Neue Ideen müssen nicht zwingend von externen Beratern kommen oder durch Unternehmensplanspiele generiert werden, die gerne mit externen Studenten oder Absolventen durchgefüht werden. Häufig werden derartige Unternehmensplanspiele durchgeführt, um von Außenstehenden Ideen und andere Blickwinkel gezeigt zu bekommen. Diese Herangehensweise konnte nun auch durch Studien belegt werden. Die Kellogg School of Management konnte in einer aktuellen Studie nachweisen, dass Außenstehende das Denken in Teams verbessern und so bessere Resultate erzielt werden können.

Dabei wurde ein Gruppen-Problemlösungs-Experiment durchgeführt. Hierfür wurden verschiedene Gruppen mit Problemstellungen konfrontiert. Zu einigen Gruppen wurden fünf Minuten lang Neulinge in die Überlegungen mit einbezogen. Bei den Gruppen bei denen der Neuling ein Außenstehender war, konnten häufiger das Problem erfolgreich lösen.

Müssen Unternehmen jetzt aber für jede neue Idee externe Berater anstellen oder Unternehmensplanspiele organisieren? Nein. Die gute Nachricht ist, dass der Außenstehende kein externer Berater sein muss. Wichtig ist, dass sich der Außenstehende deutlich von den anderen Gruppenmitgliedern unterscheidet. Neben offensichtlichen Unterscheidungsmerkmalen, wie Geschlecht, werden in der Studie weitere Möglichkeiten vorgeschlagen:

• Mitarbeiter einer anderen Abteilung (Produktionsleiter unterstützt das Marketing-Team)
• ein Angestellter aus einem anderen Land (interkulturelle Komponente)
• …

Dabei müssen sich die Außenstehenden nicht besonders in den Vordergrund drängen oder eigene Meinungen abgeben. Es reicht die bloße Anwesenheit der Außenstehenden, um die Effektivität und Effizienz der Gruppe zu erhöhen. So ist es möglich neue Ideen ohne große Beratungshonorare mit einer intelligenten Teamzusammensetzung kostengünstig zu generieren. Dies ist dadurch möglich, dass ein neues Teammitglied das Unbehagen des Teams steigern kann und so die Qualität der Resultate steigt.

A very interesting paper on “human behavior” by the Department of Psychiatry, Laboratory for Neuroimaging and Neurophysiology, Johann Wolfgang Goethe University Frankfurt.

In neuroeconomics, reward and social interaction are central concepts to understand what motivates human behaviour. Both concepts are investigated in humans using neuroimaging methods. In this paper, we provide an overview about these results and discuss their relevance for economic behavior. For reward it has been shown that a system exists in humans that is involved in predicting rewards and thus guides behavior, involving a circuit including the striatum, the orbitofrontal cortex and the amygdala. Recent studies on social interaction revealed a mentalizing system representing the mental states of others. A central part of this system is the medial prefrontal cortex, in particular the anterior paracingulate cortex. The reward as well as the mentalizing system is engaged in economic decision-making. We will discuss implications of this study for neuromarketing as well as general implications of these results that may help to provide deeper insights into the motivating forces of human behavior.

Neuroeconomics combines neuroscience, economics, and psychology to study how people make decisions. It looks at the role of the brain when we evaluate decisions, categorize risks and rewards, and interact with each other. It can be included in the field of social neuroscience.

Neuroscientists have studied how our brain make decisions about how much we’re willing to pay for a product.

When subjects view luxury products such as Louis Vuitton and Gucci being sold at full price, both the nucleus accumbens and the anterior cingulate light up, showing the pleasure of anticipatory reward mixed with the conflict about buying such an expensive doodad. But when consumers are shown the same products priced at a significant discount, the “conflict” signal decreases as the reward activation simultaneously goes up.

When expensive wine was presented, there was a flurry of activity in subjects medial orbitofrontal cortices, where they perceive pleasantness, indicating that the higher price of a product enhances our enjoyment of it… “we enjoy our purchases… because we paid more.”

It’s a known fact that a high price can give very high pleasure to the people who can afford, because it gives them a sense of “uniqueness”. They know that only few people can afford expensive products and therefore they feel good paying for them.

These findings, and others, indicate that Price Management, will soon be replaced by Neuroeconomics together with Neuromarketing.

Why don’t let our brain decide the “perfect price”?

This coming Saturday I will start a three-week visit to Duke University, where are located the labs of Michael Platt and Scott Huettel, two of the most prominent neuroeconomists.

The purpose of this visit is to perform an “ethnography” of neuroeconomics, focused on how interdisciplinarity works in practice. Among other purposes, this study will provide me with qualitative insights which will complement one of the other projects I am currently running on the bibliometric study of neuroeconomics.

A bibliometric study can be many things, in this case I am interested in how publications in neuroeconomics reflect its interdisciplinary nature. Online databases such as ISI Thomson help a great deal in performing such a study, and the remaining difficulties are probably of the conceptual sort (see the previous post!).

The results of those bibliometric studies are most commonly represented in graphs of social networks: they help read a tonne of information in just one picture.  I am currently training myself to use them, getting gray hair at pre-processing bibliometric files which then can be fed into those softwares…

This is how a social network can look like in practice (click on the pic to expand):

A co-citation network - green and yellow nodes indicate those papers citing heavily the original set

By Darren Schreiber

We’ve got lots of new work showing showing the role of the default mode network, specifically in social cognition (1, 3, 4, 8, 10).  While this was a speculation when I was writing about it five years ago, it seems to be quite cemented now.  We’ve also got a set of articles discussing reward processing (2, 6, 7).  I thought the overview piece on the Allen Brain Atlas was interesting (11).  Currently, the mouse brain is the most complete, but Wired Magazine had a fascinating piece about the work that is currently proceeding on doing the same thing for the human brain.

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1) Functional connectivity and alterations in baseline brain state in humans.
2) The impact of social comparison on the neural substrates of reward processing: an event-related potential study.
3) Neural correlates of social cognition in naturalistic settings: a model-free analysis approach.
4) Understanding others’ actions and goals by mirror and mentalizing systems: a meta-analysis
5) Double dissociation between action-driven and perception-driven conflict resolution invoking anterior versus posterior brain systems.
6) The influence of context valence in the neural coding of monetary outcomes.
7) Different representations of relative and absolute subjective value in the human brain
Neural Systems of Social Comparison and the “Above-Average” Effect
9) Experimental evolution of bet hedging
10) What does the retrosplenial cortex do?
11) The Allen Brain Atlas: 5 years and beyond
12) The functional anatomy of the frontal lobes

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1) Martuzzi et al. Functional connectivity and alterations in baseline brain state in humans. Neuroimage (2010) vol. 49 (1) pp. 823-34

This work examines the influence of changes in baseline activity on the intrinsic functional connectivity fMRI (fc-fMRI) in humans. Baseline brain activity was altered by inducing anesthesia (sevoflurane end-tidal concentration 1%) in human volunteers and fc-fMRI maps between the pre-anesthetized and anesthetized conditions were compared across different brain networks. We particularly focused on low-level sensory areas (primary somatosensory, visual, and auditory cortices), the thalamus, and pain (insula), memory (hippocampus) circuits, and the default mode network (DMN), the latter three to examine higher-order brain regions. The results indicate that, while fc-fMRI patterns did not significantly differ (p<0.005; 20-voxel cluster threshold) in sensory cortex and in the DMN between the pre- and anesthetized conditions, fc-fMRI in high-order cognitive regions (i.e. memory and pain circuits) was significantly altered by anesthesia. These findings provide further evidence that fc-fMRI reflects intrinsic brain properties, while also demonstrating that 0.5 MAC sevoflurane anesthesia preferentially modulates higher-order connections.

2) Qiu et al. The impact of social comparison on the neural substrates of reward processing: an event-related potential study. Neuroimage (2010) vol. 49 (1) pp. 956-62

Event-related potentials (ERPs) were recorded to explore the electrophysiological correlates of reward processing in the social comparison context when subjects performed a simple number estimation task that entailed monetary rewards for correct answers. Three social comparison stimulus categories (three relative reward levels/self reward related to the other subject’s) were mainly prepared: Self:Other=1:2 (Disadvantageous inequity condition); Self:Other=1:1 (Equity condition); and Self:Other=2:1 (Advantageous inequity condition). Results showed that: both Disadvantageous and Advantageous inequity elicited a more negative ERP deflection (N350-550) than did Equity between 350 and 550 ms, and the generators of N350-550 were localized near the parahippocampal gyrus and the medial frontal/anterior cingulate cortex, which might be related to monitor and control reward prediction error during reward processing. Then, Disadvantageous and Advantageous inequity both elicited a more late negative complex (LNC1 and LNC2) than did Equity between 550 and 750 ms. The generators of LNC1 and LNC2 were both localized near the caudate nucleus, which might be related to reward processing under social comparison.

3) Wolf et al. Neural correlates of social cognition in naturalistic settings: a model-free analysis approach. Neuroimage (2010) vol. 49 (1) pp. 894-904

Neuroimaging studies have consistently identified a network of brain regions subserving inferences of other humans’ mental states. This network consists of the superior temporal sulcus, temporoparietal junction, medial prefrontal cortex, temporal poles, and precuneus. Little is known, however, about the neural substrate underlying Theory of Mind processes in close to real-life conditions. To investigate those processes in more naturalistic settings, we used an fMRI adaptation of the video-based Movie for the Assessment of Social Cognition (MASC; Dziobek et al., 2006), which considers separate analysis of implicit mental state reasoning during rapidly changing perceptual cues as demanded in naturalistic settings and explicit mental state reasoning. We analyzed fMRI data by means of both a standard general linear model (GLM) approach and a tensor probabilistic independent component analysis (T-PICA), which is a novel model-free approach that allows decomposition of activation into independent spatio-temporally coherent functional networks. The model-based GLM approach revealed the typical explicit mental state reasoning network. Complementary to the GLM approach, the model-free T-PICA approach showed that those regions are also recruited during implicit mental state reasoning and that they are represented in three independent, functionally connected networks. The first component, mediating face processing and recognition, comprises the occipito-parietotemporal cortices, while the second component, involved in language comprehension, comprises the temporal lobes, lateral prefrontal cortex, and precuneus. The dorsomedial prefrontal cortex and the precuneus comprise the third component, which is likely responsible for self-referential mental activity. These results show that the mental state reasoning network can be decomposed into circumscribed functional networks mediating differential aspects of Theory of Mind.

4) Van Overwalle and Baetens. Understanding others’ actions and goals by mirror and mentalizing systems: a meta-analysis. Neuroimage (2009) vol. 48 (3) pp. 564-84

This meta-analysis explores the role of the mirror and mentalizing systems in the understanding of other people’s action goals. Based on over 200 fMRI studies, this analysis demonstrates that the mirror system – consisting of the anterior intraparietal sulcus and the premotor cortex – is engaged when one perceives articulated motions of body parts irrespective of their sensory (visual or auditory) or verbal format as well as when the perceiver executes them. This confirms the matching role of the mirror system in understanding biological action. Observation of whole-body motions and gaze engage the posterior superior temporal sulcus and most likely reflects an orientation response in line with the action or attention of the observed actor. In contrast, the mentalizing system – consisting of the temporo-parietal junction, the medial prefrontal cortex and the precuneus – is activated when behavior that enables inferences to be made about goals, beliefs or moral issues is presented in abstract terms (e.g., verbal stories or geometric shapes) and there is no perceivable biological motion of body parts. A striking overlap of brain activity at the temporo-parietal junction between social inferences and other, non-social observations (e.g., Posner’s cuing task) suggests that this area computes the orientation or direction of the behavior in order to predict its likely end-state (or goal). No conclusions are drawn about the specific functionality of the precuneus and the medial prefrontal cortex. Because the mirror and mentalizing systems are rarely concurrently active, it appears that neither system subserves the other. Rather, they are complementary. There seems, however, to be a transition from the mirror to the mentalizing system even when body-part motions are observed by perceivers who are consciously deliberating about the goals of others and their behavioral executions, such as when perceived body motions are contextually inconsistent, implausible or pretended.

5) Schulte et al. Double dissociation between action-driven and perception-driven conflict resolution invoking anterior versus posterior brain systems. Neuroimage (2009) vol. 48 (2) pp. 381-90

The ability to select and integrate relevant information in the presence of competing irrelevant information can be enhanced by advance information to direct attention and guide response selection. Attentional preparation can reduce perceptual and response conflict, yet little is known about the neural source of conflict resolution, whether it is resolved by modulating neural responses for perceptual selection to emphasize task-relevant information or for action selection to inhibit pre-potent responses to interfering information. We manipulated perceptual information that either matched or did not match the relevant color feature of an upcoming Stroop stimulus and recorded hemodynamic brain responses to these events. Longer reaction times to incongruent than congruent color-word Stroop stimuli indicated conflict; however, conflict was even greater when a color cue correctly predicted the Stroop target’s color (match) than when it did not (nonmatch). A predominantly anterior network was activated for Stroop-match and a predominantly posterior network was activated for Stroop-nonmatch. Thus, when a stimulus feature did not match the expected feature, a perceptually-driven posterior attention system was engaged, whereas when interfering, automatically-processed semantic information required inhibition of pre-potent responses, an action-driven anterior control system was engaged. These findings show a double dissociation of anterior and posterior cortical systems engaging in different types of control for perceptually-driven and action-driven conflict resolution.

6) Hardin et al. The influence of context valence in the neural coding of monetary outcomes. Neuroimage (2009) vol. 48 (1) pp. 249-57

The emotional significance of objects and events depends on the context in which they occur. Using functional magnetic resonance imaging, we examined the modulation of neural responses to monetary outcomes while subjects performed a decision-making task in a positive and a negative economic context. Neural responses indicated a relative regional specialization in the neural coding of outcome valence and followed three distinct patterns. The nucleus accumbens (NAc) and orbital frontal cortex (OFC) appeared to code the most extreme outcome in each context, with a potentiated response for favorable outcomes by a positive context. The amygdala and insula appeared to also code highly salient outcomes, but showed a potentiated response to unfavorable outcomes occurring in a negative context. The medial prefrontal cortex (medPFC), on the other hand, only coded favorable responses occurring in a positive context. Moreover, the medPFC showed large inter-individual variability when responding to outcomes in a negative context, suggesting that its role in a negative context may depend on a number of individual factors. The results of this work provide evidence of complex valence-based regional dissociations that are influenced by contextual factors.

7) Grabenhorst and Rolls. Different representations of relative and absolute subjective value in the human brain. Neuroimage (2009) vol. 48 (1) pp. 258-68

Relative reward value is important for the choice between a set of available rewards, and absolute reward value for stable and consistent economic choice. It is unclear whether in the human brain subjective absolute value representations can be dissociated from relative reward value representations. Using fMRI, we investigated how the subjective pleasantness of an odor is influenced by whether the odor is presented in the context of a relatively more pleasant or less pleasant odor. We delivered two of a set of four odors separated by a delay of 6 s, with the instruction to rate the pleasantness of the second odor, and searched for brain regions where the activations were correlated with the absolute pleasantness rating of the second odor, and for brain regions where the activations were correlated with the difference in pleasantness of the second from the first odor, that is, with relative pleasantness. Activations in the anterolateral orbitofrontal cortex tracked the relative subjective pleasantness, whereas activations in the anterior insula tracked the relative subjective unpleasantness. In contrast, in the medial and midorbitofrontal cortex activations tracked the absolute pleasantness of the stimuli. Thus, both relative and absolute subjective value signals which provide important inputs to decision-making processes about which stimulus to choose are separately and simultaneously represented in the human brain.

Beer and Hughes. Neural Systems of Social Comparison and the “Above-Average” Effect. Neuroimage (2009) pp.

Extant neural models of self-evaluation are dominated by associations with medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) function and have mostly been developed from studies differentiating self-evaluation from evaluation of other people. Although self-evaluation is robustly characterized by systematic biases, current neural models of self-evaluation cannot speak to their neurobiology because of a lack of research. The few extant studies have made claims about associations between bias and ventral anterior cingulate cortex (vACC) function but have confounded bias with the valence of experimental stimuli. In Study 1, fMRI was used to examine the neurobiology of the “above average” effect, a robust self-evaluation bias. The majority of people judge their personality to be more desirable (i.e. more positive and less negative traits) than their peers’ personalities. MPFC and PCC were significantly more activated by a condition that reduced susceptibility to “above average” judgments. However, MPFC and PFCC activity were not modulated by individual differences in “above average” judgments. VACC activity distinguished positive from negative valence but did not predict individual differences in “above average” judgments. Instead, the extent to which participants viewed themselves as “above average” was negatively correlated with orbitofrontal cortex (OFC) and, to a lesser extent, dorsal anterior cingulate cortex (dACC) activation. A complementary study found that mental load increases “above average” judgments (Study 2). These findings are the first to directly examine the neural systems involved in social judgment bias and have implications for the association between frontal lobe dysfunction and poor insight.

9) Beaumont et al. Experimental evolution of bet hedging. Nature (2009) vol. 462 (7269) pp. 90-3

Bet hedging-stochastic switching between phenotypic states-is a canonical example of an evolutionary adaptation that facilitates persistence in the face of fluctuating environmental conditions. Although bet hedging is found in organisms ranging from bacteria to humans, direct evidence for an adaptive origin of this behaviour is lacking. Here we report the de novo evolution of bet hedging in experimental bacterial populations. Bacteria were subjected to an environment that continually favoured new phenotypic states. Initially, our regime drove the successive evolution of novel phenotypes by mutation and selection; however, in two (of 12) replicates this trend was broken by the evolution of bet-hedging genotypes that persisted because of rapid stochastic phenotype switching. Genome re-sequencing of one of these switching types revealed nine mutations that distinguished it from the ancestor. The final mutation was both necessary and sufficient for rapid phenotype switching; nonetheless, the evolution of bet hedging was contingent upon earlier mutations that altered the relative fitness effect of the final mutation. These findings capture the adaptive evolution of bet hedging in the simplest of organisms, and suggest that risk-spreading strategies may have been among the earliest evolutionary solutions to life in fluctuating environments.

10) Vann et al. What does the retrosplenial cortex do?. Nat Rev Neurosci (2009) vol. 10 (11) pp. 792-802

The past decade has seen a transformation in research on the retrosplenial cortex (RSC). This cortical area has emerged as a key member of a core network of brain regions that underpins a range of cognitive functions, including episodic memory, navigation, imagination and planning for the future. It is now also evident that the RSC is consistently compromised in the most common neurological disorders that impair memory. Here we review advances on multiple fronts, most notably in neuroanatomy, animal studies and neuroimaging, that have highlighted the importance of the RSC for cognition, and consider why specifying its precise functions remains problematic.

11) Jones et al. The Allen Brain Atlas: 5 years and beyond. Nat Rev Neurosci (2009) vol. 10 (11) pp. 821-8

The Allen Brain Atlas, a Web-based, genome-wide atlas of gene expression in the adult mouse brain, was an experiment on a massive scale. The development of the atlas faced a combination of great technical challenges and a non-traditional open research model, and it encountered many hurdles on the path to completion and community adoption. Having overcome these challenges, it is now a fundamental tool for neuroscientists worldwide and has set the stage for the creation of other similar open resources. Nevertheless, there are many untapped opportunities for exploration.

12) Nachev et al. The functional anatomy of the frontal lobes. Nat Rev Neurosci (2009) vol. 10 (11) pp. 829

In their illuminating recent article (Is the rostro-caudal axis of the frontal lobe hier- archical? Nature Rev. Neurosci. 10, 659–669 (2009))1, Badre and D’Esposito generalize to the frontal lobes as a whole a point we recently made about the medial frontal cor- tex2: that the functional architecture suggests a continuous rostro-caudal gradient reflect- ing the conditional complexity of the associ- ated behaviour. Reviewing a broad swathe of behavioural and neurophysiological studies, they argue — convincingly, in our view — that a wealth of data now supports this new conceptual framework for frontal lobe func- tion. However, there are some important consequences of such a perspective that might not be apparent at first glance and that merit some further consideration.

Opinions about neuroeconomics vary enormously – to begin with, there is little agreement about what even *counts* as neuroeconomics.

In my historical study of neuroeconomics,  I am confronted to this difficulty right from the first step. Before even analyzing it, what is neuroeconomics, the field that I am studying? There is yet no journal of neuroeconomics which would map and delineate the topic, and there is of course no single parent field from which this sub-field can be traced from.

Mapping neuroeconomics

The Society for Neuroeconomics is a useful rallying point where neuroeconomists can be found, but there is an obvious North American bias. More, an unknown proportion of scientists attending this conference would be reluctant to be labeled neuroeconomists, if I refer to the interviews I conducted there. So?

There is always the possibility to ask “the experts”, as it is customary to do in scientometrics. That is, I would not placate any arbitrary definition of neuroeconomics on the field, but would ask some renowned neuroeconomists what they would consider classic papers in neuroeconomics, or who do they consider to be the leading figures in neuroeconomics, and then start from there.

The problem with this approach is that leading neuroeconomists are truly extremely busy people, so the sample of experts that I could tap from would be very low, and hence surely not representative of all the currents represented in neuroeconomics.

There are many other ways to define a field, all with their particular drawbacks. One is to refer to the indexing of papers performed by Thomson Reuters‘ ISI Web of Knowledge, a database which records and indexes virtually all peer-reviewed journals and their papers on the planet since 1988. If a paper is indexed with the keyword “neuroeconomics”, then it can count as neuroeconomics. Authors who published a certain number of articles indexed with the keyword “neuroeconomics” can be considered to be neuroeconomists. However, this approach is equivalent to delegating the task of defining neuroeconomics to the employees in charge of indexing the papers at Thomson Reuters: given the immensity of their task, probably not the best experts in neuroeconomics.

I am in the process of finding my own (and hopefully, consensual) solution to this arduous problem of mapping a field which has still not a stabilized identity. But from experience, it is an issue where everybody can quickly come up with their preferred procedure. So, what do you think? What would be your procedure to arrive at a definition of *who is a neuroeconomist*, and *what is a paper in neuroeconomics*?

Zunächst stellt sich mit Sicherheit für euch die Frage, was dieses Thema mit Hirnforschung oder Neuromarketing zu tun hat. Aber dieser Blog behandelt ja auch die anatomischen und neurowissenschaftlichen Grundlagen, die dieser Artikel behandelt. In den letzten Monaten wurden von der Florida State University interessante Studien durchgeführt, die sich mit den Auswirkungen der Zuckerkonzentration im Körper auf die mentalen Fähigkeiten beschäftigten.

Dabei mussten die Probanden zunächst eine mentale Aufgabe meistern. Anschließend wurde allen Probanden Limonade zu trinken gegeben. Die eine Hälfte der Probanden erhielt dabei zuckerhaltige Limonade, die andere Hälfte bekam zuckerfreie Limonade ohne jeden Nährwert.

Anschließend mussten die Probanden nochmals eine Aufgabe bearbeiten. Die Ergebnisse dieser Aufgaben waren selbst für die Forscher überraschend. Es zeigte sich, dass die Probanden, die die zuckerfreie Limonade tranken, häufiger impulsiver entschieden und insgesamt schlechtere Entscheidungen trafen.

Diese Ergebnisse brachten die Forscher mit dem Abbau des präfrontalen Cortex zusammen, die bei der zuckerfreien Limonade auftrat. Probanden, die Zucker bekamen waren eher in der Lage die Funktion dieses Gehirnbereichs aufrecht zu erhalten.

Diese Studie kann auch erklären, wieso wir schlecht gelaunt sind und launisch werden, wenn wir Hunger haben oder noch einfacher, wenn uns Zucker fehlt. Unser Gehirn ist dadurch weniger in der Lage negative Emotionen zu unterdrücken. Unsere schlechte Stimmung ist demnach nur ein “heruntergekommener”, schwächelnder präfrontaler Cortex.

Als Light-Trinkender Zucker-Meider, finde ich diese Ergebnisse äußerst interessant. Natürlich soll dies keine Rechtfertigung sein, aber vielleicht kann ein Zucker-Schuss ab und an mein Gehirn etwas schärfen.

Computational neuroeconomics?

Neuroeconomists develop their own jargon, as it is to be expected from a consolidating community of scientists with distinct interests. But denominations, categorical classifications, and basic concepts in neuroeco are very much still in the early stages of their definitions – they have not been “blackboxed” yet.

“Computational neuroeconomics” is one of such terms. I was a bit tired of nodding to my interlocutors when computational neuroeco popped up in interviews, without being sure to understand how different it was from “not computational” neuroeco, or from computational modeling in cognitive neuroscience.

A first possibility was that it could resemble this class of models where connectivity of different brain regions is represented by an analogy with the architecture of a computer.  This is the kind of model used by Peter Dayan and Szabolcs Kali in a paper in 2004, who discussed memory storage and retrieval.

It could have also been the models inspired not by computer hardware, but by softwares: algorithm processes which demonstrate that starting with very simple building blocks and logical rules, an organism could  achieve complex cognitive tasks like letter recognition and other sensory to motor tasks.

But computational neuroeconomics seems in fact to represent an alternative, third possibility.  An entire session was devoted to it in the third day of the annual meeting of the Society for Neuroeconomics. It featured papers which were basically game theory applied to social cognitive problems. The language of game theory provides concepts  to think many useful parameters of behavior: strategies, payoffs, probabilities, types. As I understand it, the task of computational neuroeco is to operationalize those concepts. In the speeches of the session, it was interesting to see how the presenters navigated between mathematical sophistication, and constant references to pragmatic issues in social behavior: what theory of mind emerges from repeated interactions between players, or how risk minimization is accomplished through learning.

Is it a new approach in neuroeconomics? Not really. When one thinks about it, it is “simply” further work in a direction impulsed by Paul Glimcher and his collaborators since the very beginnings of neuroeconomics, when they introduced expected utility and then Bayesian Nash equilibrium in their studies of neurons in the LIP area for monkeys.  In this light, computational neuroeco appears to be at the very core of the new discipline.

One of the enduring questions of human existence relates to the tension between private and common interest. Often framed as the distinction between cooperation and individualism, it can be summarized as asking, “to what extent are my actions determined by my desire to pursue my own self-interest versus the interests of others.”  The dilemma was certainly recognized by Darwin, but has been the focus of several bursts of academic interest in the last 50 years.  In the 1960s, William Hamilton began to formalize the idea that altruism towards kin – those with whom we share some genetic heritage – made sense using the tools of evolutionary theory, and Richard Dawkins famously added a laser-like focus to this formalism with his selfish gene hypothesis.

But what are we to make of the fact that humans regularly help individuals who are not kin?  In the 1970s, Robert Trivers developed the notion of reciprocal altruism to explain such cooperative behaviour – essentially, if you help me I’ll help you.  Soon thereafter, game theory began to be applied to the paradigm, and has turned out to be an exemplary way of probing the tension between cooperation and selfish behaviour (a previous post dealt with game theory and reciprocal altruism).  In one prominent series of studies, Ernst Fehr and his colleagues have amassed a substantial body of data showing that the kind of large scale cooperative behaviour exhibited by humans is dependent primarily upon the threat of punishment: the tit for tat hypothesis.  Now, in a new paper in Science, Rand et al. challenge this view, showing that in a public goods game, positive interactions promote cooperation when repeated interactions are expected to occur.  From the abstract.

The public goods game is the classic laboratory paradigm for studying collective action problems. Each participant chooses how much to contribute to a common pool that returns benefits to all participants equally. The ideal outcome occurs if everybody contributes the maximum amount, but the self-interested strategy is not to contribute anything. Most previous studies have found punishment to be more effective than reward for maintaining cooperation in public goods games. The typical design of these studies, however, represses future consequences for today’s actions. In an experimental setting, we compare public goods games followed by punishment, reward, or both in the setting of truly repeated games, in which player identities persist from round to round. We show that reward is as effective as punishment for maintaining public cooperation and leads to higher total earnings. Moreover, when both options are available, reward leads to increased contributions and payoff, whereas punishment has no effect on contributions and leads to lower payoff. We conclude that reward outperforms punishment in repeated public goods games and that human cooperation in such repeated settings is best supported by positive interactions with others.

This work from Nowak’s group reprises a theme that is important in considering neuroeconomic studies of human behaviour: it is important to model the behaviour as closely as possible on the real world conditions in which humans live (and thrive), while trying to limit confounding variables as laboratory experiments are wont to do. [For another take on the issue, see this paper in Nature from earlier in the year, also from Nowak's group.  Also, there is an excellent commentary in the recent issue of Science on the origins of cooperation by Elizabeth Pennissi.]

The tension between private and common interest is certainly of interest to academics studying the evolution of social behavior, but it is also central to nearly every debate about political life in the modern world.  Examples abound (the current health care debate in the US is an obvious one), but as a citizen of both the US and Canada, one comment in a recent issue of The New Yorker by Adam Gopnik strikes me as particularly relevant.  The article is a profile of Michael Ignatieff, Leader of the Official Opposition and the Liberal party in Canada, and possibly the next Prime Minister of the country.  Because Ignatieff is both a politician and a political philosopher who spent 25 years abroad including a long stint on the faculty of Harvard, it was perhaps inevitable that Gopnik’s prose would wander into describing the unique brand of glue that holds together the country known as Canada.

We are not, and have never been, the Canadian collectivists argue – in conscious opposition to older Anglo-American traditions – the rational individuals of liberal contract theory.  No man is an island, and rules made for imaginary islands ignore the fragile ecology of the archipelago.  We are people who live in communities, and our sense of who we are derives from what the people around us are like.  To exalt the individual and his rights at the expense of nurturing the tenuous threads of togetherness leads to violence, alienation, political apathy, and the growth of crazy movements that can supply, in moonshine form, the sense of solidarity that pure “rights” liberalism can’t – the very traits that Canadians see in a nearby country, they name no names.

That is big, really big.

The free-rider problem is simple. It describes those situations when a group of individuals would benefit from a common action, but each individual separately would prefer not to make any effort to make this action happen.

Like: as a group, we would like to have an environmental policy to stop global warming, but when asked how much tax I personally would be ready to pay to implement this policy, I refuse to declare that I’d be willing to pay much. Even if the amount that I would pay would be more than compensated by the benefits of an environmental policy! Simply because hey, if the environmental policy is decided by others and payed by others, once implemented it will also benefit me, so why would I bother paying for it? It is much easier to let others pay for it, and then, I’ll benefit from it anyway!

Another free rider

It is the eternal problem of the free rider: “I would like to have the benefits of the collective action, but I would prefer if the costs were payed by my neighbor.” The problem is, of course, that if everybody thinks like that, then everybody states that they would not pay much, and the budget for the collective action is never gathered. That’s too bad, because the collective good would have enhanced everybody’s welfare!

It has huge implications for tax policy, or any issue where a collective action would be required. And the difficulty faced has always been that when you ask people “how much would you be ready to pay for this collective good”, they tend to understate what they are really willing to pay – always hoping that the collective good will be build anyhow – but payed by their neighbors.

What if we could “read” in people’s mind what they would really like to pay for a collective good? This would allow to know how much people would each be ready to pay for the collective action. The collective action would then be undertaken, only if its benefits would be superior to the sum of the payments that each individual declared to be ready to make.

This is the experiment conducted by a team of CalTech neuroeconomists, just published in Science. They gathered groups of subjects, and scanned their brain while they stated the amount they were willing to pay for a given collective investment.

To be precise, this what not a simple lie-detector mechanism: the accuracy of the scan to detect the “honesty” of your choice was just 56%. But it is enough to act as a threat to participants: they will be punished with heavy taxes if they are found to be willing to pay very little for an investment which will repay them much. It acts as an incentive for participants to reveal their true preference each time they are asked about a potential collective investment!

Ian Krajbich, lead author of the study

The results of this “Neurally Informed Mechanism” as they call it are astounding: the total welfare achieved by this experiment is 93% of the ideal case, which means that free-riding has almost completely disappeared! This is a remarkable result, given that traditional experimental settings do not score better than 23%.

So, the free-rider problem, or the problem of collective action finally solved? Neuroeconomics made an interesting step in this direction. Huge!

The study raises some questions though. For example, the procedure followed shows that the participants were convinced with lengthy, technical arguments that the “not free-riding” strategy was the most advantageous one.  Those arguments were true, and the experimenters deduce that if the participants did not free-ride, it is because they understood it was in their best interest. But one can also object that they did not free-ride simply because they had been brain-washed about not free-riding: they simply trusted the experimenter and played in the fashion that was strongly suggested. If that is the case, then the 93% result is not so amazing.

This doubt is even greater when one wonders about the actual role played by the fMRI scan: crucial or not? At 56% of free-riding detection, just above fifty-fifty, one doubts whether the participants refused to free-ride because the threat of the scan detection made it the best strategy to play, or simply because they were impressed by this big machine and the intense strategical training they received from the researchers (see the supplement online material of the article, esp. the section “strategy” on pp. 42-45, which shows how much the participants were lectured about NOT free-riding. )

More about it? Antonio Rangel, an economist in the CalTech team reports on the Science article in an interviewed by James Hugues, from the Institute for Ethics and Emerging Technologies. Click here for the interview.

Yes, we are all sick of the people who rip off other people’s work in their Second Life art, pass it off as their own, and even request payment from third parties for the “work” they’ve done. I’m not going to do a post about how immoral and illegal this sort of behaviour is, for the exact reason that this post is about the corresponding fact that the huge majority of us already have the right instincts in place that tell us to expose and feel disgust for those thieves: I am increasingly annoyed at the growing number of people who blame humanity and people-en-masse for the behaviour of what is genuinely a few corrupt individuals.

A good starting point for what I want to say here, is this video about neuroeconomics. I’ll summarise it for you if you don’t feel like watching the full ten minutes and that big word n-word scares you. Basically 98% of us are not thieving bastards, the vast majority of us are good people (like Ryker Beck in the above links from her stream, and all the people who helped expose the thieves). The problem comes in because we often (and naturally) ignore when things go right – it would be a waste of our lives to say to everyone who doesn’t steal “hey yay, you didn’t steal”. But we of course notice thieves (and murderers, etc, you get the idea). So you can get a distorted view of the goodness of humanity – naturally skewed towards the bad because it makes sense to expose and notice the baddies among us.

The fact is we wouldn’t be able to function in society and live peacefully among each other at all, if the vast majority of us were evil or cruel or thieves. The enormous proportion of people I interact with everyday do not try to harm me in any way whatsoever, and wouldn’t even if I gave them the open invitation to do so.

There is an added element in anything Second Life related, where your fake name makes some people act the same way I think of drunk people: As having lower inhibition and a stronger tendency towards sexual and violent behaviour. People feel (what often turns out to be a false) sense of security behind that fake face, that fake name, and so do things they would never do in the “real world”. They test their limits – which is great in some regards because it helps us to discover ourselves in a safe environment seemingly without consequences. But just in the same way that you feel somewhat invincible when you’re drunk and think you can drive home safely or have unprotected sex with that cute guy and nothing will go wrong… things can and often do go wrong. Some of us learn our lessons and take responsibility, some of us don’t and lash out at others for revealing the mistakes we make – that is as true of Second Life actions as it is of real world actions.

But do we denounce all of humanity and human nature just because quite a few of us do stupid shit when we’re drunk – or in turn, because a fair few of us do irresponsible stuff when we think we’re safely hidden behind an anonymous avatar? Those conditions of an altered state of mind don’t “reveal” hidden truths about us as humans, they just show that people can be more inclined to make bad decisions under certain circumstances. Do we judge an animal’s true nature based on what it does when we drug it up..? Of course not! Even when we humans are drunk or using avatars, the majority of us are still OK people (in my extensive experience).

So sure there are some pricks out there, and hell yeah they should be exposed and made to feel a sense of shame and responsibility for their immoral and illegal actions. But try to refrain from judging all of humanity by those sorts of actions. Not least of all because if takes the focus for responsibility away from where it should be – with the individual concerned. Stop giving them the easy way out – “oh it’s just human nature”. Because it’s not. It’s just them. And remember now and then to make a big fuss over the good guys, to help remind yourself and others that humans aren’t such dreadful creatures after all. I’m not naive or stupid enough to think myself and my friends are they only good people in this world, don’t make the reverse mistake and generalise bad people as the norm either.

One of the most valuable insights of economics, and one of the oldest, is the idea that we value goods on a marginal basis.  The core of the idea, which traces back to Adam Smith, is that our choices are not about x amount of any good but x more of that good.  If you’re packing for a hike, you might find the idea of paying for a bottle of water ridiculous–there’s so much right there in the sink!–but easily shell out $5 for a tasty bag of trail-mix; six miles in and sucking on a dry Camelback, however, you’d quickly part with a similar sum to some bush-dwelling grifter peddling Aquafina.

In the broader neuroeconomic project of moving the determinants of economic choice from abstract theory into the circuits of the brain, an important step will be to account for the cause of this marginal calculation.  Despite its centrality to economic thought, however, previous studies have paid it little attention.  A paper published last month in The Journal of Neuroscience, however, reveals subtle signals in the brain regions previously known to drive economic choice that correspond to the computation of marginal value.

A group at the Wellcome Trust Center and University College, London lead by Alex Pine and Raymond Dolan examined the neural correlates of marginal and intertemporal difference in economic behavior.  They scanned the brains of their participants while they repeatedly chose between different amounts of money that would be available at different points of time in the future.  To ensure realistic decision-making, the subjects received a gift-card corresponding to their choice in a randomly selected trial that was activated at the prescribed time.

This paradigm has been used in many neuroeconomic studies before, but the researchers here engaged in a novel strategy of analysis to reveal the effects of marginal calculation on brain activity.  From the behavioral results, which revealed a clear pattern of diminishing marginal utility (for example, subjects cared less about the difference between $45 and $46 than they did about the difference between $5 and $6), they built an economic model for each participant that revealed his or her individual rates of marginal and intertemporal discounting.

By applying this model to the shifts in blood flow measured by fMRI, the researchers found different response patterns that correlated to separate economic factors.  The activity of some brain regions tracked only the pure dollar amount of each reward or the effects of delayed receipt, but a response within an area called the dorsal striatum–an area commonly implicated in economic valuation–correlated with the integration of these signals into a single unit of marginally and temporally discounted valuation.

That a circuit including the striatum is involved in decision-making is well established (and see here and here for other research drawing on that knowledge), but this study begins to bring into focus the subtleties of neural computation within this large structure that truly determine behavior.  The rapidly maturing field of neuroeconomics is already moving past where gross localization research is useful, and this paper is valuable for demonstrating how increasingly intricate microeconomic models can illuminate–and draw from–brain research.

Pine A, Seymour B, Roiser JP, Bossaerts P, Friston KJ, Curran HV, & Dolan RJ (2009). Encoding of marginal utility across time in the human brain. The Journal of neuroscience : the official journal of the Society for Neuroscience, 29 (30), 9575-81 PMID: 19641120

I found this article on neuroeconomics of interests.

The article has reviewed two general ways in which the neuroeconomic endeavor can make important contributions to research on decision-making – firstly, the incorporation into neuroscience and psychology of the formal, rigorous economic modeling approach, and secondly, the awareness within the economic community of the evidence for multiple systems involved in decision-making.

Some of the questions that would be of interest in our decision making include:

How do systems that seem to be focused on immediate decisions and actions interact with systems involved in longer term planning (e.g. making a career decision)?

Under what circumstances do these various systems cooperate or compete?  When there is competition, how and where is it adjudicated?

Psychologists, neuroscientists and behavioural economists all seem to agree that various automatic forms of behavior (including emotional responses) reflect the operation of a  multiplicity of mechanisms.  However, do higher-level deliberate processes rely similarly on multiple mechanisms, or a single, more tightly integrated (unitary) set of mechanisms?

My further questions include:

How would the above research help in understanding our decision making in networked learning?

How do we decide on the connections in networked learning? 

Doesn’t it make sense that:

• If you want to know what customers want, just ask them.
• If you want to see if they’re satisfied with the experience, just ask them.
• If you want to know if they’re come back or will refer you, just ask them.
• If you want to understand what you can do to improve, just ask them.

Listening to customers is critical for gaining insight into their lives, their goals, their needs, as well as, their frustrations, feelings, and behaviors.  Unfortunately, we’ve found that most structured “voice of the customer” research is not only ineffective for designing influential customer experiences, but it can seriously undermine innovation by directing investment at the wrong things.

It’s common for companies to conduct customer interviews, surveys, and focus groups trying to understand what customers want.   The reality is that what customers say they want is not often well-correlated with the subconscious factors that influence their behavior.  In many cases, what customers say they want is actually quite inconsistent with what ultimately drives their behavior.  The key is to able to engage customers in fundamentally different kinds of conversations and get beneath the surface of what they say to understand the deeper experiences they’re having.

I first encountered this disconnect about 25 years ago.  At the time, I was working with Dick Larson at MIT.  Dr. Larson is an expert in the psychology of waiting.   The situation involved commercial real estate managers responsible for several high-rise office buildings in New York.  These managers were trying to figure out how to address customers’ dissatisfaction with the amount of time spent waiting for elevators during peak periods.  Not surprisingly, if you ask customers what they want, they’ll tell you that they want an increase in service levels:  faster elevators and less waiting.  Obviously, the complexity and cost of actually improving service levels are quite high; it would involve installing faster elevators, dedicating more interior space to elevator banks, improving the optimization of elevator queuing, etc…   It turned out that the most effective improvement was to install mirrors in the elevator lobbies.  This allowed people to entertain themselves by fixing their hair, straightening their tie, and checking each other out in a much more socially acceptable way.  The perceived experience improvement was greater with the relatively low cost mirrors than with the relatively high cost technology required to improve actual service levels.  Note:  Waiting is an important aspect of many experiences, for more information about designing better waiting experiences see: Helping Customers Lose Wait.

In general, the design of influential experiences involves a trade-off between two strategies:  1) improve the reality of the events, service levels, etc… and/or 2) influence the way customers experience and act on those realities.   When you ask customers what they want or what they liked or didn’t like about their experience, what do they tell you?  In most cases, they only talk about the relatively obvious service levels associated with the first strategy.

Another example of this disconnect involves customers’ surface-level desires for more choice… compared with their subconscious distaste for actually having to make choices.  When conducting traditional voice of the customer research, customers often ask for a set of choices that allow them to find the alternative they prefer.  However, when presented with the range of choices uncovered in the research, the same customers find that actually making the choice exceeds both their level of motivation and capacity for processing information at the point of purchase.  In essence, giving customers the choices they request often leads to a “choice overload” that gets in the way of profitable customer behavior… in many cases, influencing them to postpone making a decision.

In one illustrative experiment, conducted by Iyengar and Lepper, consumers shopping at an upscale grocery store were presented with a tasting booth that displayed either a limited selection (6) or an extensive (24) selection of different flavors of jam.  The experimenters measured both customers’ initial attraction to the tasting booth and their subsequent purchase behavior.  While the extensive choice booth attracted more customer attention, customers presented with the limited set of choices were 10 times more likely to make a purchase.  Customers that sampled from the limited choice booth made a purchase 30% of the time versus only 3% of the time from the extensive choice booth. Leading companies are really starting to internalize this finding.  P&G, for example, reduced the number of versions of Head and Shoulders shampoo from 26 to 15, and, in turn, experienced a 10% increase in sales.

Voice of the customer research makes the underlying assumption that people have a relatively stable, conscious, explainable, and at least somewhat consistent set of preferences.  It also makes the assumption that when ask customers about their preferences they can tell you or, in some cases, when you present them with a set of forced choice trade-offs (e.g., would you prefer to buy A or B), how they choose will reflect what they do in real life.  Unfortunately, this is far from true.  People typically don’t know what they want until they see it; they construct their preferences and work through decisions as they perceive their alternatives in the actual purchase environment.  Subtle differences in the design of that purchase environment can have a significant impact on the decisions customers make.  In fact, research in the areas of cognitive psychology and behavioral economics has shown that…

…small and seemingly insignificant contextual details have a major impact on people’s behavior.

One of my favorite recent examples comes from MIT Professor Dan Ariely.  (See Dan’s great book:  Predictably Irrational)  Dan came across the following advertisement for The Economist:

The Economist Subscription Options

The ad offered three subscription options:

• Electronic Only: $59
• Print Only: $125
• Electronic and Print: $125

Which of these options do you think people would choose?  Why would anyone choose the “Print Only” option rather than opting for the additional “FREE!” electronic subscription?  It seems very unlikely!  In fact, Ariely conducted a test with 100 Sloan School students and only 16 chose “Electronic Only” while 84 chose the “Electronic and Print” option.  No one chose the “Print Only” option! On the surface, this option seems totally irrelevant.  Why would you even offer it?   It turns out that something very interesting happens when this seemingly irrelevant option is eliminated.  When another 100 students were offered only two choices: “Electronic Only” and “Electronic and Print”, 68 chose “Electronic Only” while only 32 chose “Electronic and Print.”

The presence of an irrelevant option influenced a more than 250% increase in customers choosing the more expensive alternative!!!

Ariely observed the following, “Thinking is difficult and sometimes unpleasant.” Cues that allow us to establish the relative value of various offerings, then, reduce the cognitive load or effort required to think about your options.  What the Economist offered was a no-brainer; while we can’t be certain that the print subscription is worth more than twice the electronic version, the combination of the two was clearly worth more that the print version alone.

In another illustrative example of how subtle environmental details influence customer behavior, Cornell University researchers Sybil S. Yang, Sheryl E. Kimes, and Mauro M. Sessarego found that by dropping the “$”symbol on a restaurant menu can have a significantly positive impact on the total ticket value.  The researchers did a side by side comparison of three ways of presenting menu prices: with a preceding dollar sign (e.g., $14.95), without a dollar sign (e.g., 14.95), and as written out prices (fourteen dollars and 95 cents).  Aside from the subtle differences in price presentation, all other aspects of the actual pricing and customer experience were held constant.  They found that the average total ticket increased by $3.70 when prices were presented without the dollar sign.  They also found that the average ticket decreased by $1.85 when prices were written out.

All of these examples illustrate a level of insight into the way people have experiences and act on their experiences that cannot be accessed by most  traditional, structured voice of the customer research.

The Vast Majority of Human Experience is Subconscious

Every waking second of the day, each of us processes just over 4,000,000 bits of sensory information.  At the same time, we get to pay conscious attention to only 7+/- higher level and relatively abstract notions about what’s happening to us, what we’re doing or planning to do, and how we’re feeling about all of this.  Luckily our brain does an outstanding job of filtering, predicting, and prioritizing all if this information in a way that makes it possible for us to be reasonably effective in the world.  The challenge is every normally functioning human being on the planet lives in a state of “naïve realism.”  This naïve realism, gives us the sense that we’re experiencing our surroundings as they actually are, rather than just as a high level abstraction of what we believe them to be.

If we are asked by a researcher to describe an experience, particularly an experience we had at some point of time in the past, the best we can do is relate what we think we remember, about how we believe we felt, along with the alibis we construct for the choices we made, in an experience that was almost entirely subconscious.  However, due to the state of naïve realism we live in, we’re convinced that our explanations have merit… despite the fact that we are just reconstructing a plausible sounding story for what we think happened.  This is the way it works for all of us.  It’s also the fatal flaw for most structured, traditional voice of the customer research.

Understanding how to design highly meaningful, differentiated, influential, and profitable experiences involves engaging people in fundamentally different sorts of conversations and listening in ways that get beneath the surface of what they say to understand the deeper, subconscious aspects of how  people actually have experiences.

While there’s value to listening to customers’ recollections of the experiences they’ve had and their suggestions for improving that experience, what you really need to look for and understand are:

1. Goals and Desired States
   • What set of desired states and goals are people really trying to accomplish?
   • What kinds of experiences are people attracted to and comfortable engaging with?
2. Beliefs and Expectations
   • How do people make sense of and remember the experiences they have?
   • How do people construct situation-specific expectations and preferences?
3. Emotional States and Triggers
   • What conscious and subconscious emotional states influence peoples’ actions?
   • How do specific events trigger emotional reactions that influence behavior?
4. Natural Behavioral and Decision Pathways
   • What behavioral pathways do they naturally follow to accomplish their goals?
   • How do people make choices in light of these expectations and preferences?

We’ve developed an innovative toolset for answering these questions. Experience Miner^TM provides a rigorous way of capturing and analyzing the most critical aspects of the way people think, feel, and act  on their experiences.  It involves a fundamentally different way of listening to what people say and watching what they do in order to identify what’s going on beneath the surface.  Built on 25 years of research into the cognitive, affective, and behavioral basis of experience, it provides the specific insight required to focus design and delivery efforts on the areas of greatest influence and financial return.   Experience Miner^TM is used to identify the most influential experience elements for each target customer personae.  This insight is used to 

…design evocative experiences from the mental model of the experiencer.

The Experience Miner^TM toolset consists of the following seven elements, each designed to fill in a critical piece of insight required to design experiences that influence behavior.

• Goal Space Mapping^TM – Describes the desired states and situation-specific goals that motivate and direct the experience for each key persona
• Experiential Temperament^TM - Profiles how temperamental differences influence the way people are drawn to and engage with novelty seeking, harm avoidance, social orientation, and persistence
• Framing Metaphors – Surfaces the underlying physical metaphors people use to interpret, evaluate and act on their experiences in the relevant domain(s).
• Experiential Constructs^TM – Identifies the most common, learned distinctions that enable people to recognize, categorize, differentiate, and form expectations.
• Emotional States and Triggers^TM -  Surfaces the emotional states and specific triggers across the lifecycle of the experience highlighting areas of uncertainty, stress, frustration, etc…
• Experiential Pathways^TM – Maps the end-to-end set of activities and choice points that people follow in pursuit of their goals… including the unwritten rules and automatic behavioral scripts people apply along this pathway.
• Experiential Choice Dynamics^TM – Describes the situation-specific choice processes that people follow, as well as, how they construct preferences and make decisions that influence their behavior.

If you’re interested, I’ve covered various topics related to the elements of Experience Miner in a wide range of other posts, including:

• Experience Miner: Creating Profitable, Evocative Experiences
• Whatever You Do… Don’t Confuse Experience with Reality
• Understanding Basic Drives and Experiential Temperament
• Making Experiences Memorable
• Customer Experience and Our Search for Meaning
• Designing “Socially Influential” Experiences
• Designing for Customers’ Reactive, Deliberative, and Reflective Experiences
• Choice Architecture: Designing Experiences that Influence Customer Behavior
• Optimizing the Most Critical Elements of the Customer Experience: Customer Choices
• Framing and Priming the Customer Experience
• Automatic Behavioral Scripts: Don’t Overestimate Your Customers’ Interest in Having an “Experience” with You

Most of the time and money organizations invest on customer experience is wasted…

… because they focus on how the organization “delivers the experience”…

… rather than on how customers actually “HAVE the experience”…

… and how those experiences influence behavior!

Most customer experience efforts are based on touch-point oriented approaches that define the experience in terms of a customers’ interactions with the company.  These approaches are inherently company-centric and, at best, lead to improvements that create “better sameness.”  The fact is:

Customers’ experiences do not just happen at your organizations’ touch-points.

Evocative Experiences… The Experiences that Matter

An experience is evocative when it positively and profitably influences:

• What people think (cognitive outcomes)
  • What they remember about their experience
  • The story they tell themselves and others about their experience
  • The distinctions they draw that differentiate what you did for them
• How people feel (affective outcomes)
  • How doing business with you makes them feel about themselves
  • How the way they feel about themselves drives how they feel about you
  • What specific emotional states and triggers motivate behavior
• What people do (behavioral outcomes)
  • Making additional purchases
  • Diversifying what they buy from you
  • Telling stories about their experience with you
  • Recommending you to others
  • Behaving more cost effectively
  • Adopting new product, service, or process offerings

Four Characteristics of an Evocative Experience

1. Are immediately simple to understand and easy to navigate. The vast majority of peoples’ experiences are accomplished using a combination of “gist processing” and “automatic behavioral scripts.” Well-designed experiences fit easily with the mindsets and natural behaviors people have for the problem they’re trying to solve. Note: As a result of being designed around automatic behavioral scripts, evocative experiences can have a surprising subconscious influence on behavior.
2. Offer innovative solutions to peoples’ latent problems. Well-designed experiences start with a deep understanding of what people are trying to accomplish and provide solutions to problems, accomplish goals, and address needs that people may not even realize they have or be able to easily describe. These innovative solutions almost never occur at the existing company touch-points.
3. Tell a compelling and memorable story. People perceive, interpret, and recall their experiences using stories. Well-designed experiences tell a story that has a clear and distinctive message that resolves conflict using a small number of high-contrast, signature experience elements. These signature experience elements get people’s attention and are perceived as a meaningful differences in kind… rather than incremental differences in degree.
4. Trigger specific emotional states that influence behavior. The most influential experiences are designed to influence how people feel… not about the company… but about themselves. The specific emotional state(s) associated with the experience are chosen as the precursors to the behavior the experience is intended to generate.

Creating Evocative Experiences

In order to create evocative experiences you must start with an “experiencer-centric” rather than “company-centric” definition of experience.   We define an experience to be:

Experience:  A person’s cognitive, affective, and behavioral reactions… across the end-to-end process they follow… in order to realize a desired state, satisfy needs, and accomplish goals that are important to them.

This is fundamentally different than the typical company-centric definition:  Customer experience is the sum or all interactions a customer has with a supplier of goods or services, over the duration of their relationship with that supplier.

Experience Miner^TM and the Design of Evocative Experiences

The objective of any product, service, or experience design is to profitably and powerfully influence how people think… how people feel… and, most importantly, how people act.   Most organizations’ efforts fail to achieve this objective because they focus on how their organization “delivers” an experience rather than how people actually HAVE experiences.  As a result, organizations routinely over-invest in incremental improvements that deliver “better sameness” at the existing touch-points.  In the course of doing so, these organizations miss the fact that customers’ experiences don’t just happen at their touch-points.   Although these investments may have a marginal impact on reported satisfaction, they often don’t lead to any measurable change in behavior in the face of changing customer needs, priorities, expectations, and alternatives.  In order to positively influence customer behavior, experiences must be designed and delivered with a deep understanding of how people actually HAVE experiences.  For more information on this, see:  Getting Beneath the Voice of the Customer

Experience Miner^TM provides a rigorous way of capturing and analyzing the most critical aspects of the way people think, feel, and act  on their experiences.  Built on 25 years of research into the cognitive, affective, and behavioral basis of experience, it provides the specific insight required to focus design and delivery efforts on the areas of greatest influence and financial return.   Experience Miner^TM is used to describe the key elements for each target customer personae.  This insight is used to 

…design evocative experiences from the mental model of the experiencer.

The Experience Miner^TM toolset consists of the following seven elements, each designed to fill in a critical piece of insight required to design experiences that influence behavior.

Goal Space Mapping^TM – Describes the desired states and situation-specific goals that motivate and direct the experience for each key persona

Experiential Temperament^TM - Profiles how temperamental differences influence the way people are drawn to and engage with novelty seeking, harm avoidance, social orientation, and persistence

Framing Metaphors – Surfaces the underlying physical metaphors people use to interpret, evaluate and act on their experiences in the relevant domain(s).

Experiential Constructs^TM – Identifies the most common, learned distinctions that enable people to recognize, categorize, differentiate, and form expectations.

Emotional States and Triggers^TM -  Surfaces the emotional states and specific triggers across the lifecycle of the experience highlighting areas of uncertainty, stress, frustration, etc…

Experiential Pathways^TM – Maps the end-to-end set of activities and choice points that people follow in pursuit of their goals… including the unwritten rules and automatic behavioral scripts people apply along this pathway.

Experiential Choice Dynamics^TM – Describes the situation-specific choice processes that people follow, as well as, how they construct preferences and make decisions that influence their behavior.

Are you losing too many customers or sales opportunities?    Are you experiencing too much negative word of mouth?    Are customers’ expectations changing faster than your company’s ability to stay ahead of the competition?    Do you have trouble aligning the efforts of intermediaries in order to deliver for the customer?    Are customers behaving in a way that constrains or undermines your efficiency and profitability?    Are all your efforts just leading to “better sameness”?

Over the past couple of years, I’ve covered an extensive array of topics focused on how companies can address these issues.  In this post, I’d like to take the liberty of  describing the type of work we do and the unique tools we use in the process.

My colleagues and I at Customer Innovations have a 25 year track record helping leading organizations create experiences that improve the acquisition, retention, and profitability of customers.  In the course of our work, we’ve demonstrated bottom line results of 10-25% in the form of increased retention, incremental sales, reduced acquisition costs, positive word of mouth, higher price realization, and improved productivity of customer-facing operations.   Most of our work has been with organizations that create experiences across complex networks of “customers” including consumers, agents, brokers, retailers, and other influencers.

Our work generally takes the form of these types of efforts:

• Rapid Revenue Retention. We quickly identify specific elements of the current experience that are leading to attrition, lost sales, negative word of mouth, and unproductive customer behavior.   Intensive 10-12 week efforts often lead to $10 – $100 million in benefits.
• Accelerating Sales From the “Outside In”. Rather than starting with the internal structure, processes, tools, and training, we start with a deep understanding of how and why your customers buy and then focus improvements on shifting buying behavior.
• Creative Customer Insight. Without breakthrough customer insight, design efforts can only produce “better sameness.”  We have a unique approach to surfacing customers’ latent motives, beliefs, needs, and priorities in a way that informs the creation of highly evocative and profitable products, services, and experiences.
• Signature Experience Design. We design, deliver, and engage customers in experiences that capture their attention and influence the actions they take.  These evocative experiences are structured to tell a meaningful and influence customer behavior using a set of differentiated “signature experience” elements.
• Aligning Effective Employee and Intermediary Experiences. We help create the specific employee and intermediary experiences required to ensure that those who work directly or indirectly with your customers reinforce the intended evocative experience.

We Have a Unique Technology for Creating Experiences that Influence Customer Behavior

Traditional touch-point oriented approaches rarely deliver more than “better sameness” because they focus on how the organization delivers an experience rather than on deeply understanding how people actually have experiences and how those experiences influence behavior.   Customer Innovations has a unique approach and toolset for designing evocative experiences that positively and profitably influence behavior. 

• Experience Miner^TM – Traditional “voice of the customer” approaches are insufficient for understanding the largely subconscious processes that influence customers’ desires, preferences, emotional states, choices, and behavior. Based on 25 years of cognitive and behavioral research, the Experience Miner^TM toolset helps surface, analyze, and measure the ways customers think about, feel about, and act on their experiences.
• Experience Designer^TM – The output from Experience Miner^TM feeds our structured Experience Designer^TM toolset that guides every step of the experience ideation, concept development, specification, and blueprinting processes.  Experience Designer^TM also incorporates an integrated experience-chain framework that helps specify and design the specific employee and intermediary experience interventions required to generate the intended customer experience.
• Experience Economics^TM – It’s exceptionally easy to deliver an uneconomic experience.  Most organizations simultaneously over-invest in elements of the experience that don’t matter to customers and under-invest in elements that have significant influence on customer behavior.  The Experience Economics^TM toolset helps companies find the optimal investment point based on the influence that individual and collective experience design elements and service levels have on the financial performance of the business.

I’ll continue to expand on these tools in upcoming posts.   In the meantime, you might want to check out the following links:

• Rapid Revenue Retention: A “Swarming” Approach to Keeping Customers During Recessionary Conditions
• When the Going Gets Tough… The Tough Get Closer to Their Customers
• Choice Architecture: Designing Experiences that Influence Customer Behavior
• Designing Experiences that Fit the Customers’ Mental Model
• Integrating Customer and Employee Experiences
• A Break in the Service Profit Chain: Why Increases in Employee Engagement Don’t Improve the Customer Experience

If you’d like any more information, just post a reply or send me a note at fcapek (at) customerinnovations (dot) com.   Cheers, Frank

In 1802, in a letter to then Secretary of the Treasury, Albert Gallatin, Thomas Jefferson warned that, “If the American people ever allow private banks to control the issue of their money, first by inflation and then by deflation, the banks and corporations that will grow up around them (around the banks), will deprive the people of their property until their children will wake up homeless on the continent their fathers conquered.” (source)  Although the US now does have a central government bank, Jefferson’s warning still chillingly echoes through our current crisis as we teeter on this very brink.

The reasons why the US financial system lies stricken now (not to mention many times before) are complex for sure, but for a neuroscience & genetics buff like myself, its fun to consider the underlying mechanisms of human biology and behavior within a macroeconomic framework.  What role for the brain and human nature? How does our understanding of human social and emotional behavior reconcile with the premise of so-called “rational” behavior of investors and consumers in a marketplace? Can we regulate and design a debacle-proof economic system that accounts for human social and emotional influences on otherwise rational behavior? Luckily, if you are interested in these questions, you need only to pick up a copy of “Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism” by George Akerlof and Robert Shiller, who cover this very topic in great detail and provide a broad framework for neuropsychological research to inform macroeconomic policy.  A lofty and distant goal indeed, but perhaps the only way forward from such spectacular wreckage of the current system.

One such aspect of so-called “animal  spirits” could be, for example – fear – which has been blamed many times for financial panics and is covered in great measure by Akerlof and Shiller.  During the depths of the great depression, FDR famously tried to shake people loose from their animal spirits by suggesting “Only Thing We Have to Fear Is Fear Itself” (listen to the audio).   As another example, consider the chart at the top of the post – a 5yr trace of the VIX an index of volatility in the price of stock options over time.  In a bull or a bear market, when there are clear economic signals that stock prices should rise or fall, the VIX is rather low – since people feel relatively certain about the overall direction of the market.  Note however, what happened in the fall of 2008, when the heady days of the housing boom ended and our current crisis began – the VIX rockets toward 100% volatility – indicating rather dramatic swings in future earnings estimates and hence, tremendous uncertainty about the future direction of the market.  Indeed, for high flying investors (who may reside in tall buildings with windows that open) the VIX is sometimes referred to as the fear index.

What – in terms of brain mechanisms – might underlie such fear – which seems to stem from the uncertainty of whether things will get better or worse?  What do we know about how humans react to uncertainty and how humans process uncertainty?  What brain systems and mechanisms are at play here? One recent report that uses genetic variation as a tool to peer into such brain mechanisms suggests that dopamine signaling modulates different brain areas and our propensity to respond in conditions of low and high uncertainty.

In their article, “Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation“, [doi:10.1038/nn.2342] Michael Frank and colleagues examine individual differences in a so-called exploration/exploitation dilemma.  In their ‘‘temporal utility integration task’’, individuals could maximize their rewards by pressing “stop” on a rotating dial which can offer greater rewards when individuals press faster, or when individuals learn to withold and wait longer, and, in a third condition when rewards are uncertain.  The authors liken the paradigm to a common life dilemma when there are clear rewards to exploiting something you know well (like the restaurant around the corner), but, however, there may be more rewards obtained by exploring the unknown (restaurants on the other side of town).  In the case of the VIX and its massive rise on the eve of our nations financial calamity, investors were forced to switch from an exploitation strategy (buy housing-related securities!!!) to an exploration strategy (oh shit, what to do?!!).

The neurobiological model hypothesized by Frank and colleagues predicts that the striatum will be important for exploitation strategies and find supporting data in gene associations with the striatally-enriched DARPP-32 gene (a marker for dopamine D1-dependent signalling) and DRD2 for the propensity to respond faster and slower, respectively, in the exploitative conditions (rs907094 & rs1800496).  For the exploratory conditions, the team found an association with the COMT gene which is well-known to modulate neural function in the prefrontal cortex (rs4680). Thus, in my (admittedly loose) analogy, I can imagine investors relying on their striata during the housing boom years and then having to rely more on their prefrontal cortices suddenly in the fall of 2008 when it was no longer clear how to maximize investment rewards.  Egregious bailouts were not yet an option!

Click here and here to read more breakthrough neuroeconomics & genetic research from Michael Frank and colleagues.  Here and here for more on Shiller and Keynes.

Dear all,

We are currently running a survey on people’s attitudes towards neuromarketing and related topics. We hope that you will all take this survey, as well as shard this link to as many people as you like. We hope to get as many people’s opinion as possible, and report the results through appropriate channels (i.e. journal paper, Master’s paper, and online here)

The original text and link goes as follows:

NEUROMARKETING SURVEY

We would like to invite you to take part to this survey. Your answers will help to gather information about the perceptions and thoughts about the use of brain science methods in non-medical settings.

CONFIDENTIALITY

Any information that you provide will be confidential. All participants will be anonymous such that no personal information concerning you or your company will be made public either during, or after the completion and release of this study. The questionnaire should take about 10 minutes of your time. If you wish to receive a summary of the results (that you can pass on to your home company) please indicate at the end of this questionnaire and include your e-mail address. We will not use this e-mail for other purposes than for sending you the summary.

WHO IS BEHIND THIS STUDY

My name is Matteo Bellisario, and I am completing my final report for my Master Degree in Strategic Market Creation at the Copenhagen Business School, in Copenhagen, Denmark.

My academic supervisor for this research is Dr. Thomas Z. Ramsøy, head of the Decision Neuroscience Research Group at the Copenhagen Business School and Danish Research Centre for Magnetic Resonance at Copenhagen University Hospital.

The results will be part of my Master Thesis, and may, if suitable, be submitted to a peer-reviewed journal.

PLEASE CLICK ON THE LINK BELOW TO START THE SURVEY

http://www.surveymonkey.com/s.aspx?sm=UrWT2vR9gSIXNSKooLsNuQ_3d_3d

-Thomas

A nice summary with Colin Camerer, Ralph Adolphs, etc.

Thanks to Investing Pinoy for the link.

While the rest of the country strips Sarah Palin of her humanity for no good reason except that mobbing is so much fun, something very weird is going on with Goldman-Sachs.  There are a number of credible sources reporting now on the arrest of Sergey Alenyikov on July 4, 2009 at Newark Airport.  Mr. Alenyikov is accused of stealing G-S source code and uploading it to a computer somewhere in Europe.  Goldman sent the Feds to round up the rogue programmer because, as the US Attorney on the case says, the source can be used to manipulate the market.  Daytraders who monitor the market very closely say that they noticed some non-linear activity in the several weeks leading up to the arrest and that afterwards, the markets seemed to have returned to normal, whatever that means since last year’s crash.

But it gets more interesting.  As Bloomberg notes, how do we know that Goldman-Sachs hasn’t been using this code all along to manipulate the markets?  The theory goes something like this: Goldman-Sachs gets to put a unix server with the code on a network cable somewhere and uses a packet sniffer to watch the transactions that come across from traders at other companies.  With that knowledge, G-S is able to anticipate trades and shave a bit off for itself for every transaction.  If this is true, it means that G-S has a massively unfair advangtage in trading compared to, well, just about everyone else. Here’s a post at the Big Orange Cheeto for those of you who eat netstats for breakfast and think Perl poetry is romantic.  It goes into quite a bit of technical detail.

Check out this video from Bloomberg that lays it all out:

What’s really disturbing about this case is that the government seems to be extraordinarily receptive to calls for help from G-S.  Either the Goldman crew close to the White House is concerned with losing their bonuses or the potential for market failure is huge. Either way, it lessens confidence in the system.  It looks like “all traders are equal except some traders are more equal than others”.  What happens to the system when the people who have no choice but to operate in it no longer have any trust?  This leads me to my podcast du jour recommendation.  This one is from Speaking of Faith with Krista Tippet.  Her topic this past week was The Science of Trust with guest Chris Farrell, a neuroeconomist.  The secret to keeping everyone honest is the neurotrsnsmitter hormone oxytocin.  Oxytocin promotes empathy.  Approxomately 2% of the population has insufficient levels of oxytocin to experience empathy with other human beings(and you’re probably thinking they all work at Goldman-Sachs, right?)

Farrell says that the lax regulatory system is partially responsible for the financial disaster, although he takes a pretty long time before he gets around to saying it.  Farrell thinks that for *most* people, *most* of the time, the feelings of empathy lead to a sense of reponsibility and honesty.  That’s why you might have felt you could trust your banker with your money.  But in recent years, there have been advances in technology that lead to a depersonalization of the banker-client relationship.  It’s hard to see the person behind an account number on a monitor.

The podcast is part of a series by Tippett on Repossessing Virtue, all highly recommended.  You don’t have to be religious to have an ethical model.  But ethics, virtue and trust are all severely lacking in this dog eat dog world where nothing much makes sense.  Societies start to unravel when the sense of morality and consideration for others is replaced by, well, nothing at all.

If Tippett is concerned with repossessing virtue, I guess you could say that the fundamentalist group, The Family, is concerned with redefining it.  A second podcast recommendation is one that Terry Gross did last week on The Family, a religious community in DC that has been the spiritual guiding force for many enemies of The New Deal over the decades.  John Ensign was a recent alumni as was Governor Sanford of South Carolina.  The Family believes the end justifies the means and that chosen Family members are possessed of the virtue that requires no further regulation by the government.  They will lead us because it is their destiny and if they cheat on their wives on Family property, it must mean that God works in mysterious ways.  Caveat: Terry Gross is the best interviewer in the world but she has kool-ade psychosis and it is unfortunately seeping into her interviews.  “The propaganda is strong in this one.”  Proceed with caution.  Her bias shows.  Even so, if this group is only 1/10th as bad as the interview suggests, it’s pretty bad.

Update: It looks like Citadel is getting in on the act now.  The former head quant at Citadel started a new company called Teza Technologies and hired Aleynikov. Mikail Malyshev signed a 9 month noncompete clause when he left Citadel back in February.  That means he wasn’t supposed to open his door until November.  Maybe it’s all a preparation phase thing.  Teza is cooperating with the FBI. This stuff is starting to sound familiar.  Several of my former colleagues (all Russians BTW, tho’ I’m sure it’s all a coincidence) have gone to work for quant firms, though I can’t imagine they’ll be writing code.  Who knows?

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