94 research outputs found
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Recognition by directed attention to recursively partitioned images
A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth
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Learning and memory in machines and animals : an AI model that accounts for some neurobiological data
The CEL model of learning and memory (Components of Episodic Learning) [Granger 1982, 1983a, 1983b] provides a process model of certain aspects of learning and memory in animals and humans. The model consists of a set of asynchronous and semi-independent functional operators that collectively create and modify memory traces as a result of experience. The model conforms to relevant results in the learning literature of psychology and neurobiology. There are two goals to this work: one is to create a set of working learning systems that will improve their performance on the basis of experience, and the other is to compare these systems' performance with that of living systems, as a step towards the eventual comparative characterizations of different learning systems.Parts of the model have been implemented in the CEL-0 program, which operates in a 'Maze-World' simulated maze environment. The program exhibits simple exploratory behavior that leads to the acquisition of predictive and discriminatory schemata. A number of interesting theoretical predictions have arisen in part from observation of the operation of the program, some of which are currently being tested in neurobiological experiments. In particular, some neurobiological evidence for the existence of multiple, seperable memory systems in humans and animals is interpreted in terms of the model, and some new experiments are suggested arising from the model's predictions
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Learning Relative Attribute Weights For Instance-Based Concept Descriptions
Nosofsky recently described an elegant instance-based model (GCM) for concept learning that defined similarity (partly) in terms of a set of attribute weights. He showed that, when given the proper parameter settings, the G C M model closely fit his human subject data on classification performance. However, no algorithm was described for learning the attribute weights. The central thesis of the GCM model is that subjects distribute their attention amiong attributes to optimize their classification and learning performance. In this paper, we introduce two comprehensive process models based on the G C M . Our first model is simply an extension of the G C M that learns relative attribute weights. The GCM's learning and representational capabilities are limited - concept descriptions are assumed to be disjoint and exhaustive. Therefore, our second model is a further extension that learns a unique set of attribute weights for each concept description. Our empirical evidence indicates that this extension outperforms the simple G C M process model when the domain includes overlapping concept descriptions with conflicting attribute relevancies
Trust as a daily defense against collective disease threats
Although the isolated threat of disease often motivates people to avoid others, people need the help and cooperation of others to protect themselves against pandemic disease threats. Therefore, the fear of contracting a highly contagious virus like COVID-19 should motivate people to believe that they can in fact count on the help and cooperation of others for protection. Trusting in others provides the basis to anticipate their cooperation. Therefore, we expected a greater daily threat of contracting COVID-19 to motivate people to trust more in others, providing needed assurance that others would keep them safe from harm. We obtained 4 daily diary samples involving 2794 participants who provided in excess of 18,000 daily observations within the first three months of the COVID-19 pandemic. Each day, we tracked (1) disease threat, captured daily by personal concerns about COVID-19 and infection totals in the nearest most populous city, and (2) trust in others, captured daily by expressions of trust in intimates, collective caregivers (e.g., President, Congress), and strangers. Participants in two samples completed 2-month follow-ups. Integrative analyses of the daily diaries revealed that people trusted more in intimates and collective caregivers on days they had greater (vs. less) reason to be concerned about COVID-19. Further integrative analyses of the follow-up data revealed that participants who were initially more likely to trust in others on days when COVID-19 cases in nearby communities spread more rapidly later reported greater confidence that others would keep them safe from harm. That is, they evidenced greater physical, interpersonal, and collective security in social connection than participants who were initially less likely to defensively trust in others on such occasions. The present findings suggest that ecological threats may dynamically motivate people to trust others more than they otherwise would, providing optimism that collectively-faced crises may motivate social cooperation when it is most needed
Production of highly-polarized positrons using polarized electrons at MeV energies
The Polarized Electrons for Polarized Positrons experiment at the injector of
the Continuous Electron Beam Accelerator Facility has demonstrated for the
first time the efficient transfer of polarization from electrons to positrons
produced by the polarized bremsstrahlung radiation induced by a polarized
electron beam in a high- target. Positron polarization up to 82\% have been
measured for an initial electron beam momentum of 8.19~MeV/, limited only by
the electron beam polarization. This technique extends polarized positron
capabilities from GeV to MeV electron beams, and opens access to polarized
positron beam physics to a wide community.Comment: 5 pages, 4 figure
A New Measurement of the Radiative Decay Width
High precision measurements of the differential cross sections for
photoproduction at forward angles for two nuclei, C and Pb, have
been performed for incident photon energies of 4.9 - 5.5 GeV to extract the
decay width. The experiment was done at Jefferson
Lab using the Hall B photon tagger and a high-resolution multichannel
calorimeter. The decay width was extracted by
fitting the measured cross sections using recently updated theoretical models
for the process. The resulting value for the decay width is . With the 2.8% total uncertainty, this result is a factor of 2.5 more
precise than the current PDG average of this fundamental quantity and it is
consistent with current theoretical predictions.Comment: 4 pages, 5 figure
Pursuing Safety in Social Connection Regulates the Risk-Regulation, Social-Safety and Behavioral-Immune Systems
A new goal-systems model is proposed to help explain when individuals will protect themselves against the risks inherent to social connection. This model assumes that people satisfy the goal to feel included in safe social connections—connections where they are valued and protected rather than at risk of being harmed—by devaluing rejecting friends, trusting in expectancy–consistent relationships, and avoiding infectious strangers. In the hypothesized goal system, frustrating the fundamental goal to feel safe in social connection sensitizes regulatory systems that afford safety from the risk of being interpersonally rejected (i.e., the risk-regulation system), existentially uncertain (i.e., the social-safety system), or physically infected (i.e., the behavioral-immune system). Conversely, fulfilling the fundamental goal to feel safe in social connection desensitizes these self-protective systems. A 3-week experimental daily diary study (N = 555) tested the model hypotheses. We intervened to fulfill the goal to feel safe in social connection by repeatedly conditioning experimental participants to associate their romantic partners with highly positive, approachable words and images. We then tracked how vigilantly experimental versus control participants protected themselves when they encountered social rejection, unexpected behavior, or contagious illness in everyday life. Multilevel analyses revealed that the intervention lessoned self-protective defenses against each of these risks for participants who ordinarily felt most vulnerable to them. The findings provide the first evidence that the fundamental goal to feel safe in social connection can co-opt the risk-regulation, social-safety, and behavioral-immune systems as independent means for its pursuit
Looking for Safety in all the Right Places: When Threatening Political Reality Strengthens Family Relationship Bonds
Elections and pandemics highlight how much one’s safety depends on fellow community members, a realization that is especially threatening when the collective perceives political realities inconsistent with one’s own. Two longitudinal studies examined how people restored safety to social bonds when everyday experience suggested that fellow community members inhabited inconsistent realities. We operationalized consensus political realities through the negativity of daily, nation-wide social media posts mentioning President Trump (Studies 1 and 2), and the risks of depending on fellow community members through the pending transition to a divided Congress during the 2018 election season (Study 1), and escalating daily U.S. COVID-19 infections (Study 2). On days that revealed people could not count on fellow community members to perceive the same reality of President Trump’s stewardship they perceived, being at greater risk from the judgment and behavior of the collective community motivated people to find greater happiness in their family relationships
A Moth to a Flame? Fulfilling Connectedness Needs Through Romantic Relationships Protects Conspiracy Theorists Against COVID-19 Misinformation
Conspiracy theorists’ unpopular opinions likely make them more apprehensive about interactions with others, frustrating their need to belong. Therefore, they may be susceptible to believing misinformation because evidence that others share their beliefs provides “social proof” that they can expect interactions with others to be positive and rewarding. The present research examined whether alternatively fulfilling the need for social connection through romantic relationships could protect conspiracy theorists against COVID-19 misinformation. In a 3-week daily diary study (N = 555), experimental participants implicitly learned to associate their romantic partners with positive experiences (by repeatedly pairing their partner with highly positive and approachable stimuli, McNulty et al., 2017). We then assessed how much participants trusted individuals they might normally distrust, as a manipulation check, and how much participants tuned their daily personal beliefs and behavior to match the U.S. public's daily susceptibility to COVID-19 misinformation. Participants high on conspiratorial thinking trusted fellow community members more in the experimental than control condition. Participants high on conspiratorial thinking in the experimental condition were also less likely to treat the U.S. public's greater daily susceptibility to COVID-19 misinformation as proof that they could discount the virus. The present findings suggest that rewarding romantic connections might be leveraged to limit conspiracy theorists’ susceptibility to believing public skepticism about COVID-19
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