An exploratory study of time use of allied health clinicians in research roles and their potential to contribute to student placements.

Although information systems (IS) problem solving involves knowledge of both the IS and application domains, little attention has been paid to the role of application domain knowledge. In this study, which is set in the context of conceptual modeling, we examine the effects of both IS and application domain knowledge on different types of schema understanding tasks: syntactic and semantic comprehension tasks and schema-based problem-solving tasks. Our thesis was that while IS domain knowledge is important in solving all such tasks, the role of application domain knowledge is contingent upon the type of understanding task under investigation. We use the theory of cognitive fit to establish theoretical differences in the role of application domain knowledge among the different types of schema understanding tasks. We hypothesize that application domain knowledge does not influence the solution of syntactic and semantic comprehension tasks for which cognitive fit exists, but does influence the solution of schema-based problem-solving tasks for which cognitive fit does not exist. To assess performance on different types of conceptual schema understanding tasks, we conducted a laboratory experiment in which participants with high- and low-IS domain knowledge responded to two equivalent conceptual schemas that represented high and low levels of application knowledge (familiar and unfamiliar application domains). As expected, we found that IS domain knowledge is important in the solution of all types of conceptual schema understanding tasks in both familiar and unfamiliar applications domains, and that the effect of application domain knowledge is contingent on task type. Our findings for the EER model were similar to those for the ER model. Given the differential effects of application domain knowledge on different types of tasks, this study highlights the importance of considering more than one application domain in designing future studies on conceptual modeling.

In 2012, stroke affected over 420 000 people within Australia and the number of stroke survivors is anticipated to increase. Many stroke survivors live with a disability which affects their ability to carry out activities of daily living. In Australia stroke survivors are cared for by both publicly and privately funded hospitals. Available to both systems are the Stroke Foundation Clinical Guidelines for the Management of Stroke Survivors (SF 2010a). The Stroke Foundation carries out biannual national audits in Australia, one focusing on acute services and the other on rehabilitation services, to determine hospitals’ quality of care in accordance with the recommendations of the Stroke Foundation clinical guidelines. Participation of privately funded hospitals in both these national audits has been consistently low. Therefore, there is less understanding of how privately funded hospitals manage stroke survivors compared with publicly funded hospitals. Stroke survivors admitted to hospitals often journey through both acute and rehabilitation services. A stroke audit that follows the journey of stroke survivors by auditing both acute and rehabilitation services concurrently is required to gain a better understanding of how the clinical guidelines are applied across each service individually and across the combined services. When developing a stroke audit tool for either local or national use, clinicians tend to have limited input into the selection of audit criteria and tool development. As the overarching framework underpinning this research program, knowledge translation involves clinicians and researchers partnering to develop ready to use research. Therefore, in this research program allied health clinicians (physiotherapists, occupational therapists, social workers, speech pathologists, and dietitians) were invited to assist in the development of a stroke audit tool in a single privately funded hospital and to test its reliability. Establishing the reliability of the stroke audit tool adds to its robustness. The stroke audit tool was used to assess the adherence of allied health clinicians from a privately funded hospital to the selective Stroke Foundation clinical guidelines. The audit included stroke survivors with consecutive admissions to both acute and rehabilitation services. This research comprised two studies. Study 1 developed a stroke audit tool with allied health clinicians selecting the most relevant clinical guidelines from the Stroke Foundation Clinical Guidelines for Stroke Management (SF 2010a). Both inter and intra-rater reliability of the tool were tested with allied health clinicians who were able to agree on eight Stroke Foundation clinical guidelines with 70% agreement. An additional two Stroke Foundation clinical guidelines with 50% agreement and a further two Stroke Foundation clinical guidelines were added to ensure the stroke audit tool was relevant to all disciplines. Interrater reliability for the stroke audit tool was tested across ten clinical records by five (one from each discipline) allied health clinicians or raters. Inter-rater reliability was high with substantial consistency demonstrated across both services. Intra-rater reliability demonstrated substantial to moderate consistency. Study 2 comprised a 12-month retrospective clinical record audit of stroke survivors with consecutive admission to both acute and rehabilitation services. The stroke audit tool assessed the percentage adherence of allied health clinicians against the selected Stroke Foundation’s clinical guidelines included in the audit tool. Adherence was defined as stroke survivor care delivered in accordance with the Stroke Foundation clinical guidelines 2010. A minimal adherence level was set at 60% with an aspirational level set at 80%. Secondary analysis compared adherence across acute and rehabilitation services. Also, a subgroup analysis was undertaken investigating the influence of age, gender and length of stay to allied health clinician’s adherence of Stroke Foundation clinical guidelines. Allied health clinicians met the 60% adherence rate, for nine (out of 12) Stroke Foundation clinical guidelines, including three that were above 80% adherence rate (aspirational level). There was a significant difference across acute and rehabilitation services for eight of the 12 Stroke Foundation clinical guidelines. Age, gender and length of stay did not influence adherence rates of the allied health clinicians across acute and rehabilitation stroke services. Allied health clinicians from a privately funded hospital developed a reliable stroke audit tool. The stroke audit tool highlighted areas of adherence as well as service gaps across acute, rehabilitation and the combined service. Investigating adherence across both acute and rehabilitation services could lead to a co-ordinated approach to improving adherence towards Stroke Foundation clinical guidelines.

With the increasing usage of big data, the types of big data technologies have also become diverse. When solving a particular problem, it often involves many different types of big data tasks. How to realize the hybrid scheduling of different types of tasks is an urgent problem to be solved. Before this, the industry used crontab to schedule big data tasks regularly, it can conveniently execute system task scheduling and user task scheduling in Linux environment, but it cannot meet the scheduling needs of complex business scenarios and it requires users to write their own submission logic. Therefore, this paper designs a hybrid scheduling system for big data tasks based on Airflow. The system supports the construction of different types of big data tasks into a workflow, and scheduling these tasks based on workflow. At the same time, the scheduling module is independent of other modules, which reduces the coupling degree between the modules. The method proposed in this paper has been applied to big data platform, and the effectiveness of the method has been verified.

We investigated how the opportunity to learn (OTL) with different types of mathematics tasks are related to mathematical literacy and the role of perceived control in the relationship between OTL and mathematical literacy. The structural equation modeling was applied to the data of 1,649 Korean students from the PISA 2012 database. OTL with the four different types of tasks – algebraic word problems, procedural tasks, pure mathematics reasoning, and applied mathematics reasoning – were measured via student survey on how often they have encountered each type of task in their mathematics lessons and tests. The results showed that OTL with the procedural tasks was likely to increase mathematical literacy directly and indirectly through internal perceived control. Engaging in the applied reasoning tasks is positively related to external perceived control, but negatively to mathematical literacy.

This paper focuses on a group of mothers of immigrant origin as they engage in explorations and conversations about mathematics. We examine how bringing in tasks that draw on their children’s school mathematics, and on the participants’ knowledge and experiences, can inform us about their understanding of mathematics and provide opportunities to further enhance this understanding. Our analysis centers on two different types of mathematical tasks, both with rich potential for adult numeracy education. The first type of task engages the mothers in school mathematics through an exploration of addition and subtraction. In the second type of task, we look at mothers as adult learners working on two mathematical problem solving tasks, with different levels of connections to their everyday experiences. The findings point to a relationship between the type of context (everyday based versus school based) and the nature of the adult learners’ mathematical sense making and engagement with the tasks. While there was some initial apprehension towards the school based tasks, the direct connection to what their children are learning was a strong motivator. The everyday based tasks bring up the potential tension between everyday mathematics and academic mathematics. The more the task connected to their everyday knowledge, the more the mothers drew on their experience to offer approaches that made sense from a practical point of view. We argue that these different types of tasks can complement each other in working with adult learners to enhance their numeracy skills.

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

The goal of this study is to investigate the impact of manipulating the cognitive complexity of three different types of oral tasks on interaction. The study first considers the concepts of task complexity and interaction and then examines the specific studies that have looked at the effects of increasing task complexity on conversational interaction. In the experiment, learners of English as a foreign language organized into 27 dyads carry out three different types of tasks: a narrative reconstruction task, an instruction-giving map task, and a decision-making task. Two different versions of each task, one simple and one complex, are presented to learners in different sequences. Task complexity is manipulated along the degree of displaced, past time reference, the number of elements, and the reasoning demands. Audio recordings are transcribed and coded for interactional feedback, which is measured in terms of negotiation of meaning (i.e., confirmation checks, clarification requests, and comprehension checks), recasts, language-related episodes (LREs), and repairs, all of which have been described in the literature as being conducive to acquisition. Both parametric and non-parametric statistical tests are used. Results are discussed in the light of previous studies that have looked at the specific relationship between task complexity and interaction, attention models (Robinson, Applied Linguistics 22: 27–57, 2001a, Second Language Studies 21: 45–105, 2003, International Review of Applied Linguistics 55: 1–32, 2005, International Review of Applied Linguistics 45: 193–213, 2007b; Skehan and Foster, Cognition and tasks, Cambridge University Press, 2001), and how different task types may variously affect the way interaction proceeds during task performance.

PURPOSE: This study is to examine the effects of different types of tasks on gait functions of chronic stroke patients when different types of dual tasks were applied while the patients were implementing practical and continuous circuit tasks using their upper and lower extremities circulating many workbenches. METHODS: Forty-four chronic stroke patients were divided into a dual motor circuit task training group, a dual cognitive circuit task training group and a simple task training group. Before training, all the patients were identically encouraged to receive conservative physical therapy for 30 minutes by a physical therapist were thereafter made to train for 30 minutes, five times a week for a total of eight weeks with individual additional tasks. The dual motor circuit task training consisted of continuous circuit training motor tasks and additional motor tasks and the dual cognitive circuit task training consisted of tasks combining the same circuit training motor tasks and additional cognitive tasks. The simple task training consisted of natural walks on a flat terrain to the front, rear and lateral sides of the terrain. Changes in functional gait abilities made through the training were evaluated using GAITRite. SPSS Win 12.0 was used for the data analysis. RESULTS: As for the gait variables that showed significant differences in comparison between the groups over the training period, the dual motor circuit task training group showed more significant differences than the dual cognitive circuit task training group and the simple task training group at 4 weeks and 8 weeks of training(p<.05). CONCLUSION: Therefore, it could be seen that the practical and continuous dual circuit task training was more effective than simple task training on gait. In comparison between the types of dual tasks, the dual motor circuit task training group showed more effects than the dual cognitive circuit task training group.

The rise of mobile usage, as well as increasing amount of information, contributes to the individual’s inattention to ads and multitasking behavior. In many situations, multitasking has taken attention away from advertising messages, leading to a decrease in message effectiveness. Nonetheless, previous research has shown a potential benefit of multitasking on memory because, in some situations, multitasking could reduce mind wandering, which is a hidden distraction in ad processing. This research compares how different types of secondary tasks (cognitive vs. perceptual) affect ad memory. The results showed that a low perceptual load secondary task increased memory about ad content while decreasing mind wandering. However, cognitive load secondary tasks did not interfere with the cognitive processing more than the control condition.

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract In addition to long-timescale rewiring, synapses in the brain are subject to significant modulation that occurs at faster timescales that endow the brain with additional means of processing information. Despite this, models of the brain like recurrent neural networks (RNNs) often have their weights frozen after training, relying on an internal state stored in neuronal activity to hold task-relevant information. In this work, we study the computational potential and resulting dynamics of a network that relies solely on synapse modulation during inference to process task-relevant information, the multi-plasticity network (MPN). Since the MPN has no recurrent connections, this allows us to study the computational capabilities and dynamical behavior contributed by synapses modulations alone. The generality of the MPN allows for our results to apply to synaptic modulation mechanisms ranging from short-term synaptic plasticity (STSP) to slower modulations such as spike-time dependent plasticity (STDP). We thoroughly examine the neural population dynamics of the MPN trained on integration-based tasks and compare it to known RNN dynamics, finding the two to have fundamentally different attractor structure. We find said differences in dynamics allow the MPN to outperform its RNN counterparts on several neuroscience-relevant tests. Training the MPN across a battery of neuroscience tasks, we find its computational capabilities in such settings is comparable to networks that compute with recurrent connections. Altogether, we believe this work demonstrates the computational possibilities of computing with synaptic modulations and highlights important motifs of these computations so that they can be identified in brain-like systems. Editor's evaluation The study shows that fast and transient modifications of the synaptic efficacies, alone, can support the storage and processing of information over time. Convincing evidence is provided by showing that feed-forward networks, when equipped with such short-term synaptic modulations, perform a wide variety of tasks at a performance level comparable with that of recurrent networks. The results of the study are valuable to both neuroscientists and researchers in machine learning. https://doi.org/10.7554/eLife.83035.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The brain’s synapses constantly change in response to information under several distinct biological mechanisms (Love, 2003; Hebb, 2005; Bailey and Kandel, 1993; Markram et al., 1997; Bi and Poo, 1998; Stevens and Wang, 1995; Markram and Tsodyks, 1996). These changes can serve significantly different purposes and occur at drastically different timescales. Such mechanisms include synaptic rewiring, which modifies the topology of connections between neurons in our brain and can be as fast as minutes to hours. Rewiring is assumed to be the basis of long-term memory that can last a lifetime (Bailey and Kandel, 1993). At faster timescales, individual synapses can have their strength modified (Markram et al., 1997; Bi and Poo, 1998; Stevens and Wang, 1995; Markram and Tsodyks, 1996). These changes can occur over a spectrum of timescales and can be intrinsically transient (Stevens and Wang, 1995; Markram and Tsodyks, 1996). Though such mechanisms may not immediately lead to structural changes, they are thought to be vital to the brain’s function. For example, short-term synaptic plasticity (STSP) can affect synaptic strength on timescales less than a second, with such effects mainly presynaptic-dependent (Stevens and Wang, 1995; Tsodyks and Markram, 1997). At slower timescales, long-term potentiation (LTP) can have effects over minutes to hours or longer, with the early phase being dependent on local signals and the late phase including a more complex dependence on protein synthesis (Baltaci et al., 2019). Also on the slower end, spike-time-dependent plasticity (STDP) adjusts the strengths of connections based on the relative timing of pre- and postsynaptic spikes (Markram et al., 1997; Bi and Poo, 1998; McFarlan et al., 2023). In this work, we investigate a new type of artificial neural network (ANN) that uses biologically motivated synaptic modulations to process short-term sequential information. The multi-plasticity network (MPN) learns using two complementary plasticity mechanisms: (1) long-term synaptic rewiring via standard supervised ANN training and (2) simple synaptic modulations that operate at faster timescales. Unlike many other neural network models with synaptic dynamics (Tsodyks et al., 1998; Mongillo et al., 2008; Lundqvist et al., 2011; Barak and Tsodyks, 2014; Orhan and Ma, 2019; Ballintyn et al., 2019; Masse et al., 2019), the MPN has no recurrent synaptic connections, and thus can only rely on modulations of synaptic strengths to pass short-term information across time. Although both recurrent connections and synaptic modulation are present in the brain, it can be difficult to isolate how each of these affects temporal computation. The MPN thus allows for an in-depth study of the computational power of synaptic modulation alone and how the dynamics behind said computations may differ from networks that rely on recurrence. Having established how modulations alone compute, we believe it will be easier to disentangle synaptic computations from brain-like networks that may compute using a combination of recurrent connections, synaptic dynamics, neuronal dynamics, etc. Biologically, the modulations in the MPN represent a general synapse-specific change of strength on shorter timescales than the structural changes, the latter of which are represented by weight adjustment via backpropagation. We separately consider two forms of modulation mechanisms, one of which is dependent on both the pre- and postsynaptic firing rates and a second that only depends on presynaptic rates. The first of these rules is primarily envisioned as coming from associative forms of plasticity that depend on both pre- and postsynaptic neuron activity (Markram et al., 1997; Bi and Poo, 1998; McFarlan et al., 2023). Meanwhile, the second type of modulation models presynaptic-dependent STSP (Mongillo et al., 2008; Zucker and Regehr, 2002). While both these mechanisms can arise from distinct biological mechanisms and can span timescales of many orders of magnitude, the MPN uses simplified dynamics to keep the effects of synaptic modulations and our subsequent results as general as possible. It is important to note that in the MPN, as in the brain, the mechanisms that represent synaptic modulations and rewiring are not independent of one another – changes in one affect the operation of the other and vice versa. To understand the role of synaptic modulations in computing and how they can change neuronal dynamics, throughout this work we contrast the MPN with recurrent neural networks (RNNs), whose synapses/weights remain fixed after a training period. RNNs store temporal, task-relevant information in transient internal neural activity using recurrent connections and have found widespread success in modeling parts of our brain (Cannon et al., 1983; Ben-Yishai et al., 1995; Seung, 1996; Zhang, 1996; Ermentrout, 1998; Stringer et al., 2002; Xie et al., 2002; Fuhs and Touretzky, 2006; Burak and Fiete, 2009). Although RNNs model the brain’s significant recurrent connections, the weights in these networks neglect the role transient synaptic dynamics can have in adjusting synaptic strengths and processing information. Considerable progress has been made in analyzing brain-like RNNs as population-level dynamical systems, a framework known as neural population dynamics (Vyas et al., 2020). Such studies have revealed a striking universality of the underlying computational scaffold across different types of RNNs and tasks (Maheswaranathan et al., 2019b). To elucidate how computation through synaptic modulations affect neural population behavior, we thoroughly characterize the MPN’s low-dimensional behavior in the neural population dynamics framework (Vyas et al., 2020). Using a novel approach of analyzing the synapse population behavior, we find the MPN computes using completely different dynamics than its RNN counterparts. We then explore the potential benefits behind its distinct dynamics on several neuroscience-relevant tasks. Contributions The primary contributions and findings of this work are as follows: We elucidate the neural population dynamics of the MPN trained on integration-based tasks and show it operates with qualitatively different dynamics and attractor structure than RNNs. We support this with analytical approximations of said dynamics. We show how the MPN’s synaptic modulations allow it to store and update information in its state space using a task-independent, single point-like attractor, with dynamics slower than task-relevant timescales. Despite its simple attractor structure, for integration-based tasks, we show the MPN performs at level comparable or exceeding RNNs on several neuroscience-relevant measures. The MPN is shown to have dynamics that make it a more effective reservoir, less susceptible to catastrophic forgetting, and more flexible to taking in new information than RNN counterparts. We show the MPN is capable of learning more complex tasks, including contextual integration, continuous integration, and 19 neuroscience tasks in the NeuroGym package (Molano-Mazon et al., 2022). For a subset of tasks, we elucidate the changes in dynamics that allow the network to solve them. Related work Networks with synaptic dynamics have been investigated previously (Tsodyks et al., 1998; Mongillo et al., 2008; Sugase-Miyamoto et al., 2008; Lundqvist et al., 2011; Barak and Tsodyks, 2014; Orhan and Ma, 2019; Ballintyn et al., 2019; Masse et al., 2019; Hu et al., 2021; Tyulmankov et al., 2022; Tyulmankov et al., 2022; Rodriguez et al., 2022). As we mention above, many of these works investigate networks with both synaptic dynamics and recurrence (Tsodyks et al., 1998; Mongillo et al., 2008; Lundqvist et al., 2011; Barak and Tsodyks, 2014; Orhan and Ma, 2019; Ballintyn et al., 2019; Masse et al., 2019), whereas here we are interested in investigating the computational capabilities and dynamical behavior of computing with synapse modulations alone. Unlike previous works that examine computation solely through synaptic changes, the MPN’s modulations occur at all times and do not require a special signal to activate their change (Sugase-Miyamoto et al., 2008). The networks examined in this work are most similar to the recently introduced ‘HebbFF’ (Tyulmankov et al., 2022) and ‘STPN’ (Rodriguez et al., 2022) that also examine computation through continuously updated synaptic modulations. Our work differs from these studies in that we focus on elucidating the neural population dynamics of such networks, contrasting them to known RNN dynamics, and show why this difference in dynamics may be beneficial in certain neuroscience-relevant settings. Additionally, the MPN uses a multiplicative modulation mechanism rather than the additive modulation of these two works, which in some settings we investigate yields significant performance differences. The exact form of the synaptic modulation updates were originally inspired by ‘fast weights’ used in machine learning for flexible learning (Ba et al., 2016). However, in the MPN, both plasticity rules apply to the same weights rather than different ones, making it more biologically realistic. This work largely focuses on understanding computation through a neural population dynamics-like analysis (Vyas et al., 2020). In particular, we focus on the dynamics of networks trained on integration-based tasks, that have previously been studied in RNNs (Maheswaranathan et al., 2019b; Maheswaranathan et al., 2019a; Maheswaranathan and Sussillo, 2020; Aitken et al., 2020). These studies have demonstrated a degree of universality of the underlying computational structure across different types of tasks and RNNs (Maheswaranathan et al., 2019b). Due to the MPN’s dynamic weights, its operation is fundamentally different than said recurrent networks. Setup Throughout this work, we primarily investigate the dynamics of the MPN on tasks that require an integration of information over time. To correctly respond to said task, the network is required to both store and update its internal state as well as compare several distinct items in its memory. All tasks in this work consist of a discrete sequence of vector inputs, xt for t=1,2,…,T. For the tasks we consider presently, at time T the network is queried by a ‘go signal’ for an output, for which the correct response can depend on information from the entire input sequence. Throughout this paper, we denote vectors using lowercase bold letters, matrices by uppercase bold letters, and scalars using standard (not-bold) letters. The input, hidden, and output layers of the networks we study have d, n, and N neurons, respectively. Multi-plasticity network The multi-plasticity network (MPN) is an artificial neural network consisting of input, hidden, and output layers of neurons. It is identical to a fully-connected, two-layer, feedforward network (Figure 1, middle), with one major exception: the weights connecting the input and hidden layer are modified by the time-dependent synapse modulation (SM) matrix, M (Figure 1, left). The expression for the hidden layer activity at time step t is (1) ht=tanh⁡((Mt−1⊙Winp)xt+Winpxt) where Winp is an n-by-d weight matrix representing the network’s synaptic strengths that is fixed after training, ‘⊙’ denotes element-wise multiplication of the two matrices (the Hadamard product), and the tanh⁡(⋅) is applied element-wise. For each synaptic weight in Winp, a corresponding element of Mt−1 multiplicatively modulates its strength. Note if Mt−1=0 the first term vanishes, so the Winp are unmodified and the network simply functions as a fully connected feedforward network. Figure 1 Download asset Open asset Two neural network computational mechanisms: synaptic modulations and recurrence. Throughout this figure, neurons are represented as white circles, the black lines between neurons represent regular feedforward weights that are modified during training through gradient descent/backpropagation. From bottom to top are the input, hidden, and output layers, respectively. (Middle) A two-layer, fully connected, feedforward neural network. (Left) Schematic of the MPN. Here, the pink and black lines (between the input and hidden layer) represent weights that are modified by both backpropagation (during training) and the synapse modulation matrix (during an input sequence), see Equation 1. (Right) Schematic of the Vanilla RNN. In addition to regular feedforward weights between layers, the RNN has (fully connected) weights between its hidden layer from one time step to the next, see Equation 3. What allows the MPN to store and manipulate information as the input sequence is passed to the network is how the SM matrix, Mt, changes over time. Throughout this work, we consider two distinct modulation update rules. The primary rule we investigate is dependent upon both the pre- and postsynaptic firing rates. An alternative update rule only depends upon the presynaptic firing rate. Respectively, the SM matrix updated for these two cases takes the form (Hebb, 2005; Ba et al., 2016; Tyulmankov et al., 2022), (2a) pre.&post.:Mt=λMt−1+ηhtxtT (2b) pre. only:Mt=λMt−1+η1xtT/n, where λ and η are parameters learned during training and 1 is the n-dimensional vector of all 1s. We allow for −∞<η<∞, so the size and sign of the modulations can be optimized during training. Additionally, 0<λ<1, so the SM matrix exponentially decays at each time step, asymptotically returning to its M=0 baseline. For both rules, we define M0=0 at the start of each input sequence. Since the SM matrix is updated and passed forward at each time step, we will often refer to Mt as the state of said networks. To distinguish networks with these two modulation rules, we will refer to networks with the presynaptic only rule as MPNpre, while we reserve MPN for networks with the pre- and postsynatpic update that we primarily investigate. For brevity, and since almost all results for the MPN generalize to the simplified update rule of the MPNpre, the main text will foremost focus on results for the MPN. Results for the MPNpre are discussed only briefly or given in the supplement. As mentioned in the introduction, from a biological perspective the MPN’s modulations represent a general associative plasticity such as STDP, whereas the presynaptic-dependent modulations of the MPNpre can represent STSP. The decay induced by λ represents the return to baseline of the aforementioned processes, which all occur at a relatively slow speed to their onset (Bertram et al., 1996; Zucker and Regehr, 2002). To ensure the eventual decay of such modulations, unless otherwise stated, throughout this work we further limit λ<λmax with λmax=0.95. Additionally, we observe no major performance or dynamics difference for positive or negative η, so we do not distinguish the two throughout this work (Methods). We emphasize that the modulation mechanisms of the MPN and MPNpre could represent biological processes that occur at significantly different timescales, so although we train them on identical tasks the tasks themselves are assumed to occur at timescales that match the modulation mechanism of the corresponding network. Note that the modulation mechanisms are not independent of weight adjustment from backpropagation. Since the SM matrix is active during training, the network’s weights that are being adjusted by backpropgation (see below) are experiencing modulations, and said modulations factor into how the weights are adjusted. Lastly, the output of the MPN and MPNpre at time T is determined by a fully-connected readout matrix, yT=WROhT, where WRO is an N-by-n weight matrix adjusted during training. Throughout this work, we will view said readout matrix as N distinct n-dimensional readout vectors, that is one for each output neuron. Recurrent neural networks As discussed in the introduction, throughout this work we will compare the learned dynamics and performance of the MPN to artificial RNNs. The hidden layer activity for the simplest recurrent neural network, the Vanilla RNN, is (3) ht=tanh⁡(Wrecht−1+Winpxt+b), with Wrec the recurrent weights, an n-by-n matrix that updates the hidden neurons from one time step to the next (Figure 1, right). We also consider a more sophisticated RNN structure, the gated recurrent unit (GRU), that has additional gates to more precisely control the recurrent update of its hidden neurons (see Methods 5.2). In both these RNNs, information is stored and updated via the hidden neuron activity, so we will often refer to ht as the RNNs’ hidden state or just its state. The output of the RNNs is determined through a trained readout matrix in the same manner as the MPN above, i.e. yT=WROhT. Training The weights of the MPN, MPNpre, and RNNs will be trained using gradient descent/backpropagation through time, specifically ADAM (Kingma and Ba, 2014). All network weights are subject to L1 regularization to encourage sparse solutions (Methods 5.2). Cross-entropy loss is used as a measure of performance during training. Gaussian noise is added to all inputs of the networks we investigate. Results Network dynamics on a simple integration task Simple integration task We begin our investigation of the MPN’s dynamics by training it on a simple N-class (Through most of this work, the number of neurons in the output layer of our networks will always be equal to the number of classes in the task, so we use N to denote both unless otherwise stated). integration task, inspired by previous works on RNN integration-dynamics (Maheswaranathan et al., 2019a; Aitken et al., 2020). In this task, the network will need to determine for which of the N classes the input sequence contains the most evidence (Figure 2a). Each stimulus input, xt, can correspond to a discrete unit of evidence for one of the N classes. We also allow inputs that are evidence for none of the classes. The final input, xT, will always be a special ‘go signal’ input that tells the network an output is expected. The network’s output should be an integration of evidence over the entire input sequence, with an output activity that is largest from the neuron that corresponds to the class with the maximal accumulated evidence. (We omit sequences with two or more classes tied for the most evidence. See Methods 5.1 for additional details). Prior to adding noise, each possible input, including the go signal, is mapped to a random binary vector (Figure 2b). We will also investigate the effect of inserting a delay period between the stimulus period and the go signal, during which no input is passed to the network, other than noise (Figure 2c). Figure 2 Download asset Open asset Schematic of simple integration task. (a) Example sequence of the two-class integration task where each represents an and throughout this work, distinct classes are represented by different In this and The represent evidence for their while the represents an input that is evidence for At the of the sequence is the ‘go signal’ that the network an output is expected. The correct response for the sequence is the class with the most in the the Each possible input is mapped to a random binary The integration task can be modified by the of a between the stimulus period and the go the delay the network no input than We find the MPN is capable of learning the integration task to across a wide of class sequence and delay It is the of this to the dynamics behind the trained MPN that allow it to solve such a task and compare them to more RNN dynamics. Here, in the main we will explore the dynamics of a two-class integration task, to classes are and are discussed in the Methods We will start by the simplest of integration a delay the effects of delay we into the dynamics of the MPN, we a of the known RNN dynamics on integration-based tasks. of RNN attractor dynamics accumulated evidence both on and artificial neural networks, have that networks with recurrent connections attractor dynamics to solve integration-based tasks (Maheswaranathan et al., 2019a; Maheswaranathan and Sussillo, 2020; Aitken et al., 2020). Here, we specifically review the behavior of artificial RNNs on the aforementioned N-class integration tasks that many with of neural networks. Note also the of the dynamics can depend on between the classes et al., in this work we only investigate the where the classes are RNNs are capable of learning to solve the simple integration task at and their dynamics are qualitatively the same across several (Maheswaranathan et al., 2019b; Aitken et al., 2020). the network’s behavior by at individual hidden neuron activity can be difficult (Figure and so it is to to a population-level analysis of the dynamics. the number of hidden neurons is than number of integration classes the population activity of the trained RNN primarily in a low-dimensional of et al., 2020). This is to recurrent dynamics that a attractor of and the hidden activity often operates to said Methods for a more in-depth review of these results including how is In the two-class the RNN will operate to a The of hidden activity allows for an of the dynamics using a (Figure From the we see the network’s hidden activity from the attractor its As evidence for one class over the other the hidden activity accumulated evidence by the attractor (Figure The two readout vectors are with the two of the so the further the final hidden activity, is one of the attractor, the that corresponding output and thus the RNN correctly the class with the most evidence. For we note that the hidden activity of the trained RNN is not dependent upon the input of the present time step (Figure it is the change in the hidden activity from one time step to the next, that are (Figure For the Vanilla RNN (GRU), we find of the hidden activity to be by the accumulated evidence and only to be by the present input to the network Methods Figure with see all Download asset Open asset of multi-plasticity network and RNN dynamics. Vanilla RNN hidden neuron dynamics, see Figure 1 for (a) layer neural activity, for neurons of the RNN as a of sequence time of sequence The represents the stimulus period during which information should be across time and the representing the response to the go neuron activity, over input into their top two by relative accumulated evidence between classes at time t (Methods Also shown are of by the class readout vector and the state as with ht by input at the present time step, xt see The shows the of as a of the present input, xt, with the lines showing the for each of the MPN hidden neuron dynamics, see Figure 1 for as as as for The shows the of each with the readout vectors (Methods MPN synaptic modulation dynamics. as of hidden neuron activity, the of the SM Mt, over input Mt are for as with a different The is the same as that shown in for MPN hidden activity inputs, not so accumulated evidence We to analyzing the hidden activity of the trained in the same manner that for the RNNs. The MPN trained on a two-class integration task to have significantly more activity in the individual of ht (Figure We find the hidden neuron activity to be with it to using a (Methods Unlike the RNN, we observe the hidden neuron activity to be into several distinct (Figure input sequences ht to between said the ht by the sequence input at the present time step, we see the different inputs are the hidden activity into distinct that we (Figure the hidden neuron activity is largely dependent upon the most input to the network, rather than the accumulated evidence as we for the RNN. However, each we also see a in ht from accumulated evidence (Figure For the MPN we find only of the hidden activity to be by accumulated evidence and to be by the present input to the network Methods MPNpre dynamics are largely the same of we see for further the hidden neuron activity primarily dependent upon the input to the network, one may how the MPN information dependent upon the entire sequence to solve the task. the other possible inputs to the network, the go signal has its distinct which the hidden by accumulated evidence. all we find the readout vectors are with the evidence the go (Figure The are

Decision letter: Neural population dynamics of computing with synaptic modulations

It is mostly observed that textbooks are a key component in most language programs especially in the process of teaching and learning a second/foreign language. As a result, a number of studies have been carried out to evaluate English language teaching (ELT) materials. Trying to contribute to this growing body of literature, the present study was conducted to evaluate the eight most popular global textbooks which are taught in Iranian ELT institutes in terms of their task types according to Nunan’s (1999) classification of the tasks. To this end, all the tasks of each textbook were categorized in their major and minor associated groups. The results revealed that linguistic tasks were the most frequent type of tasks in almost all of the textbooks. Moreover, there was a significant difference among the frequency of task types in these course books. Finally Total English, English Result and Interchange proved to be the most balanced textbooks respectively in comparison to others with regard to each task types. It was concluded that unlike the common belief, being recently published does not guarantee the appropriateness of textbooks for language teaching program. The results of this study could be helpful for teachers to realize the types of the tasks and exercises in the available ELT textbooks and consequently help their students develop their English proficiency by adapting and mixing different types of tasks from different textbooks.

Students receive different types of task‐ and process‐level feedback in educational settings, but it is unclear how they perceive and use these different types of feedback they receive. In a broad and representative survey, 378 students answered questions about their experiences with feedback in college courses, their perceptions of the feedback, and the regulatory and motivational strategies they used to learn from it. On average, students reported receiving feedback on three different types of tasks in a course and the nature of the feedback differed by task. Although some student practices in processing feedback were uniform across types of tasks and feedback, other practices varied markedly. Students reported that they highly valued all feedback, but believed its utility declined over time and preferred immediate feedback. This study provides novel insights into student beliefs and preferences regarding feedback and the regulatory and motivational practices that they use to learn from feedback.

When using information retrieval (IR) systems, users often pose short and ambiguous query terms. It is critical for IR systems to obtain more accurate representation of users’ information need, their document preferences, and the context they are working in, and then incorporate them into the design of the systems to tailor retrieval to individual users. The proposed study is to personalize IR systems by tailoring search result content to individual users through the inference of useful documents during their information seeking episode, in different types of tasks. Specifically, this dissertation has two research goals: (1) generate predictive models of document usefulness based on multiple user behaviors as in different types of tasks; (2) generate predictive models of task type through observing users’ search behaviors. To address these research goals, this study analyzed data collected in a controlled lab experiment. Thirty-two students were invited to participate in the study, each worked on four search tasks, and these tasks were designed to be different types. During search sessions, all users’ interactions were recorded by multiple loggers. Predictive models of document usefulness and task type were generated using various statistical analysis methods. Our results demonstrate that multiple behavioral measures on both content pages and search result pages can be indicators of document usefulness. More importantly, task type affected the relationship between the behavioral measures and document usefulness, and it may therefore be necessary to build task-specific predictive models of document usefulness, which can achieve better prediction accuracy than a non-task specific predictive model. In addition, behavioral measures on within-session level and whole-session levels could be able to generate predictive models of task type. The results improve our understanding on how to infer users’ search context information and document usefulness from user behaviors, and then to use this knowledge to improve the information searcher’s experience; that is, to make their information search more effective and pleasurable. The research findings have theoretical and practical implications for using behavioral measures and taking account of contextual factors in the development of personalized IR systems. Future studies are suggested for making use of these findings as well as research on related issues.

The article is devoted to the description of modern subject-integrative pedagogical technologies of working with educational texts, especially important for younger school students living in the Republic of Belarus. In general, the problem of simultaneous use of two languages in educational activities in conditions of closely related bilingualism is becoming especially relevant today and involves the search for new solutions, among which are subject-integrative technologies for the formation of communicative and speech competence of bilingual primary school students. The object of special attention is the educational text, with the help of which practical tasks, aimed at the assimilation by students of knowledge about the features of a coherent statement, the development of speech skills in bilinguals, the development of communicative and speech strategies in each of their two languages, are solved. Within the framework of subject-integrative learning, comparative activities based on texts of the following styles are effective: artistic and scientific (scientific-educational and popular science sub-styles), artistic and official-business, artistic and colloquial (colloquial-official sub-style).The article describes didactic material and communicative exercises, and most importantly, it outlines the vector for solving educational and practical problems of an analytical and comparative nature in teaching subject integration when working with educational texts in the context of closely related bilingualism. Different types of tasks are offered with Russian and Belarusian texts of different styles, containing identical and complementary information. The productivity of such work for the development of skills to perceive, interpret and creatively transform information contained in the text is noted. This makes it possible to increase the cognitive potential of bilingual students and develop the flexibility and variability of their thinking.