Moral masters or moral apprentices? A connectionist account of sociomoral evaluation in preverbal infants

2021 ◽  
Author(s):  
Deon T. Benton ◽  
Candace Lapan
Keyword(s):  
Computational Model ◽  
Associative Learning ◽  
Computer Simulations ◽  
Learning Mechanism ◽  
Theoretical Account ◽  
Sociomoral Reasoning ◽  
Preverbal Infants

Numerous studies suggest that preverbal infants possess the ability to make sociomoral judgements and demonstrate a preference for prosocial agents. Some theorists argue that infants possess an “innate moral core” that guides their sociomoral reasoning. However, we propose that infants’ capacity for putative sociomoral evaluation and reasoning can just as likely be driven by a domain-general associative-learning mechanism that is sensitive to agent action. We implement this theoretical account in a connectionist computational model and show that it can account for the pattern of results in Hamlin et al. (2007) and Hamlin and Wynn (2011). These are pioneering studies in this area and were among the first studies to examine sociomoral evaluation in preverbal infants. Based on the results of 6 computer simulations, we suggest that a domain-general associative-learning mechanism can account for previous findings on preverbal infants’ capacity for sociomoral evaluation. These results suggest that an innate moral core may not be necessary to account for apparent sociomoral evaluation in infants.

scholarly journals The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model

2013 ◽  
Vol 16 (2) ◽  
pp. 209-226 ◽  
Author(s):  
J. Kiley Hamlin ◽  
Tomer Ullman ◽  
Josh Tenenbaum ◽  
Noah Goodman ◽  
Chris Baker
Keyword(s):  
Social Cognition ◽  
Computational Model ◽  
Preverbal Infants

scholarly journals Complex dynamics of choice during operant extinction and a “simple” associative learning account

2020 ◽  
Author(s):  
José R. Donoso ◽  
Julian Packheiser ◽  
Roland Pusch ◽  
Zhiyin Lederer ◽  
Thomas Walther ◽  
...  
Keyword(s):  
Computational Model ◽  
Associative Learning ◽  
Operant Conditioning ◽  
Complex Dynamics ◽  
Rule Learning ◽  
Learning Curves ◽  
Learning Mechanisms ◽  
Learning Session ◽  
Extinction Learning ◽  
Operant Extinction

AbstractExtinction learning, the process of ceasing an acquired behavior in response to altered reinforcement contingencies, is essential for survival in a changing environment. So far, research has mostly neglected the learning dynamics and variability of behavior during extinction learning and instead focused on a few response types that were studied by population averages. Here, we take a different approach by analyzing the trial-by-trial dynamics of operant extinction learning in both pigeons and a computational model. The task involved discriminant operant conditioning in context A, extinction in context B, and a return to context A to test the context-dependent return of the conditioned response (ABA renewal). By studying single learning curves across animals under repeated sessions of this paradigm, we uncovered a rich variability of behavior during extinction learning: (1) Pigeons prefer the unrewarded alternative choice in one-third of the sessions, predominantly during the very first extinction session an animal encountered. (2) In later sessions, abrupt transitions of behavior at the onset of context B emerge, and (3) the renewal effect decays as sessions progress. While these results could be interpreted in terms of rule learning mechanisms, we show that they can be parsimoniously accounted for by a computational model based only on associative learning between stimuli and actions. Our work thus demonstrates the critical importance of studying the trial-by-trial dynamics of learning in individual sessions, and the unexpected power of “simple” associative learning processes.Significance StatementOperant conditioning is essential for the discovery of purposeful actions, but once a stimulus-response association is acquired, the ability to extinguish it in response to altered reward contingencies is equally important. These processes also play a fundamental role in the development and treatment of pathological behaviors such as drug addiction, overeating and gambling. Here we show that extinction learning is not limited to the cessation of a previously reinforced response, but also drives the emergence of complex and variable choices that change from learning session to learning session. At first sight, these behavioral changes appear to reflect abstract rule learning, but we show in a computational model that they can emerge from “simple” associative learning.

Propose But Verify

2020 ◽  
pp. 575-622
Author(s):  
John C. Trueswell ◽  
Tamara Nicol Medina ◽  
Alon Hafri ◽  
Lila R. Gleitman
Keyword(s):  
Associative Learning ◽  
Experimental Evidence ◽  
Learning Mechanism ◽  
Trial Procedure ◽  
Learning Procedure ◽  
Adult Participants ◽  
Novel Words

We report three eyetracking experiments that examine the learning procedure used by adults as they pair novel words and visually presented referents over a sequence of referentially ambiguous trials. Successful learning under such conditions has been argued to be the product of a learning procedure in which participants provisionally pair each novel word with several possible referents and use a statistical associative learning mechanism to gradually converge on a single mapping across learning instances. We argue here that successful learning in this setting is instead the product of a one-trial procedure in which a single hypothesized word-referent pairing is retained across learning instances, abandoned only if the subsequent instance fails to confirm the pairing. We provide experimental evidence for this propose-but-verify learning procedure via three experiments in which adult participants attempted to learn the meanings of nonce words cross-situationally under varying degrees of referential uncertainty.

Structure from Stereo by Associative Learning of the Constraints

Perception ◽  
1989 ◽  
Vol 18 (6) ◽  
pp. 767-782 ◽  
Author(s):  
Alice J O'Toole
Keyword(s):  
Visual Field ◽  
Markov Process ◽  
Computational Model ◽  
Associative Learning ◽  
Input Data ◽  
Luminance Change ◽  
Depth Data ◽  
Graded Response ◽  
Left And Right ◽  
Spatial Locations

A computational model of structure from stereo that develops smoothness constraints naturally by associative learning of a large number of example mappings from disparity data to surface depth data is proposed. Banks of disparity-selective graded response units at all spatial locations in the visual field were the input data. These cells responded to matches of luminance change at convergent, divergent, or zero offsets in the left and right ‘retina’ samples. Surfaces were created by means of a pseudo-Markov process. From these surfaces, shaded marked and ummarked surfaces were created, along with random-dot versions of the same surfaces. Learning of these example shaded and shaded marked surfaces allowed the system to solve stereo mappings both for the surfaces it had learned and for surfaces it had not learned but which had been created by the same pseudo-Markov process. Further, the model was able to solve some random-dot versions of the surfaces when the surfaces had been learned as shaded marked surfaces.

scholarly journals How the Interval between Prime and Boost Injection Affects the Immune Response in a Computational Model of the Immune System

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
F. Castiglione ◽  
F. Mantile ◽  
P. De Berardinis ◽  
A. Prisco
Keyword(s):  
Immune Response ◽  
Immune System ◽  
Computational Model ◽  
Computer Simulations ◽  
Antibody Responses ◽  
High Antibody ◽  
Antibody Titers ◽  
First Contact

The immune system is able to respond more vigorously to the second contact with a given antigen than to the first contact. Vaccination protocols generally include at least two doses, in order to obtain high antibody titers. We want to analyze the relation between the time elapsed from the first dose (priming) and the second dose (boost) on the antibody titers. In this paper, we couplein vivoexperiments with computer simulations to assess the effect of delaying the second injection. We observe that an interval of several weeks between the prime and the boost is necessary to obtain optimal antibody responses.

scholarly journals Sleeping Our Way to Weight Normalization and Stable Learning

2008 ◽  
Vol 20 (12) ◽  
pp. 3111-3130 ◽  
Author(s):  
Thomas J. Sullivan ◽  
Virginia R. de Sa
Keyword(s):  
Computational Model ◽  
Computer Simulations ◽  
Slow Wave ◽  
Hebbian Learning ◽  
Slow Wave Sleep ◽  
Neural Models ◽  
Synaptic Scaling ◽  
Scale Down ◽  
Stable Learning ◽  
Stable Development

The functions of sleep have been an enduring mystery. Tononi and Cirelli (2003) hypothesized that one of the functions of slow-wave sleep is to scale down synapses in the cortex that have strengthened during awake learning. We create a computational model to test the functionality of this idea and examine some of its implications. We show that synaptic scaling during slow-wave sleep is capable of keeping Hebbian learning in check and that it enables stable development. We also show theoretically how it implements classical weight normalization, which has been in common use in neural models for decades. Finally, a significant computational limitation of this form of synaptic scaling is revealed through computer simulations.

scholarly journals Novel insights into the electrophysiology of murine cardiac macrophages: relevance of voltage-gated potassium channels

2021 ◽  
Author(s):  
Ana Simon-Chica ◽  
Marbely C Fernández ◽  
Eike M Wülfers ◽  
Achim Lother ◽  
Ingo Hilgendorf ◽  
...  
Keyword(s):  
Membrane Potential ◽  
Computational Model ◽  
Rna Sequencing ◽  
Computer Simulations ◽  
Cardiac Electrophysiology ◽  
Potassium Currents ◽  
Cardiac Conduction ◽  
Resting Membrane Potential ◽  
Electrophysiological Properties ◽  
Electrophysiological Effects

Abstract Aims Macrophages (MΦ), known for immunological roles such as phagocytosis and antigen presentation, have been found to electrotonically couple to cardiomyocytes (CM) of the atrio-ventricular node via Cx43, affecting cardiac conduction in isolated mouse hearts. Here, we characterise passive and active electrophysiological properties of murine cardiac resident MΦ, and model their potential electrophysiological relevance for CM. Methods and Results We combined classic electrophysiological approaches with 3 D florescence imaging, RNA-sequencing, pharmacological interventions and computer simulations. We used Cx3cr1eYFP/+ mice wherein cardiac MΦ were fluorescently labelled. FACS-purified fluorescent MΦ from mouse hearts were studied by whole-cell patch-clamp. MΦ electrophysiological properties include: membrane resistance 2.2 ± 0.1 GΩ (all data mean±SEM), capacitance 18.3 ± 0.1 pF, resting membrane potential -39.6 ± 0.3 mV, and several voltage-activated, outward or inwardly-rectifying potassium currents. Using ion channel blockers (barium, TEA, 4-AP, margatoxin, XEN-D0103, DIDS), flow cytometry, immuno-staining and RNA-sequencing, we identified Kv1.3, Kv1.5 and Kir2.1 as channels contributing to observed ion currents. MΦ displayed four patterns for outward and two for inward-rectifier potassium currents. Additionally, MΦ showed surface expression of Cx43, a prerequisite for homo- and/or heterotypic electrotonic coupling. Experimental results fed into development of an original computational model to describe cardiac MΦ electrophysiology. Computer simulations to quantitatively assess plausible effects of MΦ on electrotonically coupled CM showed that MΦ can depolarise resting CM, shorten early and prolong late action potential duration, with effects depending on coupling strength and individual MΦ electrophysiological properties, in particular resting membrane potential and presence/absence of Kir2.1. Conclusions Our results provide a first electrophysiological characterisation of cardiac resident MΦ, and a computational model to quantitatively explore their relevance in the heterocellular heart. Future work will be focussed at distinguishing electrophysiological effects of MΦ–CM coupling on both cell types during steady-state and in patho-physiological remodelling, when immune cells change their phenotype, proliferate, and/or invade from external sources. Translational Perspective Cardiac tissue contains resident macrophages (MΦ) which, beyond immunological and housekeeping roles, have been found to electrotonically couple via connexins to cardiomyocytes (CM), stabilising atrio-ventricular conduction at high excitation rates. Here, we characterise structure and electrophysiological function of murine cardiac MΦ and provide a computational model to quantitatively probe the potential relevance of MΦ-CM coupling for cardiac electrophysiology. We find that MΦ are unlikely to have major electrophysiological effects in normal tissue, where they would hasten early and slow late CM-repolarisation. Further work will address potential arrhythmogenicity of MΦ in patho-physiologically remodelled tissue containing elevated MΦ-numbers, incl. non-resident recruited cells.

scholarly journals Modelling and analysis of the stability of a harbour wharf’s embankment with regards to its failure

2021 ◽  
Vol 21 (4) ◽  
Author(s):  
Grzegorz Dmochowski ◽  
Piotr Berkowski ◽  
Jerzy Szołomicki ◽  
Barbara Gronostajska ◽  
Jarosław Krążelewski
Keyword(s):  
Stability Analysis ◽  
Computational Model ◽  
Computer Simulations ◽  
Environmental Conditions ◽  
3D Model ◽  
Historical Analysis ◽  
Data Preparation ◽  
The Stability ◽  
Material Tests ◽  
River Embankment

AbstractThe article presents the process of creating a computational model for the stability analysis of a harbour wharf’s embankment with regards to its failure. The described elements that were taken into account at the stage of data preparation are: historical analysis of the structure (structural calculations, and the calculations of the stability of the embankment), results of the author’s own soil and material tests (concrete, steel), environmental conditions (atmospheric and water), the possible load systems that acted on the facility, and also the results from the 3D model of the wharf’s structure. On this basis, five computational schemes were prepared for computer simulations of the stability of the embankment in a plain state of deformations. Various stages and conditions of its operation were taken into account. In conclusion, a possible course of the process of destruction of the structure of the harbour wharf, and the river embankment that cooperates with it, was proposed.

scholarly journals Modeling the dynamics of progression of chromosomal alterations in cervical cancer: a computational model

2017 ◽  
Author(s):  
Augusto Cabrera-Becerril ◽  
Cruz Vargas-De-León ◽  
Sergio Hernández ◽  
Pedro Miramontes ◽  
Raúl Peralta
Keyword(s):  
Cervical Cancer ◽  
Drug Resistance ◽  
Computational Model ◽  
Computer Simulations ◽  
Cancer Progression ◽  
The Other ◽  
Precursor Lesions ◽  
Deleterious Mutations ◽  
Chromosomal Alterations ◽  
Somatic Alterations

AbstractComputational modeling has been applied to simulate the heterogeneity of cancer behavior. The development of Cervical Cancer (CC) is a process in which the cell acquires dynamic behavior among non-deleterious and deleterious mutations, exhibiting chromosomal alterations as a manifestation of this dynamic. To further determine the progression of chromosomal alterations in precursor lesions and CC, we introduce a computational model to study the dynamic of deleterious and non-deleterious mutations as an outcome of tumor progression. Analysis of chromosomal alterations mediated by our model reveals that multiple deleterious mutations are more frequent in precursor lesions than in CC. Cells with lethal deleterious mutations would be eliminated, which would mitigate cancer progression; on the other hand, cells with non-deleterious mutations would become dominant, which could predispose to cancer progression. The study of somatic alterations by computer simulations during cancer progression provides a feasible pathway for insights into the transformation of cell mechanisms in humans. During cancer progression, tumors may acquire new phenotype traits, such as the ability to invade and metastasize or to become clinically important when they develop drug resistance. Chromosomal alterations non deleterious contributes to this progression.

scholarly journals The propositional nature of human associative learning

2009 ◽  
Vol 32 (2) ◽  
pp. 183-198 ◽  
Author(s):  
Chris J. Mitchell ◽  
Jan De Houwer ◽  
Peter F. Lovibond
Keyword(s):  
Associative Learning ◽  
Cognitive Processes ◽  
Memory Retrieval ◽  
Mental Representations ◽  
Future Research ◽  
The Unconscious ◽  
Learning Mechanism ◽  
Unconscious Processes ◽  
The Past ◽  
High Level

AbstractThe past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.

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