively selecting between decision strategie

Neural correlates of informational cascades: brain mechanisms of social influence on belief updating

Informational cascades can occur when rationally acting individuals decide independently of their private information and follow the decisions of preceding decision-makers. In the process of updating beliefs, differences in the weighting of private and publicly available social information may modulate the probability that a cascade starts in a decisive way. By using functional magnetic resonance imaging, we examined neural activity while participants updated their beliefs based on the decisions of two fictitious stock market traders and their own private information, which led to a final decision of buying one of two stocks. Computational modeling of the behavioral data showed that a majority of participants overweighted private information. Overweighting was negatively correlated with the probability of starting an informational cascade in trials especially prone to conformity. Belief updating by private information was related to activity in the inferior frontal gyrus/anterior insula, the dorsolateral prefrontal cortex and the parietal cortex; the more a participant overweighted private information, the higher the activity in the inferior frontal gyrus/anterior insula and the lower in the parietal-temporal cortex. This study explores the neural correlates of overweighting of private information, which underlies the tendency to start an informational cascad

The attraction effect modulates reward prediction errors and intertemporal choices

Classical economic theory contends that the utility of a choice option should be independent of other options. This view is challenged by the attraction effect, in which the relative preference between two options is altered by the addition of a third, asymmetrically dominated option. Here, we leveraged the attraction effect in the context of intertemporal choices to test whether both decisions and reward prediction errors (RPE)-in the absence of choice-violate the independence of irrelevant alternatives principle. We first demonstrate that intertemporal decision making is prone to the attraction effect in humans. In an independent group of participants, we then investigate how this affects the neural and behavioral valuation of outcomes, using a novel intertemporal lottery task and fMRI. Participants' behavioral responses (i.e., satisfaction ratings) were systematically modulated by the attraction effect, and this modulation was correlated across participants with the respective change of the RPE signal in the Nucleus Accumbens. Furthermore, we show that since exponential and hyperbolic discounting models are unable to account for the attraction effect, recently proposed sequential sampling models might be more appropriate to describe intertemporal choices. Our findings demonstrate for the first time that the attraction effect modulates subjective valuation even in the absence of choice. The findings also challenge the prospect of using neuroscientific methods to measure utility in a context-free manner and have important implications for theories of reinforcement learning and delay discounting.; Many theories of value-based decision making assume that people first assess the attractiveness of each option independently of each other and then pick the option with the highest subjective value. The attraction effect, however, shows that adding a new option to a choice set can change the relative value of the existing options, which is a violation of the independence principle. Using an intertemporal choice framework, we test whether such violations also occur when the brain encodes the difference between expected and received rewards (i.e., the reward prediction error). Our results suggest that both intertemporal choice and valuation without choice do not adhere to the independence principle

How social information affects information search and choice in probabilistic inferences

When making decisions, people are often exposed to relevant information stemming from qualitatively different sources. For instance, when making a choice between two alternatives people can rely on the advice of other people (i.e., social information) or search for factual information about the alternatives (i.e., non-social information). Prior research in categorization has shown that social information is given special attention when both social and non-social information is available, even when the social information has no additional informational value. The goal of the current work is to investigate whether framing information as social or non-social also influences information search and choice in probabilistic inferences. In a first study, we found that framing cues (i.e., the information used to make a decision) with medium validity as social increased the probability that they were searched for compared to a task where the same cues were framed as non-social information, but did not change the strategy people relied on. A second and a third study showed that framing a cue with high validity as social information facilitated learning to rely on a non-compensatory decision strategy. Overall, the results suggest that social in comparison to non-social information is given more attention and is learned faster than non-social information

Attraction effect in risky choice can be explained by subjective distance between choice alternatives

Individuals make decisions under risk throughout daily life. Standard models of economic decision making typically assume that people evaluate choice options independently. There is, however, substantial evidence showing that this independence assumption is frequently violated in decision making without risk. The present study extends these findings to the domain of decision making under risk. To explain the independence violations, we adapted a sequential sampling model, namely Multialternative Decision Field Theory (MDFT), to decision making under risk and showed how this model can account for the observed preference shifts. MDFT not only better predicts choices compared with the standard Expected Utility Theory, but it also explains individual differences in the size of the observed context effect. Evidence in favor of the chosen option, as predicted by MDFT, was positively correlated with brain activity in the medial orbitofrontal cortex (mOFC) and negatively correlated with brain activity in the anterior insula (aINS). From a neuroscience perspective, the results of the present study show that specific brain regions, such as the mOFC and aINS, not only code the value or risk of a single choice option but also code the evidence in favor of the best option compared with other available choice options

Distinguishing three effects of time pressure on risk taking : choice consistency, risk preference, and strategy selection

Quick decision making under risk is ubiquitous in modern times, yet its consequences are not fully understood. Time pressure might change people's risk preferences, lead to less consistent choices, or change people's decision strategy. With the present work, we make the novel contribution of testing all hypotheses against each other in a unifying hierarchical Bayesian model. In two studies, participants decided repeatedly between two risky gambles either with or without high time pressure. We found a significant increase in risky choices under time pressure. With modeling, we show that time pressure decreased choice consistency but did not systematically affect people's risk preferences. In addition, the number of participants using simple, noncompensatory strategies increased slightly under time pressure. Finally, participants did not systematically choose easier gambles more often under time pressure. Thus, a reliable analysis of the effect of time pressure on preferential choice requires a model framework that allows for the distinction between the various effects time pressure can have

Empirical underidentification in estimating random utility models : the role of choice sets and standardizations

A standard approach to distinguishing people’s risk preferences is to estimate a random utility model using a power utility function to characterize the preferences and a logit function to capture choice consistency. We demonstrate that with often‐used choice situations, this model suffers from empirical underidentification, meaning that parameters cannot be estimated precisely. With simulations of estimation accuracy and Kullback–Leibler divergence measures we examined factors that potentially mitigate this problem. First, using a choice set that guarantees a switch in the utility order between two risky gambles in the range of plausible values leads to higher estimation accuracy than randomly created choice sets or the purpose‐built choice sets common in the literature. Second, parameter estimates are regularly correlated, which contributes to empirical underidentification. Examining standardizations of the utility scale, we show that they mitigate this correlation and additionally improve the estimation accuracy for choice consistency. Yet, they can have detrimental effects on the estimation accuracy of risk preference. Finally, we also show how repeated versus distinct choice sets and an increase in observations affect estimation accuracy. Together, these results should help researchers make informed design choices to estimate parameters in the random utility model more precisely

Testing Learning Mechanisms of Rule-Based Judgment

Weighing the importance of different pieces of information is a key determinant of making accurate judgments. In social judgment theory, these weighting processes have been successfully described with linear models. How people learn to make judgments has received less attention. Although the hitherto proposed delta learning rule can perfectly learn to solve linear problems, reanalyzing a previous experiment showed that it does not adequately describe human learning. To provide a more accurate description of learning processes we amended the delta learning rule with three learning mechanisms-a decay, an attentional learning mechanism, and a capacity limitation. An additional study tested the different learning mechanisms in predicting learning in linear judgment tasks. In this study, participants first learned to predict a continuous criterion based on four cues. To test the three learning mechanisms rigorously against each other, we changed the importance of the cues after 200 trials so that the mechanisms make different predictions with regard to how fast people adapt to the new environment. On average, judgment accuracy improved from Trial 1 to Trial 200, dropped when the task environment changed, but improved again until the end of the task. The capacity-restricted learning model, restricting how much people update the cue weights on a single trial, best described and predicted the learning curve of the majority of participants. Taken together, these results suggest that considering cognitive constraints within learning models may help to understand how humans learn when making inferences.</p

Reverse Engineering tools: development and experimentation of innovative methods for physical and geometrical data integration and post-processing

In recent years, the use of Reverse Engineering systems has got a considerable interest for a wide number of applications. Therefore, many research activities are focused on accuracy and precision of the acquired data and post processing phase improvements. In this context, this PhD Thesis deals with the definition of two novel methods for data post processing and data fusion between physical and geometrical information. In particular a technique has been defined for error definition in 3D points’ coordinates acquired by an optical triangulation laser scanner, with the aim to identify adequate correction arrays to apply under different acquisition parameters and operative conditions. Systematic error in data acquired is thus compensated, in order to increase accuracy value. Moreover, the definition of a 3D thermogram is examined. Object geometrical information and its thermal properties, coming from a thermographic inspection, are combined in order to have a temperature value for each recognizable point. Data acquired by an optical triangulation laser scanner are also used to normalize temperature values and make thermal data independent from thermal-camera point of view.L’impiego di tecniche di Ingegneria Inversa si è ampiamente diffuso e consolidato negli ultimi anni, tanto che questi sistemi sono comunemente impiegati in numerose applicazioni. Pertanto, numerose attività di ricerca sono volte all’analisi del dato acquisito in termini di accuratezza e precisione ed alla definizione di tecniche innovative per il post processing. In questo panorama, l’attività di ricerca presentata in questa tesi di dottorato è rivolta alla definizione di due metodologie, l’una finalizzata a facilitare le operazioni di elaborazione del dato e l’altra a permettere un agevole data fusion tra informazioni fisiche e geometriche di uno stesso oggetto. In particolare, il primo approccio prevede l’individuazione della componente di errore nelle coordinate di punti acquisiti mediate un sistema di scansione a triangolazione ottica. Un’opportuna matrice di correzione della componente sistematica è stata individuata, a seconda delle condizioni operative e dei parametri di acquisizione del sistema. Pertanto, si è raggiunto un miglioramento delle performance del sistema in termini di incremento dell’accuratezza del dato acquisito. Il secondo tema di ricerca affrontato in questa tesi consiste nell’integrazione tra il dato geometrico proveniente da una scansione 3D e le informazioni sulla temperatura rilevata mediante un’indagine termografica. Si è così ottenuto un termogramma in 3D registrando opportunamente su ogni punto acquisito il relativo valore di temperatura. L’informazione geometrica, proveniente dalla scansione laser, è stata inoltre utilizzata per normalizzare il termogramma, rendendolo indipendente dal punto di vista della presa termografica