Archive for December, 2015

Ovid Today App

OvidToday, an easy-to-use app for your library’s Ovid-subscribed journals, is aimed at providing you with the latest and most-relevant content. This app – available for iPad® and Android™ devices – offers instant access to browse through your institution’s Ovid journals – including multiple issues and Publish Ahead of Print. Plus, users can read or save PDF articles.

Browse ALL Your Institution’s Ovid® Journal Holdings – search by journal name, A-Z or by category to find journals that best fit any specialty area
Intuitive Interface and Navigation – made with the busy user in mind to make reading journal articles a simple experience
Personal Reading List – users can create a list of journals to follow, even offline, that is most relevant to their research, practice and education
Stay Up-To-Date – the latest content from the institution’s subscribed journals is instantly available on the app once on Ovid – including Publish Ahead of Print
Read Articles Anywhere – Readers can up to 6 months-worth of rolling content and PDF articles while on-the-go – access at work or at home with remote authentication

How can you access the Ovid Today App?

Step 1: Download the free iPad app from the App store
Download the free Android app from Google Play

Step 2: Start your Cisco AnyConnect Client

Step 3: Click on “Let’s Get Started” and create a personal account

See details here

Misbehaving

Richard Thaler‘s new book Misbehaving is a good read and a very entertaining account of the development of behavioural economics, from rogue subdiscipline to integral part of mainstream economics. The book reads like a memoir of Thaler’s life and career, beginning with Thaler’s start as a promising graduate student in Economics, up until he became a key policy advisor (think Nudge and “Nudge Unit“) and president of the American Economic Association.

The book could be a good Christmas gift to those interested in the (short) history of the behavioural sciences and the marriage between psychology and economics!

Thorsten Hens

hens.thorsten

This week, Prof. Dr. Thorsten Hens, Professor of Financial Economics, University of Zurich, will visit us and give a talk at the SWE colloquium (abstract follows).

Designing a risk profiler: Which measures predict risk taking?

In this paper we assess the suitability of different risk profiling measures when individuals are involved in a process of discovering their willingness to take risks over different decision modes. The latter involve decisions under ambiguity, decisions after gaining experience and receiving outcome information on previous decisions. We find that risk taking is associated with individuals’ risk preferences in all decision modes but not with their investment experience. Although simulated experience improves the risk awareness and supports a higher risk taking, it cannot substitute the assessment of risk preferences and in particular the assessment of individual’s loss aversion. In contrast, self-assessed risk tolerance measures are not suitable for predicting risk taking in any decision mode. If risk preferences cannot be assessed, only the gender can be used as a predictor of risk taking.

New acquisition of the library: Friend & Foe: When to Cooperate, When to Compete and How to Succeed at Both

friend or foe.jpg

Do we achieve our best outcomes by competing or by cooperating? This question has fueled a long-running debate. Some have argued that humans are fundamentally competitive and that pursuing our self-interest is the best way to get ahead. Others argue that humans are hardwired to cooperate and that we are most successful when we collaborate with others.

In FRIEND AND FOE, researchers Galinsky and Schweitzer explain why this debate misses the mark. Rather than being hardwired to compete or cooperate, humans have evolved to do both. It is only by learning how to strike the right balance between these two forces that we can improve our long-term relationships and get more of what we want.

Galinsky and Schweitzer draw on original, cutting edge research across the social sciences to show how to to maximize success in work and in life by deftly navigating between cooperation and competition. They offer insights into topics ranging from how to get and keep power, how to recognize deception and build trust, how to turn our weaknesses into strengths, and when to begin a negotiation to get the best outcome – while ensuring that our counterparts walk away wanting to negotiate with us down the road.

And along the way, they pose and offer surprising answers to a number of perplexing puzzles: when does too much talent undermine a team’s or company’s success; when can acting less competently help you gain status, why many gender differences in the workplace may simply be power differences in disguise; why ending an auction at 2am can get you the best outcome; how our best intentions can ironically make us appear racist; and why husbands gain weight during pregnancy.

We perform at our very best when cooperation and competition are held in the right balance. This book is a guide for better navigating our social world by learning when to cooperate as a friend and when to compete as a foe—and how to be better at both.

Maurice Schweitzer and Adam Galinsky discuss their new book in a videocast.

Table of contents

Shelf number: ig 48596

See you at the library!

Rafael Polania

Rafael Polania is visiting us this week and giving a talk in the SWE colloquium (abstract below). Rafael is a post-doctoral researcher in the group of Christian Ruff at the Economics Department of the University of Zürich.

Perceptual vs. preference-based decisions

Two common types of decisions are particularly prevalent in our daily life: perceptual decisions, where organisms make decisions on the basis of objective states of the world (e.g., melons are bigger than apples), and preference-based decisions, where organisms make decisions on the basis of preferences (e.g., I prefer apples to melons). It has been recently proposed that there must exist some type of computational overlap between these two types of choices, but surprisingly, previous studies have only investigated these two types of decisions in isolation and have used very different experimental paradigms, stimuli, and response options. It therefore remains virtually unknown whether perceptual and value-based decisions are indeed controlled by a unified computational mechanism and whether this mechanism depends on similar or distinct neural processes in the human brain. We developed novel paradigms that allowed us to identify common and distinct neural mechanisms of perceptual and value-based decisions by explicit comparisons of neural activity during both types of decsision taken on identical stimuli and involving the same motor output. The combination of this behavioral paradigm together with fMRI, EEG recordings and computational models of choice offered a uniquely clear view on the neuro-computational processes underlying decision-making by showing that decisions emerge from an integrative evidence accumulation process occuring in parallel across distinct brain regions that process different aspects of the incoming sensory signals. Moreover we showed that these processes are instantiated locally by neural oscillations and are coordinated between different areas via large-scale oscilliatory synchronization.

Why smarter people are quicker on simple tasks: The tale of a blind, pre-registered analysis

(If you find IQ and RT boring, skip to paragraph 3, “How can one test…”)

The smartest people also tend to perform best on simple tasks, such as detecting the direction of an arrow. This effect has been interpreted as evidence that the same elementary processing capacity underlies behavior on both tasks. Interestingly, the association between higher order cognitive ability (IQ, working memory) and simple task-performance is most pronounced when the simple task-performance is summarized by people’s slowest responses: People’s 10% slowest response times are more predictive of their intelligence than their fastest 10%. This phenomenon is called the “worst performance rule”.

The diffusion model offers a very simple explanation for the worst performance rule. The diffusion model postulates that response time and accuracy in a simple multi-trial task are the result of one process that is governed by a number of parameters. One of those, the drift rate, quantifies the rate of information processing. The higher this drift rate, the quicker and more accurate the responses. More crucially for the worst performance rule: Drift rate affects mostly slow RTs. So, it seems that drift rate quantifies the elementary information processing speed that underlies IQ, working memory, and simple task performance. In our study, we use the very large data set of the Basel-Berlin Risk Study (a large study designed to understand the biological foundations of risk taking) to seek confirmative evidence for the connection between simple task drift rate and working memory capacity.

How can one test this (or any) effect in a truly confirmatory fashion?

First, we would want to publish the results irrespective of the outcomes (that is: prevent publication bias). Therefore, we chose to submit this study to an appropriate journal, specifically, the journal Attention, Perception and Psychophysics (AP&P) as a “registered report”. This new type of research report implies that you submit your method and analysis plan before carrying it out. This plan gets reviewed. If they like it, they commit to publishing it — no matter what you find — as long as you do precisely what you planned (BTW, we just passed this stage, woohoo!).

Second, we didn’t want to be able to fool ourselves (that is: prevent experimenter bias). Therefore, we came up with a pretty inventive blind analysis plan: The person who carries out the diffusion model analyses (modeling also the correlation of the drift rate with working memory capacity) gets the freedom to tweak around with the data until the model works and fits. However, there’s a twist: the working memory capacity variable is shuffled. This means that the modeler cannot be influenced by his own expectation of a positive correlation between drift rate and working memory. Once the model is bug-free and the modeler is happy, he will post the code on the Open Science Framework website. Only then we supply him with the unshuffled version of the working memory variable.

I am very curious about the results. The paper is on my website, waiting for the results section.

Hrvoje Stojic

Hrvoje Stojic, a researcher at the Department of Economics and Business, Universitat Pompeu Fabra, Barcelona will be visiting us this week and giving a presentation in the SWE Colloquium (abstract follows).

Exploration and generalization in multi-attribute decision making

Reinforcement learning (RL) models have been successful in explaining human and animal learning and decision-making. However, the experimental tasks often lack crucial characteristics of realistic decision situations: high-dimensional alternatives where features are informative of the alternative value. We developed a contextual multi-armed bandit task where participants choose repeatedly between multiple alternatives where payoffs were a function of two informative features. We report results of several experiments where the payoffs of the alternatives were governed by different type of functions. We find that although people seem to learn the function rather well, they do not exploit fully this knowledge and stick too much to the alternatives they know well. This is supported by the fit of the hybrid model that combines naïve RL model that ignores feature information and pure function-based RL model. Moreover, we examine exploration patterns of participants and find evidence that they are strategic in their exploratory choices: they choose alternatives to learn the function better, not necessarily to learn about the specific alternative.

Sozial-, Wirtschafts- und Entscheidungspsychologie

Social, Economic, and Decision Psychology Blog