Welcome to the Nexus of Ethics, Psychology, Morality, Philosophy and Health Care

Welcome to the nexus of ethics, psychology, morality, technology, health care, and philosophy
Showing posts with label Reinforcement. Show all posts
Showing posts with label Reinforcement. Show all posts

Saturday, February 23, 2019

The Psychology of Morality: A Review and Analysis of Empirical Studies Published From 1940 Through 2017

Naomi Ellemers, Jojanneke van der Toorn, Yavor Paunov, and Thed van Leeuwen
Personality and Social Psychology Review, 1–35

Abstract

We review empirical research on (social) psychology of morality to identify which issues and relations are well documented by existing data and which areas of inquiry are in need of further empirical evidence. An electronic literature search yielded a total of 1,278 relevant research articles published from 1940 through 2017. These were subjected to expert content analysis and standardized bibliometric analysis to classify research questions and relate these to (trends in) empirical approaches that characterize research on morality. We categorize the research questions addressed in this literature into five different themes and consider how empirical approaches within each of these themes have addressed psychological antecedents and implications of moral behavior. We conclude that some key features of theoretical questions relating to human morality are not systematically captured in empirical research and are in need of further investigation.

Here is a portion of the article:

In sum, research on moral behavior demonstrates that people can be highly motivated to behave morally. Yet, personal convictions, social rules and normative pressures from others, or motivational lapses may all induce behavior that is not considered moral by others and invite self-justifying
responses to maintain moral self-views.

The review article can be downloaded here.

Monday, November 19, 2018

Why Facts Don’t Change Our Minds

James Clear
www.jamesclear.com
Undated

Facts Don't Change Our Minds. Friendship Does.

Convincing someone to change their mind is really the process of convincing them to change their tribe. If they abandon their beliefs, they run the risk of losing social ties. You can’t expect someone to change their mind if you take away their community too. You have to give them somewhere to go. Nobody wants their worldview torn apart if loneliness is the outcome.

The way to change people’s minds is to become friends with them, to integrate them into your tribe, to bring them into your circle. Now, they can change their beliefs without the risk of being abandoned socially.

The British philosopher Alain de Botton suggests that we simply share meals with those who disagree with us:
“Sitting down at a table with a group of strangers has the incomparable and odd benefit of making it a little more difficult to hate them with impunity. Prejudice and ethnic strife feed off abstraction. However, the proximity required by a meal – something about handing dishes around, unfurling napkins at the same moment, even asking a stranger to pass the salt – disrupts our ability to cling to the belief that the outsiders who wear unusual clothes and speak in distinctive accents deserve to be sent home or assaulted. For all the large-scale political solutions which have been proposed to salve ethnic conflict, there are few more effective ways to promote tolerance between suspicious neighbours than to force them to eat supper together.” 
Perhaps it is not difference, but distance that breeds tribalism and hostility. As proximity increases, so does understanding. I am reminded of Abraham Lincoln's quote, “I don't like that man. I must get to know him better.”

Facts don't change our minds. Friendship does.

Thursday, July 12, 2018

Learning moral values: Another's desire to punish enhances one's own punitive behavior

FeldmanHall O, Otto AR, Phelps EA.
J Exp Psychol Gen. 2018 Jun 7. doi: 10.1037/xge0000405.

Abstract

There is little consensus about how moral values are learned. Using a novel social learning task, we examine whether vicarious learning impacts moral values-specifically fairness preferences-during decisions to restore justice. In both laboratory and Internet-based experimental settings, we employ a dyadic justice game where participants receive unfair splits of money from another player and respond resoundingly to the fairness violations by exhibiting robust nonpunitive, compensatory behavior (baseline behavior). In a subsequent learning phase, participants are tasked with responding to fairness violations on behalf of another participant (a receiver) and are given explicit trial-by-trial feedback about the receiver's fairness preferences (e.g., whether they prefer punishment as a means of restoring justice). This allows participants to update their decisions in accordance with the receiver's feedback (learning behavior). In a final test phase, participants again directly experience fairness violations. After learning about a receiver who prefers highly punitive measures, participants significantly enhance their own endorsement of punishment during the test phase compared with baseline. Computational learning models illustrate the acquisition of these moral values is governed by a reinforcement mechanism, revealing it takes as little as being exposed to the preferences of a single individual to shift one's own desire for punishment when responding to fairness violations. Together this suggests that even in the absence of explicit social pressure, fairness preferences are highly labile.

The research is here.

Monday, September 4, 2017

Teaching A.I. Systems to Behave Themselves

Cade Metz
The New York Times
Originally published August 13, 2017

Here is an excerpt:

Many specialists in the A.I. field believe a technique called reinforcement learning — a way for machines to learn specific tasks through extreme trial and error — could be a primary path to artificial intelligence. Researchers specify a particular reward the machine should strive for, and as it navigates a task at random, the machine keeps close track of what brings the reward and what doesn’t. When OpenAI trained its bot to play Coast Runners, the reward was more points.

This video game training has real-world implications.

If a machine can learn to navigate a racing game like Grand Theft Auto, researchers believe, it can learn to drive a real car. If it can learn to use a web browser and other common software apps, it can learn to understand natural language and maybe even carry on a conversation. At places like Google and the University of California, Berkeley, robots have already used the technique to learn simple tasks like picking things up or opening a door.

All this is why Mr. Amodei and Mr. Christiano are working to build reinforcement learning algorithms that accept human guidance along the way. This can ensure systems don’t stray from the task at hand.

Together with others at the London-based DeepMind, a lab owned by Google, the two OpenAI researchers recently published some of their research in this area. Spanning two of the world’s top A.I. labs — and two that hadn’t really worked together in the past — these algorithms are considered a notable step forward in A.I. safety research.

The article is here.

Sunday, November 27, 2016

Approach-Induced Biases in Human Information Sampling

Laurence T. Hunt and others
PLOS Biology
Published: November 10, 2016

Abstract

Information sampling is often biased towards seeking evidence that confirms one’s prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled (“positive evidence approach”), the selection of which information to sample (“sampling the favorite”), and the interaction between information sampling and subsequent choices (“rejecting unsampled options”). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.

The article is here.

Monday, November 21, 2016

From porkies to whoppers: over time lies may desensitise brain to dishonesty

Hannah Devlin
The Guardian
Originally posted October 24, 2016

Here is an excerpt:

Now scientists have uncovered an explanation for why telling a few porkies has the tendency to spiral out of control. The study suggests that telling small, insignificant lies desensitises the brain to dishonesty, meaning that lying gradually feels more comfortable over time.

Tali Sharot, a neuroscientist at University College London and senior author, said: “Whether it’s evading tax, infidelity, doping in sports, making up data in science or financial fraud, deceivers often recall how small acts of dishonesty snowballed over time and they suddenly found themselves committing quite large crimes.”

Sharot and colleagues suspected that this phenomenon was due to changes in the brain’s response to lying, rather than simply being a case of one lie necessitating another to maintain a story.

The article is here.

Saturday, November 28, 2015

Penn study: Pay patients to take their pills

By Tom Avril
Philly.com
Originally posted November 8, 2015

Here are two excerpt:

While the field of medicine has moved increasingly toward paying doctors for performance, there has been little controlled research on whether it works. Studies of patients, meanwhile, have found that incentives can encourage healthy behaviors such as giving up cigarettes.

But in a study of 1,503 patients announced Sunday, the Penn team reported that the most effective approach, at least where statins are concerned, may be to reward both patient and physician.

"In some respects, it takes two to tango," said lead author David A. Asch, a professor at Penn's Perelman School of Medicine.

(cut)

Even if money helps, the notion of paying people to do the right thing may rub some the wrong way.

"We shouldn't have to," said Bobbi Cecco, president of the Hackensack, N.J., chapter of the Mended Hearts patient support group. "But if that's what it comes down to . . ."

Wei, the Michigan physician, said she already is motivated to help her patients stick with their medicine.

"Financial incentives wouldn't change my values or patient care," she said. "I am also an idealist."

The entire article is here.

Thursday, June 11, 2015

Goal-directed, habitual and Pavlovian prosocial behavior

Filip Gęsiarz and Molly J. Crockett
Front. Behav. Neurosci., 27 May 2015

Discussion

In this review we summarized evidence showing how the RLDM framework can integrate diverse findings describing what motivates prosocial behaviors. We suggested that the goal-directed system, given sufficient time and cognitive resources, weighs the costs of prosocial behaviors against their benefits, and chooses the action that best serves one’s goals, whether they be to merely maintain a good reputation or to genuinely enhance the welfare of another. We also suggested that to appreciate some of the benefits of other-regarding acts, such as the possibility of reciprocity, agents must have a well-developed theory of mind and an ability to foresee the cumulative value of future actions—both of which seem to involve model-based computations.

Furthermore, we reviewed findings demonstrating that the habitual system encodes the consequences of social interactions in the form of prediction errors and uses these signals to update the expected value of actions. Repetition of prosocial acts, resulting in positive outcomes, gradually increases their expected value and can lead to the formation of prosocial habits, which are performed without regard to their consequences. We speculated that the expected value of actions on a subjective level might be experienced as a ‘warm glow’ (Andreoni, 1990), linking our proposition to the behavioral economics literature. We also suggested that the notion of prosocial habits shares many features of the social heuristics hypothesis (Rand et al., 2014), implying that the habitual system could be a possible neurocognitive mechanism explaining the expression of social heuristics.

Finally, we have posited that the Pavlovian system, in response to another’s distress cues, evokes an automatic approach response towards stimuli enhancing another’s well-being—even if that response brings negative consequences.

The entire article is here.