Formal Computational Model Research Articles

Modeling collaborative memory with SAM.

While humans often encode and retrieve memories in groups, the bulk of our knowledge of human memory comes from paradigms with individuals in isolation. The primary phenomenon of interest within the relatively new field of collaborative memory is collaborative inhibition: the tendency for collaborative groups to underperform in free recall tasks compared with noncollaborative groups of the same size. This effect has been found in a variety of materials and group compositions. However, most research in this field is led by verbal conceptual theories without guidance from formal computational models. We present a framework to scale the Search of Associative Memory model (SAM) to collaborative free recall paradigms with multiple models working together. Multiple SAM models recalling together naturally produce collaborative inhibition when the group members use recalls by the group as cues to retrieve from memory, strongly supporting the "retrieval disruption" hypothesis. This work shows that SAM can act as a unified theory to explain both individual and collaborative memory effects and offers a framework for future predictions of scaling to increased group sizes, shared knowledge, and factors facilitating the spread of false memories in groups.

Self-organization in computation and chemistry: Return to AlChemy.

How do complex adaptive systems, such as life, emerge from simple constituent parts? In the 1990s, Walter Fontana and Leo Buss proposed a novel modeling approach to this question, based on a formal model of computation known as the λ calculus. The model demonstrated how simple rules, embedded in a combinatorially large space of possibilities, could yield complex, dynamically stable organizations, reminiscent of biochemical reaction networks. Here, we revisit this classic model, called AlChemy, which has been understudied over the past 30 years. We reproduce the original results and study the robustness of those results using the greater computing resources available today. Our analysis reveals several unanticipated features of the system, demonstrating a surprising mix of dynamical robustness and fragility. Specifically, we find that complex, stable organizations emerge more frequently than previously expected, that these organizations are robust against collapse into trivial fixed points, but that these stable organizations cannot be easily combined into higher order entities. We also study the role played by the random generators used in the model, characterizing the initial distribution of objects produced by two random expression generators, and their consequences on the results. Finally, we provide a constructive proof that shows how an extension of the model, based on the typed λ calculus, could simulate transitions between arbitrary states in any possible chemical reaction network, thus indicating a concrete connection between AlChemy and chemical reaction networks. We conclude with a discussion of possible applications of AlChemy to self-organization in modern programming languages and quantitative approaches to the origin of life.

An Axiomatic Theory for Reversible Computation

Undoing computations of a concurrent system is beneficial in many situations, such as in reversible debugging of multi-threaded programs and in recovery from errors due to optimistic execution in parallel discrete event simulation. A number of approaches have been proposed for how to reverse formal models of concurrent computation, including process calculi such as CCS, languages like Erlang, and abstract models such as prime event structures and occurrence nets. However, it has not been settled as to what properties a reversible system should enjoy, nor how the various properties that have been suggested, such as the parabolic lemma and the causal-consistency property, are related. We contribute to a solution to these issues by using a generic labelled transition system equipped with a relation capturing whether transitions are independent to explore the implications between various reversibility properties. In particular, we show how all properties we consider are derivable from a set of axioms. Our intention is that when establishing properties of some formalism, it will be easier to verify the axioms rather than proving properties such as the parabolic lemma directly. We also introduce two new properties related to causal-consistent reversibility, namely causal liveness and causal safety, stating, respectively, that an action can be undone if (causal liveness) and only if (causal safety) it is independent from all of the following actions. These properties come in three flavours: defined in terms of independent transitions, independent events, or via an ordering on events. Both causal liveness and causal safety are derivable from our axioms.

Development of a Computational Model for Cassava Food Processing Using Coloured Petri Net

A food system is composed of a complex network of activities and processes for production, distribution, transportation and consumption, which interact with each other, thus leading to changeable behaviour. Most existing empirical studies on cassava processing have focused on the technical efficiency analysis of the cassava crop processing techniques among processors indicating that the modelling of the events and operations involved in the processing of the cassava crop is highly limited. In this context, different strategies have been used to solve difficult environmental and agro-informatic systems model-based problems such as system dynamics, agent based, rule-based knowledge and mathematical modeling. However, the structural comprehension and behavioral investigation of this modeling are constrained. In this regard, formal computational modeling is a method that enables modeling and simulation of the dynamical characteristics of these food systems to be examined. In this study, the system specification is designed using Unified Modelling language (UML) to show the structural process and system design modelled and simulated using Coloured Petri Net (CPN), a formal method for analyzing the behavioural properties of complex system because of its efficient analysis. For the purpose of observing and analyzing the behaviour of the cassava food process, a series of simulation runs was proposed.

Facial typicality and attractiveness reflect an ideal dimension of face structure

Face perception and recognition are important processes for social interaction and communication among humans, so understanding how faces are mentally represented and processed has major implications. At the same time, faces are just some of the many stimuli that we encounter in our everyday lives. Therefore, more general theories of how we represent objects might also apply to faces. Contemporary research on the mental representation of faces has centered on two competing theoretical frameworks that arose from more general categorization research: prototype-based face representation and exemplar-based face representation. Empirically distinguishing between these frameworks is difficult and neither one has been ruled out. In this paper, we advance this area of research in three ways. First, we introduce two additional frameworks for mental representation of categories, varying abstraction and ideal representation, which have not been applied to face perception and recognition before. Second, we fit formal computational models of all four of these theories to human perceptual judgments of the typicality and attractiveness (a strong correlate of typicality) of 100 young adult Caucasian female faces, with the models expressed within a face space derived from facial similarity judgments via multidimensional scaling. Third, we predict the perceived typicality and attractiveness of the faces using these models and compare the predictive performance of each to the empirical data. We found that of all four models, the ideal representation model provided the best account of perceived typicality and attractiveness for the present set of faces, although all models showed discrepancies from the empirical data. These findings demonstrate the relevance of mental categorization processes for representing faces.

Category structure and region-specific selective attention.

A fundamental component of human categorization involves learning to attend selectively to relevant dimensions and ignore irrelevant ones. Past research has shown that humans can learn flexible strategies in which the attended dimensions vary depending on the region of feature space in which classification takes place. However, region-specific selective attention (RSA) is often challenging to learn. Here, we test the hypothesis that RSA is facilitated when individual categories are embedded within single regions of stimulus space rather than dispersed across multiple regions. We conduct an experiment that varies across conditions whether categories are embedded within regions, but in which the same RSA strategy would benefit performance across the conditions. To evaluate the hypothesis, we use measures of overall performance accuracy as well as comparisons among formal computational models that do and do not make allowance for RSA. We find strong support for the hypothesis among the upper-median-performing participants in the tested groups. However, even in the condition that promotes the learning of RSA, performance is considerably worse than in comparison conditions in which a single set of dimensions can be attended throughout the entire stimulus space.

A Bayesian computational model to investigate expert anticipation of a seemingly unpredictable ball bounce

During dynamic and time-constrained sporting tasks performers rely on both online perceptual information and prior contextual knowledge to make effective anticipatory judgments. It has been suggested that performers may integrate these sources of information in an approximately Bayesian fashion, by weighting available information sources according to their expected precision. In the present work, we extended Bayesian brain approaches to anticipation by using formal computational models to estimate how performers weighted different information sources when anticipating the bounce direction of a rugby ball. Both recreational (novice) and professional (expert) rugby players (n = 58) were asked to predict the bounce height of an oncoming rugby ball in a temporal occlusion paradigm. A computational model, based on a partially observable Markov decision process, was fitted to observed responses to estimate participants’ weighting of online sensory cues and prior beliefs about ball bounce height. The results showed that experts were more sensitive to online sensory information, but that neither experts nor novices relied heavily on prior beliefs about ball trajectories in this task. Experts, but not novices, were observed to down-weight priors in their anticipatory decisions as later and more precise visual cues emerged, as predicted by Bayesian and active inference accounts of perception.

Models of Identity Signaling

Identity signals inform receivers of a signaler’s membership in a subset of individuals, and in doing so shape cooperation, conflict, and social learning. Understanding the use and consequences of identity signaling is therefore critical for a complete science of collective human behavior. As is true for all complex social systems, this understanding is aided by the use of formal mathematical and computational models. Here I review some formal models of identity signaling. I divide these models into two categories. First, I discuss models used to study how identity functions as a signal, with a focus on public-health-related behavior and disease transmission. Second, I discuss models used to understand how identity signals operate strategically in different social environments, with a focus on covert, or encrypted, communication.

Hardware, Software, and Wetware Codesign Environment for Synthetic Biology.

Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.

Towards a formal model of computation for RMAS

RMAS is a multi-agent system architecture and associated model of computation that has been recently proposed as promising framework for autonomic computation and as viable implementation means for industrial agents. RMAS is proposed as an effective tool in the realization of the full-fledged concept of cyber-physical systems of systems, in agreement with new visions of the digital future and prominent standardization efforts like the IEC 61499. Currently, there is the need to establish a solid formalism for the model of computation of RMAS in order to harness and fully express its possibilities. In particular, this model should bridge the disciplines of control and systems engineering and computer science, whilst concerning artificial intelligence. This work proposes a first operational step in this direction. A formalism of the RMAS model of computation is established for the first time, and a complete definition of hierarchical nested levels of RMAS units is proposed. This work constitutes the ground for industry-ready technological instances of the RMAS architecture.

A Formal Framework for Complex Event Recognition

Complex event recognition (CER) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real time. CER finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. Existing CER languages lack a clear semantics, however, which makes them hard to understand and generalize. Moreover, there are no general techniques for evaluating CER query languages with clear performance guarantees.In this article, we embark on the task of giving a rigorous and efficient framework to CER. We propose a formal language for specifying complex events, calledcomplex event logic(CEL), that contains the main features used in the literature and has a denotational and compositional semantics. We also formalize the so-called selection strategies, which had only been presented as by-design extensions to existing frameworks. We give insight into the language design trade-offs regarding the strict sequencing operators of CEL and selection strategies.With a well-defined semantics at hand, we discuss how to efficiently process complex events by evaluating CEL formulas with unary filters. We start by introducing a formal computational model for CER, calledcomplex event automata(CEA), and study how to compile CEL formulas with unary filters into CEA. Furthermore, we provide efficient algorithms for evaluating CEA over event streams using constant time per event followed by output-linear delay enumeration of the results.

A Bayesian inference model for metamemory.

The dual-basis theory of metamemory suggests that people evaluate their memory performance based on both processing experience during the memory process and their prior beliefs about overall memory ability. However, few studies have proposed a formal computational model to quantitatively characterize how processing experience and prior beliefs are integrated during metamemory monitoring. Here, we introduce a Bayesian inference model for metamemory (BIM) which provides a theoretical and computational framework for the metamemory monitoring process. BIM assumes that when people evaluate their memory performance, they integrate processing experience and prior beliefs via Bayesian inference. We show that BIM can be fitted to recall or recognition tasks with confidence ratings on either a continuous or discrete scale. Results from data simulation indicate that BIM can successfully recover a majority of generative parameter values, and demonstrate a systematic relationship between parameters in BIM and previous computational models of metacognition such as the stochastic detection and retrieval model (SDRM) and the meta-d' model. We also show examples of fitting BIM to empirical data sets from several experiments, which suggest that the predictions of BIM are consistent with previous studies on metamemory. In addition, when compared with SDRM, BIM could more parsimoniously account for the data of judgments of learning (JOLs) and memory performance from recall tasks. Finally, we discuss an extension of BIM which accounts for belief updating, and conclude with a discussion of how BIM may benefit metamemory research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A model of symbiomemesis: machine education and communication as pillars for human-autonomy symbiosis.

Symbiosis is a physiological phenomenon where organisms of different species develop social interdependencies through partnerships. Artificial agents need mechanisms to build their capacity to develop symbiotic relationships. In this paper, we discuss two pillars for these mechanisms: machine education (ME) and bi-directional communication. ME is a new revolution in artificial intelligence (AI) which aims at structuring the learning journey of AI-enabled autonomous systems. In addition to the design of a systematic curriculum, ME embeds the body of knowledge necessary for the social integration of AI, such as ethics, moral values and trust, into the evolutionary design and learning of the AI. ME promises to equip AI with skills to be ready to develop logic-based symbiosis with humans and in a manner that leads to a trustworthy and effective steady-state through the mental interaction between humans and autonomy; a state we name symbiomemesis to differentiate it from ecological symbiosis. The second pillar, bi-directional communication as a discourse enables information to flow between the AI systems and humans. We combine machine education and communication theory as the two prerequisites for symbiosis of AI agents and present a formal computational model of symbiomemesis to enable symbiotic human-autonomy teaming. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Formal Design of Multi-Function Vehicle Bus Controller

Data of the train communication network(TCN) is becoming more complicated, which results in higher requirements of the data processing unit-the multifunction vehicle bus controller (MVBC) connected within the TCN. Developing an MVBC is challenging because of the integrated hardware-software solutions to support reactions in real time and dynamic environment. Hence, there is an urgent need for a rigorous design framework to facilitate the development of MVBC. In this paper, we propose a design framework TooMVBC to generate executable MVBC code from formal verified computation model. TooMVBC uses formal computation model MVBChart to capture the specification of the MVBC at high level. First, primitive syntax of MVBChart is designed to model MVBC features (e.g. hierarchy structure, data flow of the encoder, the control logic of communication protocol), and semantics of MVBChart is formalized for simulation and verification. Then, semantics-preserving code generation algorithms are designed to generate VHDL code for partitioned hardware implementations and C code for partitioned software implementations from verified MVBChart model. The generated code can be loaded into the proposed flexible MVBC hardware architecture directly. Finally, supporting graphical model editor, simulator, verification translator, partitioning and code generator are implemented and seamlessly integrated into TooMVBC. When we apply TooMVBC to design MVBC with the highest class 5 according to the description of the standard IEC 61375, several critical ambiguousness or bugs in the standard are detected during formal verification of the constructed system model.

Reasoning on the Efficiency of Distributed Complex Event Processing

Complex event processing (CEP) evaluates queries over streams of event data to detect situations of interest. If the event data are produced by geographically distributed sources, CEP may exploit in-network processing that distributes the evaluation of a query among the nodes of a network. To this end, a query is modularized and individual query operators are assigned to nodes, especially those that act as data sources. Existing solutions for such operator placement, however, are limited in that they assume all query results to be gathered at one designated node, commonly referred to as a sink. Hence, existing techniques postulate a hierarchical structure of the network that generates and processes the event data. This largely neglects the optimisation potential that stems from truly decentralised query evaluation with potentially many sinks. To address this gap, in this paper, we propose Multi-Sink Evaluation (MuSE) graphs as a formal computational model to evaluate common CEP queries in a decentralised manner. We further prove the completeness of query evaluation under this model. Striving for distributed CEP that can scale to large volumes of high-frequency event streams, we show how to reason on the network costs induced by distributed query evaluation and prune inefficient query execution plans. As such, our work lays the foundation for distributed CEP that is both, sound and efficient.

Deeply Felt Affect: The Emergence of Valence in Deep Active Inference.

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model—an internal estimate of overall model fitness (“subjective fitness”). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

Chapter 3 - Alexithymia

Humans are highly adept at differentiating, regulating, and responding to their emotions. At the core of all these functions is emotional awareness: the conscious feeling states that are central to human mental life. Disrupted emotional awareness-a subclinical construct commonly referred to as alexithymia-is present in a range of psychiatric and neurological disorders and can have a deleterious impact on functional outcomes and treatment response. This chapter is a selective review of the current state of the science on alexithymia. We focus on two separate but related issues: (i) the functional deficits associated with alexithymia and what they reveal about the importance of emotional awareness for shaping normative human functioning, and (ii) the neural correlates of alexithymia and what they can inform us about the biological bases of emotional awareness. Lastly, we outline challenges and opportunities for alexithymia research, focusing on measurement issues and the potential utility of formal computational models of emotional awareness for advancing the fields of clinical and affective science.

Simulating reversible computation with reaction systems

Reaction systems are a formal model of computation providing a framework for investigating biochemical reactions inside living cells. We look at the functioning of these systems as a process producing a series of different possible sets of entities representing states which can be changed by the application of reactions, and we study reversibility and its simulation in this framework. Our goal is to establish an Undo-Redo-Do-like semantics of reversibility with environmental control over the direction of the computation following a so-called no-memory approach, that is, without introducing modifications to the model of reaction systems itself. We first establish requirements the systems must satisfy in order to produce processes consisting of states with unique predecessors, then define reversible reaction systems in terms of reversible interactive processes. For such reversible systems, we also construct simulator systems that can traverse between the states of reversible interactive processes back and forth based on the input of a special “rollback” symbol from the environment.

Facilitation in reaction systems

Reaction systems is a formal model of computation which originated as a model of interactions between biochemical reactions in the living cell. These interactions are based on two mechanisms, facilitation and inhibition, and this is well reflected in the formulation of reaction systems. In this paper, we investigate the facilitation aspect of reaction systems, where the products of a reaction may facilitate other reactions by providing some of their reactants. This aspect is formalized through positive dependency graphs which depict explicitly such facilitating interactions. The focus of the paper is on demonstrating how structural properties of reaction systems defined through the properties of their positive dependency graphs influence the behavioural properties of (suitable subclasses of) reaction systems, which, as usual, are defined through their transition graphs.

A general response process theory for situational judgment tests.

Situational judgment tests (SJTs) have emerged as a staple of assessment methodologies for organizational practitioners and researchers. Despite their prevalence, many questions regarding how to interpret respondent choices or how variations in item construction and instruction influence the nature of observed responses remain. Existing conceptual and empirical efforts to explore these questions have largely been rooted in reflexive psychometric measurement models that describe participant responses as indicative of (usually multiple) latent constructs. However, some have suggested that a key to better understanding SJT responses lies in unpacking the judgment and decision-making processes employed by respondents and the psychological and contextual factors that shape how those processes play out. To this end, the present article advances an integrative and generalizable process-oriented theory of SJT responding. The framework, labeled situated reasoning and judgment (SiRJ), proposes that respondents engage in a series of conditional reasoning, similarity, and preference accumulation judgments when deciding how to evaluate and respond to an SJT item. To evaluate the theory's plausibility and utility, the SiRJ framework is translated into a formal computational model and results from a simulation study are analyzed using neural network and Bayesian survival analytic techniques that demonstrate its capability to replicate existing and new empirical effects, suggest insights into SJT interpretation and development, and stimulate new directions for future research. An interactive web application that allows users to explore the computational model developed for SiRJ (https://grandjam.shinyapps.io/sirj) as well as all reported data and the full model/simulation code (https://osf.io/uwdfm/) are also provided. (PsycInfo Database Record (c) 2020 APA, all rights reserved).