Prospection in cognition: the case for joint episodic-procedural memory in cognitive robotics

Prospection lies at the core of cognition: it is the means by which an agent \u2013 a person or a cognitive robot \u2013 shifts its perspective from immediate sensory experience to anticipate future events, be they the actions of other agents or the outcome of its own actions. Prospection, accomplished by internal simulation, requires mechanisms for both perceptual imagery and motor imagery. While it is known that these two forms of imagery are tightly entwined in the mirror neuron system, we do not yet have an effective model of the mentalizing network which would provide a framework to integrate declarative episodic and procedural memory systems and to combine experiential knowledge with skillful know-how. Such a framework would be founded on joint perceptuo-motor representations. In this paper, we examine the case for this form of representation, contrasting sensory-motor theory with ideo-motor theory, and we discuss how such a framework could be realized by joint episodic-procedural memory. We argue that such a representation framework has several advantages for cognitive robots. Since episodic memory operates by recombining imperfectly recalled past experience, this allows it to simulate new or unexpected events. Furthermore, by virtue of its associative nature, joint episodic-procedural memory allows the internal simulation to be conditioned by current context, semantic memory, and the agent\u2019s value system. Context and semantics constrain the combinatorial explosion of potential perception-action associations and allow effective action selection in the pursuit of goals, while the value system provides the motives that underpin the agent\u2019s autonomy and cognitive development. This joint episodic-procedural memory framework is neutral regarding the final implementation of these episodic and procedural memories, which can be configured sub-symbolically as associative networks or symbolically as content-addressable image databases and databases of motor-control scripts

Calibration and comparison of concrete models with respect to experimental data

At the beginning of the 21st century, civil engineers more than ever face the often-contradictory demands for designing larger, safer and more durable structures at a lower cost and in shorter time. Concrete has been used for many centuries as a safe and durable building material. Two of the main advantages of concrete are its high compressive strength and that it can be cast on the construction site into a variety of shapes and sizes. Many different constitutive models have been developed to fulfill the above mentioned requirements and describe/predict the behavior and failure of concrete. The never ending challenge for engineers is to choose and set up the appropriate material model for the modeling of structures or structural elements. Therefore, the primary objective of the present research is to calibrate, validate and compare different constitutive models with respect to an extensive set of experimental data. Depending on the application and availability of data, the expected prediction quality of the available models may vary significantly. The studied material models include the microplane models M4 and M7, the damage plasticity models available in commercial (ATENA) or open source (OOFEM) finite element codes, e.g. the Grassl-Jirasek material model. Moreover, the Lattice-Discrete-Particle- Model (LDPM), implemented in the solver MARS, is utilized. We present a comparison of these models with regard to the number of input parameters, their physical meaning, the ease of calibration and their predictive capabilities by utilizing a large set of experimental data derived from specimens, cast from the same batch. All models are calibrated using three mean value nominal stress-strain curves obtained from a notched three-point bending, uniaxial compression and compression under passive confinement test. The calibrated numerical models are then used to predict the results of the remaining experiments, i.e. 3-point bending tests of 4 sizes with various notch depths, splitting tests of 5 sizes, direct tensions tests and torsion tests. These data then serve to assess the prediction quality of the models

Comparison between Two Implementations of iCub's Fingertip

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The Use of Phonetic Motor Invariants Can Improve Automatic Phoneme Discrimination

affiliation: Castellini, C (Reprint Author), Univ Genoa, LIRA Lab, Genoa, Italy. Castellini, Claudio; Metta, Giorgio; Tavella, Michele, Univ Genoa, LIRA Lab, Genoa, Italy. Badino, Leonardo; Metta, Giorgio; Sandini, Giulio; Fadiga, Luciano, Italian Inst Technol, Genoa, Italy. Grimaldi, Mirko, Salento Univ, CRIL, Lecce, Italy. Fadiga, Luciano, Univ Ferrara, DSBTA, I-44100 Ferrara, Italy. article-number: e24055 keywords-plus: SPEECH-PERCEPTION; RECOGNITION research-areas: Science & Technology - Other Topics web-of-science-categories: Multidisciplinary Sciences author-email: [email protected] funding-acknowledgement: European Commission [NEST-5010, FP7-IST-250026] funding-text: The authors acknowledge the support of the European Commission project CONTACT (grant agreement NEST-5010) and SIEMPRE (grant agreement number FP7-IST-250026). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. number-of-cited-references: 31 times-cited: 0 journal-iso: PLoS One doc-delivery-number: 817OO unique-id: ISI:000294683900024We investigate the use of phonetic motor invariants (MIs), that is, recurring kinematic patterns of the human phonetic articulators, to improve automatic phoneme discrimination. Using a multi-subject database of synchronized speech and lips/tongue trajectories, we first identify MIs commonly associated with bilabial and dental consonants, and use them to simultaneously segment speech and motor signals. We then build a simple neural network-based regression schema (called Audio-Motor Map, AMM) mapping audio features of these segments to the corresponding MIs. Extensive experimental results show that (a) a small set of features extracted from the MIs, as originally gathered from articulatory sensors, are dramatically more effective than a large, state-of-the-art set of audio features, in automatically discriminating bilabials from dentals; (b) the same features, extracted from AMM-reconstructed MIs, are as effective as or better than the audio features, when testing across speakers and coarticulating phonemes; and dramatically better as noise is added to the speech signal. These results seem to support some of the claims of the motor theory of speech perception and add experimental evidence of the actual usefulness of MIs in the more general framework of automated speech recognition

Can a robot catch you lying? A machine learning system to detect lies during interactions.

Deception is a complex social skill present in human interactions. Many social professions such as teachers, therapists and law enforcement officers leverage on deception detection techniques to support their working activities. Robots with the ability to autonomously detect deception could provide an important aid to human-human and human-robot interactions. The objective of this work is to demonstrate that it is possible to develop a lie detection system that could be implemented on robots. To this goal, we focus on human human and human robot interaction to understand if there is a difference in the behavior of the participants when lying to a robot or to a human. Participants were shown short movies of robberies and then interrogated by a human and by a humanoid robot "detectives". According to the instructions, subjects provided veridical responses to half of the question and false replies to the other half. Behavioral variables such as eye movements, time to respond and eloquence were measured during the task, while personality traits were assessed before experiment initiation. Participant's behavior showed strong similarities during the interaction with the human and the humanoid. Moreover, the behavioral features were used to train and test a lie detection algorithm. The results show that the selected behavioral variables are valid markers of deception both in human-human and in human-robot interactions and could be exploited to effectively enable robots to detect lies.

Refractive outcome in preterm newborns with ROP after propranolol treatment. A retrospective observational cohort study

Background: Recent explorative studies suggest that propranolol reduces retinopathy of prematurity (ROP) progression, but the short-term effects of propranolol treatment at 1 year of corrected age have not been extensively evaluated. Methods: A multi-center retrospective observational cohort study was conducted to assess the physical development and the refractive outcome of infants with prior ROP treated with propranolol. Forty-nine infants treated with propranolol were compared with an equal number of patients who did not receive any propranolol therapy and represent the control group, with comparable anthropometrical characteristics and stages of ROP. Results: The weight, length, and head circumference at 1 year of corrected age were similar between infants who had been treated, or not, with propranolol, without any statistically significant differences. Refractive evaluation at 1 year showed spherical equivalent values decreasing with the progression of ROP toward more severe stages of the disease, together with an increasing number of infants with severe myopia. On the contrary, no differences were observed between infants who had been treated with propranolol and those who had not. Conclusion: This study confirms that the progression of ROP induces an increase of refractive errors and suggests that propranolol itself does not affect the refractive outcome. Therefore, if the efficacy of propranolol in counteracting ROP progression is confirmed by further clinical trials, the conclusion will be that propranolol might indirectly improve the visual outcome, reducing the progression of ROP

Body composition parameters, immunonutritional indexes, and surgical outcome of pancreatic cancer patients resected after neoadjuvant therapy: A retrospective, multicenter analysis

Background and aims: Body composition parameters and immunonutritional indexes provide useful information on the nutritional and inflammatory status of patients. We sought to investigate whether they predict the postoperative outcome in patients with pancreatic cancer (PC) who received neoadjuvant therapy (NAT) and then pancreaticoduodenectomy. Methods: Data from locally advanced PC patients who underwent NAT followed by pancreaticoduodenectomy between January 2012 and December 2019 in four high-volume institutions were collected retrospectively. Only patients with two available CT scans (before and after NAT) and immunonutritional indexes (before surgery) available were included. Body composition was assessed and immunonutritional indexes collected were: VAT, SAT, SMI, SMA, PLR, NLR, LMR, and PNI. The postoperative outcomes evaluated were overall morbidity (any complication occurring), major complications (Clavien-Dindo ≥ 3), and length of stay. Results: One hundred twenty-one patients met the inclusion criteria and constituted the study population. The median age at the diagnosis was 64 years (IQR16), and the median BMI was 24 kg/m2 (IQR 4.1). The median time between the two CT-scan examined was 188 days (IQR 48). Skeletal muscle index (SMI) decreased after NAT, with a median delta of −7.8 cm2/m2 (p < 0.05). Major complications occurred more frequently in patients with a lower pre-NAT SMI (p = 0.035) and in those who gained in subcutaneous adipose tissue (SAT) compartment during NAT (p = 0.043). Patients with a gain in SMI experienced fewer major postoperative complications (p = 0.002). The presence of Low muscle mass after NAT was associated with a longer hospital stay [Beta 5.1, 95%CI (1.5, 8.7), p = 0.006]. An increase in SMI from 35 to 40 cm2/m2 was a protective factor with respect to overall postoperative complications [OR 0.43, 95% (CI 0.21, 0.86), p < 0.001]. None of the immunonutritional indexes investigated predicted the postoperative outcome. Conclusion: Body composition changes during NAT are associated with surgical outcome in PC patients who receive pancreaticoduodenectomy after NAT. An increase in SMI during NAT should be favored to ameliorate the postoperative outcome. Immunonutritional indexes did not show to be capable of predicting the surgical outcome

Seed Inoculation with Pseudomonas fluorescens Promotes Growth, Yield and Reduces Nitrogen Application in Maize.

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