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)
Since that day, empathy has become my touchstone in everything I do. I have created lessons, given talks, conducted workshops, and been interviewed numerous times on the subject of empathy.
Often, when working with others on this topic, it feels as if on that early winter’s day in 1989, in that small elementary school built in the 1930’s, I was given a glimpse into my life’s work: to teach how empathy in practice brings to life one of life’s greatest lessons: To treat others the way you would like to be treated.
I review the data on human visual perception that reveal the critical role played by non-visual contextual factors influencing visual activity. The global perspective that progressively emerges reveals that vision is sensitive to multiple couplings with other systems whose nature and levels of abstraction in science are highly variable. Contrary to some views where vision is immersed in modular hard-wired modules, rather independent from higher-level or other non-cognitive processes, converging data gathered in this article suggest that visual perception can be theorized in the larger context of biological, physical, and social systems with which it is coupled, and through which it is enacted. Therefore, any attempt to model complexity and multiscale couplings, or to develop a complex synthesis in the fields of mind, brain, and behavior, shall involve a systematic empirical study of both connectedness between systems or subsystems, and the embodied, multiscale and flexible teleology of subsystems. The conceptual model (MEM) that is introduced in this paper finally relates empirical evidence gathered from psychology to biocomputational data concerning the human brain. Both psychological and biocomputational descriptions of MEM are proposed in order to help fill in the gap between scales of scientific analysis and to provide an account for both the autopoiesis-driven search for information, and emerging perception.
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.
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.
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…
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.
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.
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.
Observing that the creation of certain types of artistic artifacts necessitate intelligence, we present the Lovelace 2.0 Test of creativity as an alternative to the Turing Test as a means of determining whether an agent is intelligent. The Lovelace 2.0 Test builds off prior tests of creativity and additionally provides a means of directly comparing the relative intelligence of different agents.
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).
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.
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:
•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
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.