Outsourcing memory to external tools: A review of ‘intention offloading’

How do we remember delayed intentions? Three decades of research into prospective memory have provided insight into the cognitive and neural mechanisms involved in this form of memory. However, we depend on more than just our brains to remember intentions. We also use external props and tools such as calendars and diaries, strategically-placed objects, and technologies such as smartphone alerts. This is known as ‘intention offloading’. Despite the progress in our understanding of brain-based prospective memory, we know much less about the role of intention offloading in individuals’ ability to fulfil delayed intentions. Here, we review recent research into intention offloading, with a particular focus on how individuals decide between storing intentions in internal memory versus external reminders. We also review studies investigating how intention offloading changes across the lifespan and how it relates to underlying brain mechanisms. We conclude that intention offloading is highly effective, experimentally tractable, and guided by metacognitive processes. Therefore, metacognitive interventions could play an important role in promoting individuals’ adaptive use of cognitive tools.

Transcranial direct current stimulation (tDCS) enhances internal source monitoring abilities in healthy participants

Source monitoring refers to the ability to identify the origin of a memory, for example, whether you remember saying something or thinking about it, and confusions of these sources have been associated with the experience of auditory verbal hallucinations (AVHs). Both AVHs and source confusions are reported to originate from dysfunctional brain activations in the prefrontal cortex (PFC) and the superior temporal gyrus (STG); specifically, it is assumed that a hypoactive PFC and a hyperactive STG gives rise to AVHs and source confusions. We set out to test this assumption by trying to mimic this hypertemporal/hypofrontal model in healthy individuals with transcranial direct current stimulation (tDCS): the inhibitory cathode was placed over the left PFC and the excitatory anode over the left dorsolateral STG. Participants completed a reality monitoring task (distinguishing between external and internal memory sources) and an internal source monitoring task (distinguishing between two or more internal memory sources) in two separate experiments (offline vs. online tDCS). In the offline experiment (n = 34), both source monitoring tasks were completed after tDCS stimulation, and in the online experiment (n = 27) source monitoring tasks were completed while simultaneously being stimulated with tDCS. We found that internal source monitoring abilities were significantly enhanced during active online tDCS, while reality monitoring abilities were unaffected by stimulation in both experiments. We speculate, based on combining the present findings with previous studies, that there might be different brain areas involved in reality and internal source monitoring. While internal source monitoring seems to involve speech production areas, specifically Broca’s area, as suggested in the present study, reality monitoring seems to rely more on the STG and DLPFC, as shown in other studies of the field.

Value-based routing of delayed intentions into brain-based vs external memory stores

Individuals have the option of remembering delayed intentions by storing them in internal memory or offloading them to an external store such as a diary or smartphone alert. How do we route intentions to the appropriate store, and what are the consequences of this? We report three experiments (two pre-registered) investigating the role of value. In Experiment 1, participants preferentially offloaded high-value intentions to the external environment. This improved memory for both high- and low-value content. Experiment 2 replicated the low-value memory enhancement even when only high-value intentions were offloaded. This suggests that internal memory is reallocated to low-value information once it is no longer required for high-value content. Experiment 3 showed that memory is better for low- than high-value content when external memory for high-value content fails. Therefore, individuals prioritize high-value information for external memory; consequently, they can be left with nothing but low-value information if it fails.

An Approach of Binary Neural Network Energy-Efficient Implementation

Binarized neural networks (BNNs), which have 1-bit weights and activations, are well suited for FPGA accelerators as their dominant computations are bitwise arithmetic, and the reduction in memory requirements means that all the network parameters can be stored in internal memory. However, the energy efficiency of these accelerators is still restricted by the abundant redundancies in BNNs. This hinders their deployment for applications in smart sensors and tiny devices because these scenarios have tight constraints with respect to energy consumption. To overcome this problem, we propose an approach to implement BNN inference while offering excellent energy efficiency for the accelerators by means of pruning the massive redundant operations while maintaining the original accuracy of the networks. Firstly, inspired by the observation that the convolution processes of two related kernels contain many repeated computations, we first build one formula to clarify the reusing relationships between their convolutional outputs and remove the unnecessary operations. Furthermore, by generalizing this reusing relationship to one tile of kernels in one neuron, we adopt an inclusion pruning strategy to further skip the superfluous evaluations of the neurons whose real output values can be determined early. Finally, we evaluate our system on the Zynq 7000 XC7Z100 FPGA platform. Our design can prune 51 percent of the operations without any accuracy loss. Meanwhile, the energy efficiency of our system is as high as 6.55 × 105 Img/kJ, which is 118× better than the best accelerator based on an NVDIA Tesla-V100 GPU and 3.6× higher than the state-of-the-art FPGA implementations for BNNs.

New Possibilities for Testing the Service Life of Magnetic Contacts

Magnetic contacts we could define as a switching device used in transport structures such as a tunnel, to which the manufacturer prescribes a certain number of closures within its lifetime, during which they should operate flawlessly. Verification of the data provided by the manufacturer is time-consuming and physically demanding due to the data being large in number. For this reason, we developed a test device using torque in the research of magnetic contacts, which greatly automates the whole process and thus eliminates human error. The test device can use internal memory to calculate the number of closures of magnetic contacts and then transmit the digitized data. The test device is registered as an industrial utility model and can be used to test any magnetic contacts.

Aplicación de tecnologías inalámbricas al monitoreo climatológico en la cuenca del Río Paute

The program for the management of the water and soil (PROMAS) is a research department of the University of Cuenca. It focuses on the monitoring and conservation of the water sources and natural resources. Among others, such program mainly requires the monitoring of several variables by means of a set of hydro-meteorological stations. From its beginning, the program has deployed around 130 stations in an extensive geographic area of interest, ranging from the Cajas sector in the province of Azuay to the province of Cañar. Currently, the meteorological stations stores the variables of interest in their internal memory. Then, the analysis of the collected data requires the physical displacement of the Promas staff to the different sites. Due to the fact that most of the remote sites are of difficult access, the personal obtain the information with a periodicity of around 30 and 45 days. In this context, this paper describes the work on progress whose main objective is to provide to the meteorological stations with the wireless transmission capacity of the data collected by the sensors to the Promas data center in real time.