Supplemental Material for True and False Recognition in MINERVA2: Integrating Fuzzy-Trace Theory and Computational Memory Modeling

ABSTRACTWe used the Fuzzy-Trace Theory (FTT) perspective to investigate the role of context switching or reinstatement on correct and false recognition in Deese–Roediger–McDermott (DRM) and simple false memory for occurrence (FMO) tasks. In two experiments, participants studied words presented against unique picture backgrounds. The results indicated that providing specific item-context associative information in the reinstated-context test condition enhances verbatim trace retrieval of targets but does not influence their gist retrieval. In the case of false recognitions of related distracters, manipulations of retrieval context resulted in different effects across experiments, putatively because the DRM and FMO tasks involve different latent-processes implied by FTT. The results confirmed the critical role of retrieval conditions for false and correct recognition memory and showed the insights that can be gained from the process-level multinomial modelling analyses of data obtained in particular experimental paradigms.

In the Deese-Roediger-McDermott-Read-Solso (DRMRS) procedure, participants study lists of words associated with central concepts (critical themes) that are not on the lists, then their memory is tested. Based on 224 estimates, the rate of False Recognition of the nonstudied critical themes was .59 (95% confidence interval of .56 to .61), which is smaller than the Hit rate of .75 for correct recognition of studied items (95% confidence interval of .73 to .77) but greater than various rates of False Alarms for other nonstudied items (ranging from .13 to .19). Ratings of subjective confidence were similar on Hits and on False Recognitions but higher than on False Alarms, confirming that false recognition was more like correct recognition than like other errors. The results from judgments of feeling of remembering or knowing, from the effects of intervening activities (particularly recall) between study and test, and from the effects of age suggest that False Recognition occurs because the critical theme is activated along with studied items during list presentation and perhaps also during recall. Invoking fuzzy trace theory, it is argued Hits are based on verbatim traces whereas False Recognition is based on gist traces and a failure of source memory. Proposals are made for research.

In studies of false recognition, subjects not only endorse items that they have never seen, but they also make subjective judgments that they remember consciously experiencing them. This is a difficult problem for most models of recognition memory, as they propose that false memories should be based on familiarity, not recollection. We present a new computational model of recollection, based on the Recognition through Semantic Synchronization (RSS) model of Johns, Jones, & Mewhort (Cognitive Psychology, 2012, 65, 486), and fuzzy trace theory (Brainerd & Reyna, Current Directions in Psychological Science, 2002, 11, 164), that offers a solution to this problem. In addition to standard true and false recognition results, the model successfully extends to explain multiple studies on both true and false recollection. This work suggests that recollection does not have to be thought of as a separate process from recognition, but instead as one that is reliant upon different information sources. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Fuzzy-trace theory and source monitoring: An evaluation of theory and false-memory data

Recent research on item-method directed forgetting demonstrates that forget instructions not only decrease recognition for targets, but also decrease false recognition for foils from the same semantic categories as targets instructed to be forgotten. According to the selective rehearsal account of directed forgetting, this finding suggests that remember instructions may engage elaborative rehearsal of the category-level information of items. In contrast to this explanation, Reid and Jamieson (Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale, 76(2), 75-86, 2022) proposed that the differential rates of false recognition may emerge at retrieval when foils from "remember" and "forget" categories are compared to traces in memory. Using MINERVA S, an instance model of memory based on MINERVA 2 that incorporates structured semantic representations, Reid and Jamieson successfully simulated lower false recognition for foils from "forget" categories without assuming rehearsal of category-level information. In this study, we extend the directed forgetting paradigm to categories consisting of orthographically related nonwords. Presumably participants would have difficulty rehearsing category-level information for these items because they would have no pre-experimental knowledge of these categories. To simulate the findings in MINERVA S, we imported structured orthographic representations rather than semantic representations. The model not only predicted differential rates of false recognition for foils from "remember" and "forget" categories, but also predicted higher rates of false recognition overall than what was observed for semantic categories. The empirical data closely matched these predictions. These data suggest that differential rates of false recognition due to remember and forget instructions emerge at retrieval when participants compare recognition probes to traces stored in memory.

ABSTRACTIn the present experiment, we investigated whether warnings provided at the time of retrieval would reduce emotional false memories in the Deese-Roediger-McDermott (DRM) paradigm. The provision of retrieval warnings allowed us to test specific predictions based on the associative theories (e.g., Activation-Monitoring Theory; AMT) and Fuzzy-Trace Theory (FTT) that have been used to account for false memories in the DRM paradigm. Participants were randomly assigned to either a no-warning group or a retrieval-warning group. In each group, mood-induction procedures were used to elicit a positive, negative, or neutral mood and participants were then presented with word lists comprised of positive, negative, or neutral words. Retrieval warnings reduced false recognition, regardless of the valence of the to-be-remembered information or participants’ mood. Consistent with the associative theories’ predictions, within the warning condition, positive moods yielded greater false recognition for positive critical lures, and negative moods yielded greater false recognition for negative critical lures, compared to neutral moods. These findings have important practical implications for our understanding of the effect of mood on memory.

Theory and computational modeling have played important roles in neuroscience. Models of neural systems range from coolly abstract to scrupulously biologically detailed, but the overwhelming majority have been implemented using imperative programming languages. Very recently, declarative programming approaches have entered the realm of computational neuroscience, including models implemented in Maude. The declarative approach promises deeper insights into neurobiology, especially into the pathological processes that underlie neurological disorders. This chapter will provide a very short overview of imperative and declarative modeling in neuroscience, and will then describe a specific example of a model of a key neural process implemented in Maude. The Maude model provides potential new insights that would be difficult to obtain using an imperative approach.

The management of environmental water resources continues to be a highly complex and uncertain field of natural resource management. As a result, there may be significant gaps in shared understanding of issues that are critical to decision-making. This article reports on a facilitation process leading to improved management guidelines for an Environmental Contingency Allowance in the Gwydir Valley, NSW, with an intentional focus on the communication of complex and uncertain management issues and on the integration of local non-specialist knowledge in analysis. The article includes a review of the methodological foundations of the facilitation process, including organisational learning, action research, applied systems theory and decision-support system modelling. The facilitation process includes development of a computer model which departs in some respects from a conventional decision-support system. In view of these differences and their epistemological foundation, the computer model and facilitation process are referred to jointly as a Learning Support System.

We investigated nanoparticle electrocatalysts in oxygen evolution reaction (OER) and CO2 reduction reaction (CO2RR) via an approach coupling electrochemistry with ultra-high vacuum (UHV) surface science and computational modeling. Well-defined nanoparticle electrocatalysts such as Fe2O3, NiFeOx, and Ag, were prepared in the UHV chamber via physical vapor deposition (PVD) and characterized with surface science techniques including X-ray photoelectron spectroscopy (XPS) and scanning tunneling microscopy (STM), followed by electrochemistry measurements to establish the structure-property relationships in OER and CO2RR. Calculations based on density functional theory (DFT) and microkinetic modeling (MKM) were then implemented to gain fundamental insight into these systems. We observed the edge-enhanced OER activity of Au-supported, ultra-thin Fe2O3 electrocatalysts and revealed incorporation of Ni at the edge sites (NiFeOx) further boosting their OER activity. We also resolved the size-dependent transition between CO2RR and H2 evolution reaction (HER) selectivity in sub-5 nm Ag electrocatalysts and identified an effective minimum size limit of ∼4 nm for active and selective Ag catalysts: particle diameters below this range experienced increased H2 evolutions due to a high population of Ag edge sites; and larger diameter particles favored CO2RR as the population of Ag(100) surface sites grew but will begin to lose catalyst utilization based on total metal masses.

This paper presents a theoretical investigation of the crystallization kinetics of thin adsorbed amorphous water films mainly based on our recently published experimental results [Lofgren et al., Langmuir 19 (2003) 265]. A nucleation simulation model, in which the time course of the growth of each crystalline grain is explicitly modelled, is presented and used to determine the location of the nucleation to be both in the bulk and at the substrate/water interface. The model also shows that a non-zeroth order of desorption from the bulk layer can be related to inhomogeneous adsorption. Further, a thermodynamic analysis of the experimental data is presented. At 145 K the critical size of a (bulk) ice nucleus is approximately 40 water molecules and the nucleation rate constant is 10−6 s−1.

The paper presents ORIENT-Net as a tool that processes texts in terms of their orientalist views. This is done by the detection and classification of such texts on social media Twitter. An Orientalist view of the East can see the cultures there as being quaint, obsolete, or entirely unlike the West, which Westerners can be very often influenced by. The demographic figures are also computed for the ORIENT-Net project, to be able to provide the necessary NLP, ML, and data mining solutions that are tailored to the specific demographics. The framework collects the relevant tweets on the basis of keywords that also cover a range of different kinds of Orientalist statements. The very subtle orientalist markers are identified through three linguistic features: TF-IDF, n-grams, and BERT embeddings that are the result of fine-tuning. The CatBoost classifier is the one that does the classification of the tweets into ‘orientalist’ and ‘non-orientalist’ categories, and the system’s performance is measured through accuracy evaluation, along with precision and F1-score assessments. The model relies on SHAP and LIME for interpretability and decision explanation. The framework makes online detection of oriental discourse automatic. The results indicate the high accuracy and clarity of orientalist identification. This system offers a scalable tool to support sociocultural research and inform evidence-based policy decisions. The uniqueness of this research is that it combines postcolonial theory and computational modelling via the ORIENT-Net framework, thus providing an interpretable and scalable method for online ideological discourse analysis. Future work will focus on expanding the dataset across multiple languages and platforms, as well as refining detection capabilities to capture more nuanced and context-dependent discourse patterns.

A solution of the Steinhaus's combinatorial problem

This review introduces alkali metal (Li, Na, and K) anode‐less and anode‐free batteries and conveys a synopsis of the current challenges regarding anode‐electrolyte interfaces. The review focuses on a critical analysis of the fundamental understanding of the (eletro)chemical and (electro)physical processes occurring at the anode, including metal nucleation and dendrite growth, the properties of liquid and solid electrolytes, and their roles in the metal stripping/deposition process and the formation of solid‐electrolyte interphase, and the properties of separators and their role in inhibiting dendrite growth. Solutions to tackle the challenges for anode‐less and anode‐free batteries are discussed extensively in the aspects of the modifications of the anode substrate, novel electrolyte solutions and SEI structures, interface design, and novel separators/solid‐state electrolytes to enable stable battery performances. To highlight the importance of bridging experimental and computational insights, experimental progress derived from a range of advanced characterization techniques is analyzed in combination with the advancement in multi‐scale theory and computational modeling. Finally, outlooks are provided from both experimental and computational points of view for the exciting field of zero‐excess alkali metal batteries.

Theory and modeling of chessboard-like self-assembling of vertically aligned columnar nanostructures in films has been developed. By means of modeling and three-dimensional computational simulations, we proposed a novel self-assembly process that can produce good chessboard nanostructure architectures through a pseudo-spinodal decomposition of an epitaxial film under optimal thermodynamic and crystallographic conditions (appropriate choice of the temperature, composition of the film, and crystal lattice parameters of the film and substrate). These conditions are formulated. The obtained results have been published on Nano Letters. Based on the principles of the formation of chessboard nanostructured films, we are currently trying to find good decomposing material systems that satisfy the optimal conditions to produce the chessboard nanostructure architecture. In addition we are under way doing 'computer experiments' to look for the appropriate materials with the chessboard columnar nanostructures, as a potential candidate for engineering of optical devices, high-efficiency multiferroics, and high-density magnetic perpendicular recording media. We are also currently to investigate the magnetoelectric response of multiferroic chessboard nanostructures under applied electric/magnetic fields. A unified 3-dimensional phase field theory of the strain-mediated magnetoelectric effect in magnetoelectric composites is developed. The theory is based on the established equivalency paradigm: we proved that by using a variational priciple the exact values of the electric, magnetic and strain fields in a magnetoelectric composite of arbitrary morphology and their coupled magneto-electric-mechanical response can be evaluated by considering an equivalent homogeneous system with the specially chosen effective eigenstrain, polarization and magnetization. These equivalency parameters are spatially inhomogeneous fields, which are obtained by solving the time-dependent Ginzburg-Landau equations. The paper summarizing these results is to be submitted to JAP. We are currently using the computational model based on the unified phase field theory to predict the local and overall response of the magnetoelectric composites with arbitrary configuration under applied fields, and to find the optimal composite microstructure that produces the strongest ME coupling. We have developed modeling and simulations to support Dr. S. Pryia efforts to produce the strongest ME coupling by searching the optimal configuration of applied electric/magnetic fields, and microstructure of polycrystalline multiferroics. An analytical model demonstrates that the optimization of a magnetoelectric (ME) coupling of a laminar magnetic/piezoelectric polycrystalline composite could be obtained by a proper choice of the magnetic and electric poling directions and the directions of the applied a.c. fields. The results have been published on JAP. Our next step is to determine the domain of optimal parameters and configurations by using our optimization theory and computational modeling.

Protecting civil infrastructure from natural and man-made hazards is vital. Understanding the impact of these hazards helps allocate resources efficiently. Researchers have recently proposed static and dynamic computational models for community resilience analyses to evaluate a community’s ability to recover after a disruptive event. Yet, these frameworks still need to adequately address community interdependencies and consider the impact of decision-making in modeling. This paper presents a state-of-the-art review of computational methods to model community resilience, focusing on the last 10 years. It addresses critical terminology, community interdependencies, and current resilience guides within community resilience comprehension and discusses static and dynamic computational models, including probabilistic modeling in uncertain environments, rating models for community resilience assessment, optimization-based modeling for resilient community design, game theory, agent-based, and probabilistic dynamical modeling. This paper presents key findings of promising research for future directions in the community resilience field.