Itzhak Aharon

Cerebral blood flow in depressed patients: a methodological comparison of statistical parametric mapping and region of interest analyses.

Functional brain imaging has assumed a leading role in neuropsychiatric research. However, findings reported for mental disorders often vary. Whether this reflects diversity in pathophysiology or heterogeneity of imaging techniques and data-analytic procedures is still unknown. This study compares region of interest (ROI) and statistical parametric mapping (SPM) analyses of a Tc99m-HMPAO single photon emission computed tomography (SPECT) imaging study of 23 depressed and 21 control subjects. Reduced regional cerebral blood flow (rCBF) was demonstrated by both methods in the right parietal and occipital lobes, but additional regions were identified only on ROI analysis (left temporal) and only on SPM analysis (left parietal). To investigate the contribution of SPM spatial normalization to these discrepancies, further ROI analyses were performed, applying the original ROI templates to normalized images, and applying regions identified by SPM to the original images. This study demonstrated considerable overlap in findings of SPM and ROI analyses. Differences between these methods may be mostly related to subjective placement of ROIs in ROI analysis, and standardized warping inherent in normalization in SPM. Given the advantages and drawbacks of each procedure, the choice of methodology should be determined in accordance with the study design, and complementary use of both methods may be considered.

Toward a general theoretical framework for judgment and decision-making

Over the past 30 years, behavioral and experimental economists and psychologists have made great strides in identifying phenomena that cannot be explained by the classical model of rational choice—anomalies in the discounting of future wealth, present bias, loss aversion, the endowment effect, and aversion to ambiguity, for example. In response to these findings, there has been an enormous amount of research by behavioral scientists aimed at modeling and understanding the nature of these biases1. However, these models, typically assuming situation-specific psychological processes, have shed limited light on the conditions for and boundaries of the different biases, substantially neglecting their relative importance and joint effect. Much less attention has been paid to the investigation of the links between different biases. As a consequence of this approach, it is not always clear which model should be used to predict behavior in a new setting, and maybe a more general theory is needed. We believe that the field of neuroeconomics, which has experienced a rapid growth over the past decade, can play an important role in bridging these gaps, contributing to the building of a general theoretical framework for judgment and decision-making behaviors.

Experience-based decisions and brain activity: three new gaps and partial answers

Experience-based decisions can be defined as decisions emanating from direct or vicarious reinforcements that were received in the past. Typically, in experience-based decision tasks an agent repeatedly makes choices and receives outcomes from the available alternatives, so that choices are based on past experiences, with no explicit description of the payoff distributions from which the outcomes are drawn. The study of experience-based decisions has long roots in the works of mathematical psychologists during the 1950s and 1960s of the last century (e.g., Estes and Burke, 1953; Bush and Mosteller, 1955; Katz, 1964). This type of task has been viewed as a natural continuation of the behaviorist tradition involving animals as subjects, and multiple trials in which feedback is obtained on each trial. During the 1970s and 1980s seminal studies focusing on choices among descriptive gambles began to dominate the field of Judgment and Decision Making, paving the wave for the successful and influential works of Tversky and Kahneman (e.g., Kahneman and Tversky, 1979). Indeed, a review of the decision making literature from 1970 to 1998 conducted by Weber et al. (2004) shows prominent use of description-based tasks over experience-based tasks. Yet the study of experience-based decisions has continued to evolve. Some of the workers in this subfield were neuropsychologists who used experience-based tasks as a natural way to evaluate individual differences owing to these tasks having many choice trials (e.g., Bechara et al., 1994). Others were interested in the complex relations between learning and decision making (Erev and Roth, 1998). An interesting finding that has finally defined the importance of contrasting the two types of tasks – experience-based decisions and description-based decisions, was obtained by Ido Erev and his colleagues. Kahneman and Tversky (1979) showed that individuals overweight small probability events in their decisions from description. For instance, in selecting between an alternative producing

The Neuroscience and Psychophysiology of Experience-Based Decisions: An Introduction to the Research Topic

3 for sure or a gamble producing 10% chance to receive

Emotion, utility maximization, and ecological rationality

32 (and otherwise zero), most people pick the riskier alternative, behaving as if they give greater weight to the relatively rare event (see Hau et al., 2009). Erev and colleagues have demonstrated a reverse phenomenon in decisions from experience (Barron and Erev, 2003; Hertwig et al., 2004; Yechiam et al., 2005). People tend to experientially select alternatives as if what happens most of the time has more weight than the rare event. Thus, people overweight small probability events in decisions from description while underweighting them in decisions from experience. This has been referred to as the description–experience (D–E) gap (Hertwig et al., 2004). The studies exploring the D–E gap were followed by further investigations examining the divergent and convergent processes in these task types (e.g., Rakow et al., 2008; Barron and Yechiam, 2009; Wu et al., 2011). In parallel to the recent advancements in experience-based decisions within the field of Judgment and Decision Making, there have been numerous studies of this type of decisions in Neuroscience. For example, the feedback-based error-related negativity (fERN; see below; e.g., Gehring and Willoughby, 2002) and the role of non-declarative knowledge in selecting advantageously (Bechara et al., 1997) were found in experience-based decisions. Several studies have explicitly showed that that experience-based tasks result in higher correlation between studied brain variables and over behavior. For example, in Aharon et al.’s (2001) fMRI study, participant evaluated the attractiveness of face images either descriptively or by making choices and receiving feedback. Brain activation levels in the reward circuitry (particularly, the nucleus accumbens) matched the evaluation patterns only in the experiential condition. Similarly, severe damage to the orbitofrontal cortex was found to lead to decision impairments in experience-based tasks, but not in description-based tasks (Leland and Grafman, 2005). Still, many of the investigations of these neuroscientific aspects have borrowed their theoretical underpinning from the study of decisions from description, and have not been guided by relevant theories of experience-based decisions. At the same time, many of the decision making studies of experience-based tasks have taken place without awareness of the relevant brain studies using this paradigm. In an attempt to highlight the necessity of integrating the two bodies of research (JDM and neuroscience studies), we present three dissociations (or “gaps”) between brain activation patterns and behavioral choices in these tasks. The majority of this paper is devoted to describing the three gaps in order to encourage further research. Additionally, we also suggest some directions for exploring and explaining these inconsistencies.

What’s on Your Mind? A Brain Scan Won’t Tell

Experience-based decisions can be defined as decisions emanating from direct or vicarious reinforcements that were received in the past. For example, in a typical setting a person initially faces blank buttons and needs to press any of them without prior information concerning the selection outcomes. Upon pressing a button the participant receives monetary outcomes (e.g., “you won

Four applications of the anesthetic cut-off effect

5”) and then, based on this experience, makes another selection. Quite often hundreds of trials of this sort are administered. The outcomes of the two alternatives are usually sampled from different payoff distributions (e.g., a button producing a fixed payoff of

From Neuroeconomics to Genetics: The Intertemporal Choices Case as an Example

5 could be contrasted with a button producing risky payoff, such as wining

Complexity and individual psychology

9 or

Special issue on “Complexity modeling in social science and economics”

1 with equal likelihood). This allows examining the decision response to different incentive structures without explicit information concerning their statistical properties. The current Research Topic aims to integrate various works in this area that have been conducted in Decision science and Neuroscience. The study of experience-based decisions has recently revealed some robust regularities that differ from how people make decisions based on descriptions (i.e., where the participants have full information about the outcome distributions but no feedback). For example, people were found to underweight small probability events in experience-based decisions, while overweighting them in decisions based on descriptions. This is now commonly referred to as the description-experience gap (Hertwig and Erev, 2009). In parallel to the recent advancement in Decision Science, neuroscientists have for a long while used the experience-based decisions paradigm for analyzing brain-behavior interactions. For example, phenomena such as the feedback-based Error-Related Negativity (fERN) in event-related potentials (Gehring and Willoughby, 2002) and the role of non-declarative knowledge in selecting advantageously were discovered using experience-based tasks. The goal of the current Research Topic was to combine these two disciplinary sources concerning experience-based decisions. As expected, several works in this Research Topic explored the “underweighting rare event” tendency. Zhang and Maloney (2012) propose a logit model for this tendency as well as some other robust biases, and also suggest that the underweighting tendency may be driven by basic properties of neural transmission. Upton et al. (2012) propose that underweighting rare events may underlie some of the differences found between neuropsychological populations and controls in complex tasks such as the Iowa Gambling task. By contrast Glockner et al. (2012) do not replicate the underweighting phenomena in decisions from sampling both in behavior as well as in eye point of gaze. Finally, Nevo and Erev (2012) investigate the immediate aftermath of a rare event and highlight a phenomenon whereby surprising events trigger a change in the participants’ response. Other authors focused on the issue of consistent preferences in experience-based tasks. Yechiam and Telpaz (2011) demonstrate consistency between tonic (at rest) arousal and risk taking, and show that it is more prominent in tasks with losses. In an important critique, Marchiori and Elqayam (2012) present some boundaries for consistency in risk taking. Ert (2012) retorts by arguing that most of these boundaries have been demonstrated in decisions from description, while in experience-based decisions consistency of individual differences is more robust. Relating to this, Warren and Holroyd (2012) show that the rapid fERN phenomena, which demarcates the rapid frontal cortical sensitivity to negative/positive outcomes, is larger in a condition involving active learning similar to an experience-based task, than in a condition involving passive learning. Wang et al. (2012) examine the issue of whether choices in an experience-based task are guided by unconscious motivations, as evidenced by advantageous choices in the absence of conscious awareness of the difference between outcomes. Their results suggest a role for unconscious motivations. Such findings are very often interpreted as denoting dual processes or systems. Investigating the influence of dual processes, Hawes et al. (2012) focus on cognitive strategies in a complex decision task and their neural correlates, and their result demonstrate a combination of bottom-up experience-based learning and abstract learning. Sela et al. (2012) focus on an inhibition-related dual process and show that weak transcranial stimulation in the left hemisphere has the ability to affect risk taking, stressing the role of balance between theta activity in the two hemispheres. Finally, Warren and Holroyd (2012) propose two neuromodulatory systems in learning and decision making but stress the context-specific nature of the conditions for the activation of these two systems. For instance, changing the task context from gender to color provided sufficient conditions for differentially activating the two systems. Finally, several authors examined the effect of social versus private environments, a research area often addressed by both decision and neuroscience models. Grygolec et al. (2012) show that in an experience-based task both the striatal and behavioral response to risk greatly differs in a social versus private setting. Investigating a similar domain, de Bruijn and von Rhein (2012) find that the context in which a person makes a decision with other people greatly determines how others’ payoffs are perceived and the frontal mechanisms activated upon them. In a related work, Fahrenfort et al. (2012) show that sharing in a public good game prompts activation of neural systems associated with reward (striatum), but also empathy (anterior insular cortex and anterior cingulate cortex). Finally, Marchiori and Warglien’s (2011) study demonstrates that a neurally inspired model can explain changes in participants’ responses to different social dilemmas. We believe that the current Research Topic led to some transfusion of ideas between the two disciplinary sources of Decision science and Neuroscience in key issues related to experience-based decisions (though see our concluding paper for some gaps that remain unresolved). Reflecting on one emergent theme, it appears that brain-behavior relations are quite unstable and may form or unform in different contexts. Contexts that facilitate the relation between frontal processes and behavior, and have been discussed in this Research Topic, include the availability of active choice, feedback, and losses. This sheds light on why experience-based tasks, which typically include these three components, are quite often used in neuropsychological assessment batteries for evaluating brain dysfunctions.