Embodied sequential sampling models and dynamic neural fields for decision-making: Why hesitate between two when a continuum is the answer.

As two alternative options in a forced choice task are separated by design, two classes of computational models of decision-making have thrived independently in the literature for nearly five decades. While sequential sampling models (SSM) focus on response times and keypresses in binary decisions in experimental paradigms, dynamic neural fields (DNF) focus on continuous sensorimotor dimensions and tasks found in perception and robotics. Recent attempts have been made to address limitations in their application to other domains, but strong similarities and compatibility between prominent models from both classes were hardly considered. This article is an attempt at bridging the gap between these classes of models, and simultaneously between disciplines and paradigms relying on binary or continuous responses. A unifying formulation of representative SSM and DNF equations is proposed, varying the number of units which interact and compete to reach a decision. The embodiment of decisions is also considered by coupling cognitive and sensorimotor processes, enabling the model to generate decision trajectories at trial level. The resulting mechanistic model is therefore able to target different paradigms (forced choices or continuous response scales) and measures (final responses or dynamics). The validity of the model is assessed statistically by fitting empirical distributions obtained from human participants in moral decision-making mouse-tracking tasks, for which both dichotomous and nuanced responses are meaningful. Comparing equations at the theoretical level, and model parametrizations at the empirical level, the implications for psychological decision-making processes, as well as the fundamental assumptions and limitations of models and paradigms are discussed.

Improving power ramp rate of a coal-fired power plant by a bypass steam accumulator.

The increasing penetration of high-volatility renewable energy sources in the power system presents higher demands for flexibility from coal-fired power plant (CFPP). To enhance the flexibility of CFPPs, researchers have conducted a significant amount of thermal-system-level research in recent years on increasing system peak shaving depth. However, the load ramp rate of CFPPs under deep peak shaving is rarely discussed, despite its significance to the overall flexibility performance of CFPPs. This paper proposes a steam accumulator storage system integrating to the turbine's bypass system. The steam accumulator charges directly with working fluid from the live steam or reheat systems and discharge to the turbine, responding quickly to power ramp commands. A steady state model and a dynamic model of the proposed system were built and validated, and the calculation shows that the proposed scheme provides a load change of +2.13 % Pe and -8.3%Pe during a round-trip with a power efficiency of 63.6 % at a unit load of 40 % THA. The unit's load increase rate under coordinated control was enhanced by 1.5 % Pe/min, reaching 3 % Pe/min, using the proposed steam accumulator without revising the original controls, and the load decrease rate reached at least 5 % Pe/min. The results indicate that the proposed system provides a straightforward, easy-to-implement, and efficient solution for enhancing the load ramp rate of CFPPs at low loads.

Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees.

Extracting temporal regularities and relations from experience/observation is critical for organisms' adaptiveness (communication, foraging, predation, prediction) in their ecological niches. Therefore, it is not surprising that the internal clock that enables the perception of seconds-to-minutes-long intervals (interval timing) is evolutionarily well-preserved across many species of animals. This comparative claim is primarily supported by the fact that the timing behavior of many vertebrates exhibits common statistical signatures (e.g., on-average accuracy, scalar variability, positive skew). These ubiquitous statistical features of timing behaviors serve as empirical benchmarks for modelers in their efforts to unravel the processing dynamics of the internal clock (namely answering how internal clock "ticks"). In this chapter, we introduce prominent (neuro)computational approaches to modeling interval timing at a level that can be understood by general audience. These models include Treisman's pacemaker accumulator model, the information processing variant of scalar expectancy theory, the striatal beat frequency model, behavioral expectancy theory, the learning to time model, the time-adaptive opponent Poisson drift-diffusion model, time cell models, and neural trajectory models. Crucially, we discuss these models within an overarching conceptual framework that categorizes different models as threshold vs. clock-adaptive models and as dedicated clock/ramping vs. emergent time/population code models.

Heavy metals and potential health risk assessment of Lactuca sativa and Daucus carrota from soil treated with organic manures and chemical fertilizer.

The large-scale production of food crops with heavy application of chemical fertilizers in the effort to meet the astronomical increase in food demands may be counterproductive to the goal of food security. This study investigated the effect of different soil treatments on the levels of heavy metals (Cr, Cu, Fe, Ni, Pb, and Zn) in two types of vegetables Lactuca sativa (lettuce) and Daucus carrota (carrot). The potential carcinogenic and non-carcinogenic health risks from their consumption were also evaluated. Planting experiment was set up in a randomized block design, with different soil treatments of soil + cow dung (CD), soil + sewage sludge (SS), soil + chemical fertilizer (nitrogen-phosphorus-potassium (NPK)), and untreated soil (UNTRD). The vegetables were harvested at maturity, washed with distilled water, and subjected to an acid digestion process before the levels of heavy metals were measured by inductively coupled plasma spectrometry (ICP-MS). The mean concentrations of the metals in the vegetables across all treatments were below the maximum permissible limits. The pattern of heavy metal accumulation by the vegetables suggested that the lettuce from SS treatment accumulated higher concentrations of heavy metals like Cr (0.20 mg/kg), Cu (3.91 mg/kg), Ni (0.33 mg/kg), and Zn (20.44 mg/kg) than carrot, with highest concentrations of Fe (90.89 mg/kg) and Pb (0.16 mg/kg) recorded in lettuce from NPK treatment. The bioaccumulation factor (BAF) showed that lettuce, a leafy vegetable, has bioaccumulated more heavy metals than carrot, a root vegetable. The BAF was generally below the threshold value of 1 in both vegetables, except in lettuce from NPK and CD treatments and carrot from NPK treatments, with BAF values of 1.6, 1.69, and 1.39, respectively. The cancer risk assessment factors were well below the unacceptable maximum range of 10-4 suggesting that consuming these vegetables might not expose an individual to potential risk of cancer development. The hazard quotient estimations were below the threshold values of 1 for all heavy metals; however, the hazard index (HI) values of 1.27 and 1.58 for lettuce from NPK and SS treatments indicate a potential non-carcinogenic health risk to consumers from intake of all the heavy metals.

Parkinson's disease impairs cortical sensori-motor decision-making cascades.

The transformation from perception to action requires a set of neuronal decisions about the nature of the percept, identification and selection of response options and execution of the appropriate motor response. The unfolding of such decisions is mediated by distributed representations of the decision variables-evidence and intentions-that are represented through oscillatory activity across the cortex. Here we combine magneto-electroencephalography and linear ballistic accumulator models of decision-making to reveal the impact of Parkinson's disease during the selection and execution of action. We used a visuomotor task in which we independently manipulated uncertainty in sensory and action domains. A generative accumulator model was optimized to single-trial neurophysiological correlates of human behaviour, mapping the cortical oscillatory signatures of decision-making, and relating these to separate processes accumulating sensory evidence and selecting a motor action. We confirmed the role of widespread beta oscillatory activity in shaping the feed-forward cascade of evidence accumulation from resolution of sensory inputs to selection of appropriate responses. By contrasting the spatiotemporal dynamics of evidence accumulation in age-matched healthy controls and people with Parkinson's disease, we identified disruption of the beta-mediated cascade of evidence accumulation as the hallmark of atypical decision-making in Parkinson's disease. In frontal cortical regions, there was inefficient processing and transfer of perceptual information. Our findings emphasize the intimate connection between abnormal visuomotor function and pathological oscillatory activity in neurodegenerative disease. We propose that disruption of the oscillatory mechanisms governing fast and precise information exchanges between the sensory and motor systems contributes to behavioural changes in people with Parkinson's disease.

Moving forward to understand the alteration of physiological mechanism by seed priming with different halo-agents under salt stress.

Soil salinity hampers the survival and productivity of crops. To minimize salt-associated damages in plant, better salt management practices in agriculture have become a prerequisite. Seed priming with different halo-agents is a technique, which improves the primed plant's endurance to tackle sodium. Salt tolerance is achieved in tolerant plants through fundamental physiological mechanisms- ion-exclusion and tissue tolerance, and salt-tolerant plants may (Na+ accumulators) or may not (Na+ excluders) allow sodium movement to leaves. While Na+ excluders depend on ion exclusion in roots, Na+ accumulators are proficient Na+ managers that can compartmentalize Na+ in leaves and use them beneficially as inexpensive osmoticum. Salt-sensitive plants are Na+ accumulators, but their inherent tissue tolerance ability and ion-exclusion process are insufficient for tolerance. Seed priming with different halo-agents aids in 'rewiring' of the salt tolerance mechanisms of plants. The resetting of the salt tolerance mechanism is not universal for every halo-agent and might vary with halo-agents. Here, we review the physiological mechanisms that different halo-agents target to confer enhanced salt tolerance in primed plants. Calcium and potassium-specific halo-agents trigger Na+ exclusion in roots, thus ensuring a low amount of Na+ in leaves. In contrast, Na+-specific priming agents favour processes for Na+ inclusion in leaves, improve plant tissue tolerance or vacuolar sequestration, and provide the greatest benefit to salt-sensitive and sodium accumulating plants. Overall, this review will help to understand the underlying mechanism behind plant's inherent nature towards salt management and its amelioration with different halo-agents, which helps to optimize crop stress performance.

Temporal foundations of episodic memory.

A fundamental question in the development of animal models of episodic memory concerns the role of temporal processes in episodic memory. Gallistel, (1990) developed a framework in which animals remember specific features about an event, including the time of occurrence of the event and its location in space. Gallistel proposed that timing is based on a series of biological oscillators, spanning a wide range of periods. Accordingly, a snapshot of the phases of multiple oscillators provides a representation of the time of occurrence of the event. I review research on basic timing mechanisms that may support memory for times of occurrence. These studies suggest that animals use biological oscillators to represent time. Next, I describe recently developed animal models of episodic memory that highlight the importance of temporal representations in memory. One line of research suggests that an oscillator representation of time supports episodic memory. A second line of research highlights the flow of events in time in episodic memory. Investigations that integrate time and memory may advance the development of animal models of episodic memory.

Integrating word-form representations with global similarity computation in recognition memory.

In recognition memory, retrieval is thought to occur by computing the global similarity of the probe to each of the studied items. However, to date, very few global similarity models have employed perceptual representations of words despite the fact that false recognition errors for perceptually similar words have consistently been observed. In this work, we integrate representations of letter strings from the reading literature with global similarity models. Specifically, we employed models of absolute letter position (slot codes and overlap models) and relative letter position (closed and open bigrams). Each of the representations was used to construct a global similarity model that made contact with responses and RTs at the individual word level using the linear ballistic accumulator (LBA) model (Brown & Heathcote Cognitive Psychology, 57 , 153-178, 2008). Relative position models were favored in three of the four datasets and parameter estimates suggested additional influence of the initial letters in the words. When semantic representations from the word2vec model were incorporated into the models, results indicated that orthographic representations were almost equally consequential as semantic representations in determining inter-item similarity and false recognition errors, which undermines previous suggestions that long-term memory is primarily driven by semantic representations. The model was able to modestly capture individual word variability in the false alarm rates, but there were limitations in capturing variability in the hit rates that suggest that the underlying representations require extension.

Clarifying the nature of stochastic fluctuations and accumulation processes in spontaneous movements.

Experiments on choice-predictive brain signals have played an important role in the debate on free will. In a seminal study, Benjamin Libet and colleagues found that a negative-going EEG signal, the readiness potential (RP), can be observed over motor-related brain regions even hundreds of ms before the time of the conscious decision to move. If the early onset of the readiness potential is taken as an indicator of the "brain's decision to move" this could mean that this decision is made early, by unconscious brain activity, rather than later, at the time when the subject believes to have decided. However, an alternative kind of interpretation, involving ongoing stochastic fluctuations, has recently been brought to light. This stochastic decision model (SDM) takes its inspiration from leaky accumulator models of perceptual decision making. It suggests that the RP originates from an accumulation of ongoing stochastic fluctuations. In this view, the decision happens only at a much later stage when an accumulated noisy signal (plus imperative) reaches a threshold. Here, we clarify a number of confusions regarding both the evidence for the stochastic decision model as well as the interpretation that it offers. We will explore several points that we feel are in need of clarification: (a) the empirical evidence for the role of stochastic fluctuations is so far only indirect; (b) the interpretation of animal studies is unclear; (c) a model that is deterministic during the accumulation stage can explain the data in a similar way; (d) the primary focus in the literature has been on the role of random fluctuations whereas the deterministic aspects of the model have been largely ignored; (e) contrary to the original interpretation, the deterministic component of the model is quantitatively the dominant input into the accumulator; and finally (f) there is confusion regarding the role of "imperative" in the SDM and its link to "evidence" in perceptual decision making. Our aim is not to rehabilitate the role of the RP in the free will debate. Rather we aim to address some confusions regarding the evidence for accumulators playing a role in these preparatory brain processes.

Applying the Accumulator model to predict driver's reaction time based on looming in approaching and braking conditions.

INTRODUCTION: The prediction of when the driver will react to a change in the lead vehicle motion is critical for assessing rear-end crash risk using car-following models. Past studies have assumed constant reaction time and driver's continuous reaction. However, these assumptions are not valid as the driver's reaction time can vary in different car-following situations and the driver does not continuously react to the lead vehicle motion. Thus, this study predicted the driver's reaction time using the Wiedemann car-following model and the Accumulator model. The Accumulator model assumes the driver's start of reaction based on the accumulation of looming and thereby reflects the driver's intermittent reaction. METHOD: Fifty drivers' behavior was observed using a driving simulator in two scenarios: (1) approach and follow a moving lead vehicle and (2) approach a stopped lead vehicle. The Accumulator model predicted the reaction times based on different looming variables (angular velocity and tau-inverse), lead vehicle type (car and truck), and lead vehicle brake lights (on or off). RESULTS: The Accumulator model showed lower prediction errors of the reaction time than the Wiedemann model, which assumes reaction based on the fixed looming threshold. The Accumulator model predicted the reaction times more accurately when it was calibrated with the angular velocity due to width and height of lead vehicles. Moreover, the Accumulator model with tau-inverse produced the smallest prediction error of reaction times among different Accumulator models and the Wiedemann model when lead vehicle brake lights were on. CONCLUSIONS: This study demonstrates that the Accumulator model is a promising method of predicting the driver's reaction time in car-following situations, which affects rear-end crash risk. PRACTICAL APPLICATIONS: The Accumulator model can be incorporated into a car-following model for the prediction of reaction times and can estimate the rear-end collision risk of vehicles more accurately.

Items Outperform Adjectives in a Computational Model of Binary Semantic Classification.

Semantic memory encompasses one's knowledge about the world. Distributional semantic models, which construct vector spaces with embedded words, are a proposed framework for understanding the representational structure of human semantic knowledge. Unlike some classic semantic models, distributional semantic models lack a mechanism for specifying the properties of concepts, which raises questions regarding their utility for a general theory of semantic knowledge. Here, we develop a computational model of a binary semantic classification task, in which participants judged target words for the referent's size or animacy. We created a family of models, evaluating multiple distributional semantic models, and mechanisms for performing the classification. The most successful model constructed two composite representations for each extreme of the decision axis (e.g., one averaging together representations of characteristically big things and another of characteristically small things). Next, the target item was compared to each composite representation, allowing the model to classify more than 1,500 words with human-range performance and to predict response times. We propose that when making a decision on a binary semantic classification task, humans use task prompts to retrieve instances representative of the extremes on that semantic dimension and compare the probe to those instances. This proposal is consistent with the principles of the instance theory of semantic memory.

Patient perceptions of copay card utilization and policies.

BACKGROUND: Copay cards are intended to mitigate patient out-of-pocket (OOP) expenses. This qualitative, exploratory focus group study aimed to capture patient perceptions of copay cards and copay adjustment programs (CAPs; insurers' accumulator and maximizer policies), which redirect the copay card utilization benefits intended for patients' OOP expenses. METHODS: Patients with chronic conditions were recruited through Janssen's Patient Engagement Research Council program. They completed a survey and attended a live virtual session to provide feedback on copay cards. RESULTS: Among 33 participants (median age, 49 years [range, 24-78]), the most frequent conditions were cardiovascular-metabolic disease and inflammatory bowel disease. Patients associated copay cards with lessening financial burden, improving general and mental health, and enabling medication adherence. An impact on medication adherence was identified by 10 (63%) White and nine (100%) Black respondents. Some patients were unaware of CAPs despite having encountered them; they recommended greater copay card education and transparency about CAPs. CONCLUSION: Patients relied on copay cards to help afford their prescribed medication OOP expenses and maintain medication adherence. Use of CAPs may increase patient OOP expenses. Patients would benefit from awareness programs and industry - healthcare provider partnerships that facilitate and ensure access to copay cards.

Listening efficiency in adult cochlear-implant users compared with normally-hearing controls at ecologically relevant signal-to-noise ratios.

Introduction: Due to having to work with an impoverished auditory signal, cochlear-implant (CI) users may experience reduced speech intelligibility and/or increased listening effort in real-world listening situations, compared to their normally-hearing (NH) peers. These two challenges to perception may be usefully integrated in a measure of listening efficiency: conceptually, the amount of accuracy achieved for a certain amount of effort expended. Methods: We describe a novel approach to quantifying listening efficiency based on the rate of evidence accumulation toward a correct response in a linear ballistic accumulator (LBA) model of choice decision-making. Estimation of this objective measure within a hierarchical Bayesian framework confers further benefits, including full quantification of uncertainty in parameter estimates. We applied this approach to examine the speech-in-noise performance of a group of 24 CI users (M age: 60.3, range: 20-84 years) and a group of 25 approximately age-matched NH controls (M age: 55.8, range: 20-79 years). In a laboratory experiment, participants listened to reverberant target sentences in cafeteria noise at ecologically relevant signal-to-noise ratios (SNRs) of +20, +10, and +4 dB SNR. Individual differences in cognition and self-reported listening experiences were also characterised by means of cognitive tests and hearing questionnaires. Results: At the group level, the CI group showed much lower listening efficiency than the NH group, even in favourable acoustic conditions. At the individual level, within the CI group (but not the NH group), higher listening efficiency was associated with better cognition (i.e., working-memory and linguistic-closure) and with more positive self-reported listening experiences, both in the laboratory and in daily life. Discussion: We argue that listening efficiency, measured using the approach described here, is: (i) conceptually well-motivated, in that it is theoretically impervious to differences in how individuals approach the speed-accuracy trade-off that is inherent to all perceptual decision making; and (ii) of practical utility, in that it is sensitive to differences in task demand, and to differences between groups, even when speech intelligibility remains at or near ceiling level. Further research is needed to explore the sensitivity and practical utility of this metric across diverse listening situations.

An automated algorithm for the determination of oil absorption strategy of magnetic nanoparticles from SEM images.

In recent years, the magnetic iron oxide nanoparticles (MNPs) are employed as efficient absorbents for oil removal from water. In this research, the particle size (diameter) obtained from Scanning Electron Microscopy (SEM) images of MNPs, before and after oil-absorption, are utilized to determine the oil-absorption capacity. However, the manual evaluation of the particle size and particle size distribution (PSD) are highly time-consuming and needs expertised people for accurate analysis. Hence, an image processing algorithm is employed for the determination of particle size and PSD from the Scanning Electron Microscopy (SEM) images. The key objective revolves with the preparation of the Maleic Anhydride Grafted Polypropylene anchored Magnetic Nanoparticles (MAPP-a-MNPs) to absorb crude oil from the marine water. The shape, size, and size distribution of MAPP-a-MNPs were assessed by both manual and automated analysis. For this purpose, expertise people help with the manual analysis and Threshold Adaptive-Canny Edge Detection (TA-CED) and Accumulator Updated-Circular Hough Transform (AU-CHT) method is employed for automated analysis. All the automated process were conducted in MATLAB and the measurements were taken for both before and after the oil absorption images. These measurements aid us to determine the quantity of oil absorbed by MAPP-a-MNPs. The results demonstrates excellent oil removal capacity of MAPP-a-MNPs.

Exogenous Auxin and Gibberellin on Fluoride Phytoremediation by Eichhornia crassipes.

High rates of fluorosis were reported worldwide as a result of human consumption of water with fluoride contents. Adjusting fluoride concentration in water as recommended by the World Health Organization (<1.5 mg L-1) is a concern and it needs to be conducted through inexpensive, but efficient techniques, such as phytoremediation. The application of phytohormones was investigated as a strategy to improve this process. Thus, the main goal of this research was to evaluate the effect of exogenous auxin and gibberellin on the tropical duckweed Eichhornia crassipes performance for fluoride phytoremediation. Definitive screening and central composite rotatable designs were used for experiments where fluoride concentration (5~15 mg L-1), phosphorus concentration (1~10 mg L-1), and pH (5~9) were assessed as well throughout 10 days. Fluoride contents were determined in solution and plant tissues by potentiometry. Higher concentrations of fluoride reflected on greater absorptions by plants, though in relative terms removal efficiencies were quite similar for all treatments (~60%). Auxin and acidic conditions favored fluoride removals per mass of plant. Fluoride accumulated mostly in leaves and auxin probably alleviated toxic effects on E. crassipes while gibberellin showed no effect. Therefore, E. crassipes could be employed as a fluoride accumulator plant for water treatment and exogenous auxin may be used to improve the process.

Context-specific early recruitment of small motor units in the shoulder muscle reflects a reach movement plan.

Understanding how motor plans are transformed into appropriate patterns of muscle activity is a central question in motor control. Although muscle activity during the delay period has not been reported using conventional electromyographic (EMG) approaches, we isolated motor unit activity using a high-density surface EMG signal from the anterior deltoid muscle to test whether heterogeneity in motor units could reveal early preparatory activity. Consistent with our previous work (Rungta SP, Basu D, Sendhilnathan N, Murthy A. J Neurophysiol 126: 451-463, 2021), we observed early selective recruitment of small amplitude size motor units during the delay period for hand movements similar to the observed early recruitment of small-amplitude motor units in neck muscles of nonhuman primates performing delayed saccade tasks. This early activity was spatially specific and increased with time and resembled an accumulation to threshold model that correlated with movement onset time. Such early recruitment of ramping motor units was observed at the single trial level as well. In contrast, no such recruitment of large amplitude size motor units, called nonrampers, was observed during the delay period. Instead, nonrampers became spatially specific and predicted movement onset time after the delay period. Interestingly, spatially specific delay period activity was only observed for hand movements but was absent for isometric force-driven cursor movements. Nonetheless, muscle activity was correlated with the time it took to initiate movements in both task conditions for nonrampers. Overall, our results reveal a novel heterogeneity in the EMG activity that allows the expression of early motor preparation via small amplitude size motor units that are differentially activated during movement initiation.NEW & NOTEWORTHY We studied the spatial and temporal aspects of response preparation in the anterior deltoid muscle using high-density surface EMG. Our results show that early spatially specific ramping activity that predicted reaction times could be accessed from muscle activity but was absent during isometric force-driven cursor movements. Such ramping activity could be quantified using an accumulator framework across trials, as well as within single trials, but was not observed in isometric reach tasks involving cursor movements.

The Response of Purslane (Portulaca oleracea) to Soil-Added Pb: Is It Suitable as a Potential Phytoremediation Species?

Soils with high lead (Pb) levels can be decontaminated with the use of tolerant plants. Their effectiveness may be increased with added soil N due to boosted plant vigor, but such an agronomic practice has not been widely reported so far. In this work, purslane (Portulaca oleracea) was tested in a pot experiment as a potential phytoremediation species using soil spiked with Pb at doses of 0, 150, 300, 600, and 900 mg kg-1 (referred to as Pb(0), Pb(150), Pb(300), Pb(600), and Pb(900), respectively) with added N (referred to as N(1); at 300 kg N ha-1) and without added N (N(0)). We found that added Pb did not cause any adverse effects on plant growth (height, and aerial and root dry biomass) and physiological parameters, which were boosted with added N. Lead plant concentration and uptake significantly increased with added N, a finding that confirms our hypothesis. The number of necessary harvests of purslane in order to reduce soil Pb to half its initial concentration was also calculated and found to decrease with added N, being 131 at Pb(900)N(1). Although results indicate the potential of purslane as a phytoremediation species, further research is needed under real field conditions.

"Reliable organisms from unreliable components" revisited: the linear drift, linear infinitesimal variance model of decision making.

Diffusion models of decision making, in which successive samples of noisy evidence are accumulated to decision criteria, provide a theoretical solution to von Neumann's (1956) problem of how to increase the reliability of neural computation in the presence of noise. I introduce and evaluate a new neurally-inspired dual diffusion model, the linear drift, linear infinitesimal variance (LDLIV) model, which embodies three features often thought to characterize neural mechanisms of decision making. The accumulating evidence is intrinsically positively-valued, saturates at high intensities, and is accumulated for each alternative separately. I present explicit integral-equation predictions for the response time distribution and choice probabilities for the LDLIV model and compare its performance on two benchmark sets of data to three other models: the standard diffusion model and two dual diffusion model composed of racing Wiener processes, one between absorbing and reflecting boundaries and one with absorbing boundaries only. The LDLIV model and the standard diffusion model performed similarly to one another, although the standard diffusion model is more parsimonious, and both performed appreciably better than the other two dual diffusion models. I argue that accumulation of noisy evidence by a diffusion process and drift rate variability are both expressions of how the cognitive system solves von Neumann's problem, by aggregating noisy representations over time and over elements of a neural population. I also argue that models that do not solve von Neumann's problem do not address the main theoretical question that historically motivated research in this area.

Phytotoxicity level and accumulation ability of pot marigold (Calendula officinalis L.) to zinc.

The study aims to reveal the effects of different Zn levels on pot marigold. We determined some germination and young seedling properties in the first experiment, and morphological, stomatal, and physiological parameters besides uptake profiles of both Zn and other plant nutrients in the second one. We supplied the water requirement of the seeds with Zn solutions (0, 250, 500, 750, and 1,000 mg l-1) in the first experiment, and We added the same zinc doses as mg kg-1 to the soil of the pots where the plants would grow in the second one. As a result, the inhibitory effects were more prominent in the early seedling stage (especially at 1,000 mg l-1 Zn) than in the germination one. We determined plant growth retardation, decreases in leaf water contents, and increases in membrane damages with higher Zn (≥500 mg kg-1) in the experiment conducted by potting soil. We detected decreases in chlorophyll parameters parallel with the increases in Zn. The alterations in plant zinc contents revealed the accumulation capacity of pot marigold in potting conditions. That TF value >1 in Zn treatments up to 500 mg kg-1 points to the efficiency limit of pot marigold as a Zn-accumulator plant.

To reveal the zinc tolerance of the plant through experiments carried out in two different plant growth stages, germination-young seedling and adult plant is an innovative approach. Besides, it is the first study to evaluate detailed morphological, physiological, stomatal and nutrient contents of pot marigold under heavy metal stress conditions. Thus, this study displayed both tolerance level and accumulation potential in potting conditions of pot-marigold to zinc.