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Persistence (psychology) - Wikipedia, the free encyclopedia

In psychology, persistence (PS) is a personality trait. It is measured in the Temperament and Character Inventory (TCI) and is considered one of the four temperament traits. Persistence refers to perseverance in spite of fatigue or frustration.[1] Cloninger's research found that persistence, like the other temperament traits, is highly heritable. The subscales of PS in TCI-R consist of

A study comparing the Temperament and Character Inventory to the five factor model of personality found that persistence was substantially associated with conscientiousness.[2] Additionally, persistence was moderately positively associated with the TCI trait of self-transcendence. Research has also found that persistence is positively correlated with Activity in Zuckerman's "Alternative Five" model, and is negatively correlated with psychoticism in Eysenck's model.[2]

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Cognitive Science - Artificial Intelligence
Cognitive science is the interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does and how it works. It includes research on intelligence and behavior, especially focusing on how information is represented, processed, and transformed (in faculties such as perception, language, memory, reasoning, and emotion) within nervous systems (human or other animal) and machines (e.g. computers). Cognitive science consists of multiple research disciplines, including psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology. The fundamental concept of cognitive science is "that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures." Wikipedia (en)
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Frontiers | Blinded by magic: eye-movements reveal the misdirection of attention | Theoretical and Philosophical Psychology

Frontiers | Blinded by magic: eye-movements reveal the misdirection of attention | Theoretical and Philosophical Psychology | Cognitive Science - Artificial Intelligence | Scoop.it
Recent studies (e.g., Kuhn and Tatler, 2005) have suggested that magic tricks can provide a powerful and compelling domain for the study of attention and perception. In particular, many stage illusions involve attentional misdirection, guiding the observer's gaze to a salient object or event, while another critical action, such as sleight of hand, is taking place. Even if the critical action takes place in full view, people typically fail to see it due to inattentional blindness (IB). In an eye-tracking experiment, participants watched videos of a new magic trick, wherein a coin placed beneath a napkin disappears, reappearing under a different napkin. Appropriately deployed attention would allow participants to detect the “secret” event that underlies the illusion (a moving coin), as it happens in full view and is visible for approximately 550 ms. Nevertheless, we observed high rates of IB. Unlike prior research, eye-movements during the critical event showed different patterns for participants, depending upon whether they saw the moving coin. The results also showed that when participants watched several “practice” videos without any moving coin, they became far more likely to detect the coin in the critical trial. Taken together, the findings are consistent with perceptual load theory (Lavie and Tsal, 1994).
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Stephen Hawking's AI fears stir scientific debate | The Japan Times

Stephen Hawking's AI fears stir scientific debate | The Japan Times | Cognitive Science - Artificial Intelligence | Scoop.it

PARIS – There was the psychotic HAL 9000 computer in “2001: A Space Odyssey.”

The humanoids that attacked their flesh-and-blood masters in “I, Robot.”

And, of course, “The Terminator,” where a robot is sent into the past to kill a woman whose son will end the tyranny of the machines in the future.

Never far from the surface, a dark, dystopian view of artificial intelligence (AI) has returned to the headlines thanks to British physicist Stephen Hawking.

“The primitive forms of artificial intelligence we already have, have proved very useful. But I think the development of full artificial intelligence could spell the end of the human race,” Hawking told the BBC.

“Once humans develop artificial intelligence it would take off on its own and re-design itself at an ever increasing rate,” he said.

But experts were divided.

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Happy People Aren't Always Great At Empathy

Happy People Aren't Always Great At Empathy | Cognitive Science - Artificial Intelligence | Scoop.it
Perpetually happy individuals are wonderful to have around, until you experience something worth complaining about. Recent research in PLOS ONE suggests that people who are generally cheerful are not so great at reading other people's negative emotions, though what's especially interesting is that they think they're very good at it.

More from Science of Us: Grumpy People Get The Details Right

Researchers asked the participants both how happy they tended to be from day to day and how empathetic they considered themselves.

 

The cheerier volunteers tended to tell the researchers that they were more empathetic, too, when compared to their not-quite-so-happy study subject counterparts. Alex Fradera, in a post at the British Psychological Society's Research Digest, describes what happened next:

 

By Melissa Dahl 


Via Edwin Rutsch, Jocelyn Stoller
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Proceedings | ICCC 2014

Proceedings | ICCC 2014 | Cognitive Science - Artificial Intelligence | Scoop.it

Fifth International Conference on Computational Creativity

Ljubljana, Slovenia, 9th – 13th June 2014

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Nonpolitical Images Evoke Neural Predictors of Political Ideology: Current Biology

Nonpolitical Images Evoke Neural Predictors of Political Ideology: Current Biology | Cognitive Science - Artificial Intelligence | Scoop.it
•Literature suggests negativity bias might underlie variations in political views•fMRI responses to disgusting images accurately predict political orientation•Self-reports about affective images are not predictive of their political views•Single-stimulus data can reliably classify conservatives from liberals
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Parental Choices and Children's Skills

Parental Choices and Children's Skills | Cognitive Science - Artificial Intelligence | Scoop.it

An agent-based simulation model (ABM) is developed and implemented using Python to explore the emergence of intragenerational and intergenerational skill inequality at the societal level that results from differences in parental investment behavior at the household level during early stages of the life course. Parental behavior is modeled as optimal, heuristic-based, or norm-oriented. Skills grow according to the technology of skill formation developed in the field of economics, calibrated with empirically estimated parameters from existing research. Agents go through a simplified life course. During childhood and adolescence, skills are produced through parental investments. In adulthood, individuals find a partner, give birth to the next generation, and invest in offspring. Number and spacing of children and available resources are treated as exogenous factors and are varied experimentally. Simulation experiments suggest that parental decisions at the household level play a role in the emergence of inequality at the societal level. Being egalitarian or not is the most important distinction in parental investment behavior, while optimizing parents generate similar results as egalitarian parents. Furthermore, there is a tradeoff between equality at home and inequality at the macro-level. Changes in the environment reduce or exacerbate inequality depending on parental investment behavior. One prediction of the model on intragenerational inequality in cognitive skills was validated with the use of empirical data. The simulation can best be described as a middle-range model, informed by research on skill formation and the intrahousehold allocation of resources. It is a first step toward more complex ABMs on inequality from a life course perspective. Possible model extensions are suggested. The Overview, Design Concepts, and Details (ODD) protocol and Design of Experiments (DOE) were used to document the model and set up the experimental design respectively.

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How Your Brain Decides Without You - Issue 19: Illusions - Nautilus

How Your Brain Decides Without You - Issue 19: Illusions - Nautilus | Cognitive Science - Artificial Intelligence | Scoop.it
In a world full of ambiguity, we see what we want to see.
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Rescooped by Bernard Ryefield from Brain Tricks: Belief, Bias, and Blindspots
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Ignore Emotional Intelligence at Your Own Risk

Ignore Emotional Intelligence at Your Own Risk | Cognitive Science - Artificial Intelligence | Scoop.it
A new debate on a classic concept.

Via Rob Duke, Jocelyn Stoller
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malek's comment, November 4, 7:03 AM
Empathy is in a black hole
Dana Hoffman's comment, November 4, 8:00 AM
That needs light shined into it.
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Neural Conspiracy Theories

Neural Conspiracy Theories | Cognitive Science - Artificial Intelligence | Scoop.it

Last month, a paper quietly appeared in The Journal of Neuroscience to little fanfare and scant media attention (with these exceptions). The study revolved around a clever and almost diabolical pre...


Via LOr
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LOr's curator insight, September 27, 12:00 PM
perception et a priori
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Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012)

This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, which was held on Catalina Island, CA August 14-18 2012.

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Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (2011)

This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011

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Rescooped by Bernard Ryefield from Complexity - Complex Systems Theory
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A Fundamental Theory to Model the Mind | Quanta Magazine

A Fundamental Theory to Model the Mind |  Quanta Magazine | Cognitive Science - Artificial Intelligence | Scoop.it
Support is growing for a decades-old physics idea suggesting that localized episodes of disordered brain activity help keep the overall system in healthy balance.
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New Ideas for Brain Modelling (v2)

This paper describes some biologically-inspired processes that could be used to build the sort of networks that we associate with the human brain. New to this paper, a 'refined' neuron will be proposed. This is a group of neurons that by joining together can produce a more analogue system, but with the same level of control and reliability that a binary neuron would have. With this new structure, it will be possible to think of an essentially binary system in terms of a more variable set of values. The paper also shows how recent research can be combined with established theories, to produce a more complete picture. The propositions are largely in line with conventional thinking, but possibly with one or two more radical suggestions. An earlier cognitive model can be filled in with more specific details, based on the new research results, where the components appear to fit together almost seamlessly. The intention of the research has been to describe plausible 'mechanical' processes that can produce the appropriate brain structures and mechanisms, but that could be used without the magical 'intelligence' part that is still not fully understood. There are also some important updates from an earlier version of this paper.

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The science of why cops shoot young black men

The science of why cops shoot young black men | Cognitive Science - Artificial Intelligence | Scoop.it
And how to reform our bigoted brains.
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Keeping The Brain Stable While Learning

Keeping The Brain Stable While Learning | Cognitive Science - Artificial Intelligence | Scoop.it
A mathematical model has helped scientists resolve a decades-old paradox of how the brain remains stable while learning new information.
Bernard Ryefield's insight:

Paper available at:

http://toyoizumilab.brain.riken.jp/taro/publication.html

 

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Rescooped by Bernard Ryefield from Social Neuroscience Advances
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Cognitive Psychology of Moral Intuitions by Daniel Kahneman, Cass R. Sunstein :: SSRN

Cognitive Psychology of Moral Intuitions by Daniel Kahneman, Cass R. Sunstein :: SSRN | Cognitive Science - Artificial Intelligence | Scoop.it
Abstract:     
Moral intuitions operate in much the same way as other intuitions do; what makes the moral domain so distinctive is its foundations in the emotions, beliefs, and response tendencies that define indignation. The intuitive system of cognition, System I, is typically responsible for indignation; the more reflective system, System II, may or may not provide an override. Moral dumbfounding and moral numbness are often a product of moral intuitions that people are unable to justify. An understanding of indignation helps to explain the operation of many phenomena of interest to law and politics: the outrage heuristic, the centrality of harm, the role of reference states, moral framing, and the act-omission distinction. Because of the operation of indignation, it is extremely difficult for people to achieve coherence in their moral intuitions. Legal and political institutions usually aspire to be deliberative, and to pay close attention to System II; but even in deliberative institutions, System I can make some compelling demands.

Via Alessandro Cerboni, Jocelyn Stoller
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Intergroup Conflict and Rational Decision Making

Intergroup Conflict and Rational Decision Making | Cognitive Science - Artificial Intelligence | Scoop.it
The literature has been relatively silent about post-conflict processes. However, understanding the way humans deal with post-conflict situations is a challenge in our societies. With this in mind, we focus the present study on the rationality of cooperative decision making after an intergroup conflict, i.e., the extent to which groups take advantage of post-conflict situations to obtain benefits from collaborating with the other group involved in the conflict. Based on dual-process theories of thinking and affect heuristic, we propose that intergroup conflict hinders the rationality of cooperative decision making. We also hypothesize that this rationality improves when groups are involved in an in-group deliberative discussion. Results of a laboratory experiment support the idea that intergroup conflict –associated with indicators of the activation of negative feelings (negative affect state and heart rate)– has a negative effect on the aforementioned rationality over time and on both group and individual decision making. Although intergroup conflict leads to sub-optimal decision making, rationality improves when groups and individuals subjected to intergroup conflict make decisions after an in-group deliberative discussion. Additionally, the increased rationality of the group decision making after the deliberative discussion is transferred to subsequent individual decision making.
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Rescooped by Bernard Ryefield from Brain Tricks: Belief, Bias, and Blindspots
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Are you a poor logician? Logically, you might never know

Are you a poor logician? Logically, you might never know | Cognitive Science - Artificial Intelligence | Scoop.it
This is the second article in a series, How we make decisions, which explores our decision-making processes. How well do we consider all factors involved in a decision, and what helps and what holds us…

Via Gerald Carey, Jocelyn Stoller
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Gerald Carey's curator insight, November 9, 3:57 AM

A nice summary of our current understanding of illusions such as the Dunning-Kruger effect.

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Analysing Differential School Effectiveness Through Multilevel and Agent-Based Modelling

Analysing Differential School Effectiveness Through Multilevel and Agent-Based Modelling | Cognitive Science - Artificial Intelligence | Scoop.it

During the last thirty years education researchers have developed models for judging the comparative performance of schools, in studies of what has become known as "differential school effectiveness". A great deal of empirical research has been carried out to understand why differences between schools might emerge, with variable-based models being the preferred research tool. The use of more explanatory models such as agent-based models (ABM) has been limited. This paper describes an ABM that addresses this topic, using data from the London Educational Authority's Junior Project. To compare the results and performance with more traditional modelling techniques, the same data are also fitted to a multilevel model (MLM), one of the preferred variable-based models used in the field. The paper reports the results of both models and compares their performances in terms of predictive and explanatory power. Although the fitted MLM outperforms the proposed ABM, the latter still offers a reasonable fit and provides a causal mechanism to explain differences in the identified school performances that is absent in the MLM. Since MLM and ABM stress different aspects, rather than conflicting they are compatible methods.

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Fooled By Your Own Brain - Issue 19: Illusions - Nautilus

Fooled By Your Own Brain - Issue 19: Illusions - Nautilus | Cognitive Science - Artificial Intelligence | Scoop.it
Don’t be so certain your senses are telling you the truth.
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Shakespeare’s Genius Is Nonsense - Issue 18: Genius - Nautilus

Shakespeare’s Genius Is Nonsense - Issue 18: Genius - Nautilus | Cognitive Science - Artificial Intelligence | Scoop.it
You’d be forgiven if, settling into the fall 2003 “Literature of the 16th Century” course at University of California, Berkeley,…
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CiteSeerX — Towards A Computational Theory Of Human Daydreaming

CiteSeerX — Towards A Computational Theory Of Human Daydreaming | Cognitive Science - Artificial Intelligence | Scoop.it
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper examines the phenomenon of daydreaming: spontaneously recalling or imagining personal or vicarious experiences in the past or future. The following important roles of daydreaming in human cognition are postulated: plan preparation and rehearsal, learning from failures and successes, support for processes of creativity, emotion regulation, and motivation. A computational theory of daydreaming and its implementation as the program DAYDREAMER are presented. DAYDREAMER consists of 1) a scenario generator based on relaxed planning, 2) a dynamic episodic memory of experiences used by the scenario generator, 3) a collection of personal goals and control goals which guide the scenario generator, 4) an emotion component in which daydreams initiate, and are initiated by, emotional states arising from goal outcomes, and 5) domain knowledge of interpersonal relations and common everyday occurrences. The role of emotions and control goals in daydreaming is discussed. Four control goals commonly used in guiding daydreaming are presented: rationalization, failure/success reversal, revenge, and preparation. The role of episodic memory in daydreaming is considered, including how daydreamed information is incorporated into memory and later used. An initial version of DAYDREAMER which produces several daydreams (in English) is currently running.
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Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (2013)

This is the Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, which was held in Bellevue, WA, August 11-15, 2013

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Frontiers | Being Critical of Criticality in the Brain | Fractal Physiology

Frontiers | Being Critical of Criticality in the Brain | Fractal Physiology | Cognitive Science - Artificial Intelligence | Scoop.it
Relatively recent work has reported that networks of neurons can produce avalanches of activity whose sizes follow a power law distribution. This suggests that these networks may be operating near a critical point, poised between a phase where activity rapidly dies out and a phase where activity is amplified over time. The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point. This hypothesis, however, is still controversial. Here we will explain the concept of criticality and review the substantial objections to the criticality hypothesis raised by skeptics. Points and counter points are presented in dialogue form.
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Unconscious lie detection as an example of a widespread fallacy in the Neurosciences

Neuroscientists frequently use a certain statistical reasoning to establish the existence of distinct neuronal processes in the brain. We show that this reasoning is flawed and that the large corresponding literature needs reconsideration. We illustrate the fallacy with a recent study that received an enormous press coverage because it concluded that humans detect deceit better if they use unconscious processes instead of conscious deliberations. The study was published under a new open-data policy that enabled us to reanalyze the data with more appropriate methods. We found that unconscious performance was close to chance - just as the conscious performance. This illustrates the flaws of this widely used statistical reasoning, the benefits of open-data practices, and the need for careful reconsideration of studies using the same rationale.

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