In this paper, we report on findings from an ethnographic study of how people use their calendars for personal information management (PIM). Our participants were faculty, staff and students who were not required to use or contribute to any specific calendaring solution, but chose to do so anyway. The study was conducted in three parts: first, an initial survey provided broad insights into how calendars were used; second, this was followed up with personal interviews of a few participants which were transcribed and content-analyzed; and third, examples of calendar artifacts were collected to inform our analysis. Findings from our study include the use of multiple reminder alarms, the reliance on paper calendars even among regular users of electronic calendars, and wide use of calendars for reporting and life-archival purposes. We conclude the paper with a discussion of what these imply for designers of interactive calendar systems and future work in PIM research.
Nearly 10 years after Kelley and Chapanis' original study, Payne @cite_19 conducted interviews with 30 knowledge workers about both calendars and to-do lists, followed by a task analysis of his observations. He concluded that the central task supported by calendars was . Prospective remembering is the use of memory for remembering to do things in the future, as different from retrospective memory functions such as recalling past events.
@cite_19
2128207755 2095047274 2067450646 2027670258 2235735853 2251769296 2009928698 2114352343 2142000191 2110485445 2011348940 2160623437 2892234010 2113096211 2009627211 2151718942 2001788447 2618767506 2222620134 2106169150
This article is an interview study of calendar use and a cognitive analysis of the interactions between the design of calendars and the task of prospective remembering. The study and analysis are coordinated to present a general critique of current electronic calendar designs and to note opportunities for future designs. The interview data reveal continued use of paper calendars in a highly computerized setting. A key conclusion is that paper calendars support prospective remembering by promoting browsing of existing appointments during subsequent calendar keeping but that this advantage is compromised in current electronic designs. Other aspects of the interviews and the analyses address the representational limitations of both paper and electronic calendars. This research explores a methodology in which design implications of qualitative empirical data are understood and systematized through theoretical analyses of existing artifacts. A rapidly growing number of recent studies show that imagining the future depends on much of the same neural machinery that is needed for remembering the past. These findings have led to the concept of the prospective brain; an idea that a crucial function of the brain is to use stored information to imagine, simulate and predict possible future events. We suggest that processes such as memory can be productively re-conceptualized in light of this idea. The very limited capacity of short-term or working memory is one of the most prominent features of human cognition. Most studies have stressed delimiting the upper bounds of this memory in memorization tasks rather than the performance of everyday tasks. We designed a series of experiments to test the use of short-term memory in the course of a natural hand-eye task where subjects have the freedom to choose their own task parameters. In this case subjects choose not to operate at the maximum capacity of short-term memory but instead seek to minimize its use. In particular, reducing the instantaneous memory required to perform the task can be done by serializing the task with eye movements. These eye movements allow subjects to postpone the gathering of task-relevant information until just before it is required. The reluctance to use short-term memory can be explained if such memory is expensive to use with respect to the cost of the serializing strategy. We report on a user study that provides evidence that spaced repetition and a specific mnemonic technique enable users to successfully recall multiple strong passwords over time. Remote research participants were asked to memorize 4 Person-Action-Object (PAO) stories where they chose a famous person from a drop-down list and were given machine-generated random action-object pairs. Users were also shown a photo of a scene and asked to imagine the PAO story taking place in the scene (e.g., Bill Gates---swallowing---bike on a beach). Subsequently, they were asked to recall the action-object pairs when prompted with the associated scene-person pairs following a spaced repetition schedule over a period of 127+ days. While we evaluated several spaced repetition schedules, the best results were obtained when users initially returned after 12 hours and then in @math increasing intervals: 77 of the participants successfully recalled all 4 stories in 10 tests over a period of 158 days. Much of the forgetting happened in the first test period (12 hours): 89 of participants who remembered their stories during the first test period successfully remembered them in every subsequent round. These findings, coupled with recent results on naturally rehearsing password schemes, suggest that 4 PAO stories could be used to create usable and strong passwords for 14 sensitive accounts following this spaced repetition schedule, possibly with a few extra upfront rehearsals. In addition, we find that there is an interference effect across multiple PAO stories: the recall rate of 100 (resp. 90 ) for participants who were asked to memorize 1 PAO story (resp. 2 PAO stories) is significantly better than the recall rate for participants who were asked to memorize 4 PAO stories. These findings yield concrete advice for improving constructions of password management schemes and future user studies. We all have experienced forgetting habitual actions among our daily activities. For example, we probably have forgotten to turn the lights off before leaving a room or turn the stove off after cooking. In this paper, we propose a solution to the problem of issuing notifications on actions that may be missed. This involves learning about interdependencies between actions and being able to predict an ongoing action while segmenting the input video stream. In order to show a proof of concept, we collected a new egocentric dataset, in which people wear a camera while making lattes. We show promising results on the extremely challenging task of issuing correct and timely reminders. We also show that our model reliably segments the actions, while predicting the ongoing one when only a few frames from the beginning of the action are observed. The overall prediction accuracy is 46.2 when only 10 frames of an action are seen (2 3 of a sec). Moreover, the overall recognition and segmentation accuracy is shown to be 72.7 when the whole activity sequence is observed. Finally, the online prediction and segmentation accuracy is 68.3 when the prediction is made at every time step. In the current fast-paced world, people tend to possess limited knowledge about things from the past. For example, some young users may not know that Walkman played similar function as iPod does nowadays. In this paper, we approach the temporal correspondence problem in which, given an input term (e.g., iPod) and the target time (e.g. 1980s), the task is to find the counterpart of the query that existed in the target time. We propose an approach that transforms word contexts across time based on their neural network representations. We then experimentally demonstrate the effectiveness of our method on the New York Times Annotated Corpus. Human declarative memory involves a systematic organization of information that supports generalizations and inferences from acquired knowledge. This kind of memory depends on the hippocampal region in humans, but the extent to which animals also have declarative memory, and whether inferential expression of memory depends on the hippocampus in animals, remains a major challenge in cognitive neuroscience. To examine these issues, we used a test of transitive inference pioneered by Piaget to assess capacities for systematic organization of knowledge and logical inference in children. In our adaptation of the test, rats were trained on a set of four overlapping odor discrimination problems that could be encoded either separately or as a single representation of orderly relations among the odor stimuli. Normal rats learned the problems and demonstrated the relational memory organization through appropriate transitive inferences about items not presented together during training. By contrast, after disconnection of the hippocampus from either its cortical or subcortical pathway, rats succeeded in acquiring the separate discrimination problems but did not demonstrate transitive inference, indicating that they had failed to develop or could not inferentially express the orderly organization of the stimulus elements. These findings strongly support the view that the hippocampus mediates a general declarative memory capacity in animals, as it does in humans. We develop a probabilistic model of human memory performance in free recall experiments. In these experiments, a subject first studies a list of words and then tries to recall them. To model these data, we draw on both previous psychological research and statistical topic models of text documents. We assume that memories are formed by assimilating the semantic meaning of studied words (represented as a distribution over topics) into a slowly changing latent context (represented in the same space). During recall, this context is reinstated and used as a cue for retrieving studied words. By conceptualizing memory retrieval as a dynamic latent variable model, we are able to use Bayesian inference to represent uncertainty and reason about the cognitive processes underlying memory. We present a particle filter algorithm for performing approximate posterior inference, and evaluate our model on the prediction of recalled words in experimental data. By specifying the model hierarchically, we are also able to capture inter-subject variability. This paper presents findings from a study of how knowledge workers use the Web to seek external information as part of their daily work. Thirty-four users from seven companies took part in the study. Participants were mainly IT specialists, managers, and research marketing consulting staff working in organizations that included a large utility company, a major bank, and a consulting firm. Participants answered a detailed questionnaire and were interviewed individually in order to understand their information needs and information seeking preferences. A custom-developed WebTracker software application was installed on each of their work place PCs, and participants' Web-use activities were then recorded continuously during two-week periods. The WebTracker recorded how participants used the browser to seek information on the Web: it logged menu choices, button bar selections, and keystroke actions, allowing browsing and searching sequences to be reconstructed. In a second round of personal interviews, participants recalled critical incidents of using information from the Web. Data from the two interviews and the WebTracker logs constituted the database for analysis. Sixty-one significant episodes of information seeking were identified. A model was developed to describe the common repertoires of information seeking that were observed. On one axis of the model, episodes were plotted according to the four scanning modes identified by Aguilar (1967), Weick and Daft (1983): undirected viewing, conditioned viewing, informal search, and formal search. Each mode is characterized by its own information needs and information seeking strategies. On the other axis of the model, episodes were plotted according to the occurrence of one or more of the six categories of information seeking behaviors identified by Ellis (1989, 1990): starting, chaining, browsing, differentiating, monitoring, and extracting. The study suggests that a behavioral framework that relates motivations (Aguilar) and moves (Ellis) may be helpful in analyzing patterns of Web-based information seeking. Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type token distinction. Abstract In this article we propose an account of the early development of children's knowledge about the mind and report two studies designed to test a part of it. According to this “connections-representations” account, young children begin their discovery of the mental world by learning that they and other people have internal experiences or mental states that connect them cognitively to external objects and events-experiences such as seeing them, hearing them, and wanting them. Later, they realize that the same object can be seriously (other than in pretense) mentally represented in different, seemingly contradictory ways: for example, as A in appearance, B in reality, C according to their perceptual or conceptual perspective, and D according to another person's. The results of both studies confirmed the prediction that 3-year-olds would perform well on appearance-reality and perceptual perspective-taking tasks requiring only an understanding of cognitive connections, but perform poorly on tasks requiring an understanding of seemingly-contrary-to-fact mental representations. To illustrate, children of this age had little difficulty determining that they could hear but could not see a noise-making object located on the experimenter's side of a barrier, and that the experimenter could see it (connections-level tasks). In contrast, they were largely unable to say, for instance, that a toy bear held behind a large elephant mask and emitting a cat sound looked like an elephant, sounded like a cat, and really was a toy bear (representations-level tasks)—even though the experimenter had actually told them previously what it looked like, sounded like, and really was. The article concludes with speculations about the possible origins of connections and representations knowledge and observations about the significance of these acquisitions for the child's development. Episodic memory is widely conceived as a fundamentally constructive, rather than reproductive, process that is prone to various kinds of errors and illusions. With a view towards examining the functions served by a constructive episodic memory system, we consider recent neuropsychological and neuroimaging studies indicating that some types of memory distortions reflect the operation of adaptive processes. An important function of a constructive episodic memory is to allow individuals to simulate or imagine future episodes, happenings and scenarios. Since the future is not an exact repetition of the past, simulation of future episodes requires a system that can draw on the past in a manner that flexibly extracts and recombines elements of previous experiences. Consistent with this constructive episodic simulation hypothesis, we consider cognitive, neuropsychological and neuroimaging evidence showing that there is considerable overlap in the psychological and neural processes involved in remembering the past and imagining the future. Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (i.e. forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories. This paper presents a real-time vision-based system to assist a person with dementia wash their hands. The system uses only video inputs, and assistance is given as either verbal or visual prompts, or through the enlistment of a human caregiver's help. The system combines a Bayesian sequential estimation framework for tracking hands and towel, with a decision-theoretic framework for computing policies of action. The decision making system is a partially observable Markov decision process, or POMDP. Decision policies dictating system actions are computed in the POMDP using a point-based approximate solution technique. The tracking and decision making systems are coupled using a heuristic method for temporally segmenting the input video stream based on the continuity of the belief state. A key element of the system is the ability to estimate and adapt to user psychological states, such as awareness and responsiveness. We evaluate the system in three ways. First, we evaluate the hand-tracking system by comparing its outputs to manual annotations and to a simple hand-detection method. Second, we test the POMDP solution methods in simulation, and show that our policies have higher expected return than five other heuristic methods. Third, we report results from a ten-week trial with seven persons moderate-to-severe dementia in a long-term care facility in Toronto, Canada. The subjects washed their hands once a day, with assistance given by our automated system, or by a human caregiver, in alternating two-week periods. We give two detailed case study analyses of the system working during trials, and then show agreement between the system and independent human raters of the same trials. When glancing at a magazine, or browsing the Internet, we are continuously being exposed to photographs. Despite of this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stitch to our minds, and other are forgotten. In this paper we focus on the problem of predicting how memorable an image will be. We show that memorability is a stable property of an image that is shared across different viewers. We introduce a database for which we have measured the probability that each picture will be remembered after a single view. We analyze image features and labels that contribute to making an image memorable, and we train a predictor based on global image descriptors. We find that predicting image memorability is a task that can be addressed with current computer vision techniques. Whereas making memorable images is a challenging task in visualization and photography, this work is a first attempt to quantify this useful quality of images. Recent research has made significant advances in automatically constructing knowledge bases by extracting relational facts (e.g., Bill Clinton-presidentOf-US) from large text corpora. Temporally scoping such relational facts in the knowledge base (i.e., determining that Bill Clinton-presidentOf-US is true only during the period 1993 - 2001) is an important, but relatively unexplored problem. In this paper, we propose a joint inference framework for this task, which leverages fact-specific temporal constraints, and weak supervision in the form of a few labeled examples. Our proposed framework, CoTS (Coupled Temporal Scoping), exploits temporal containment, alignment, succession, and mutual exclusion constraints among facts from within and across relations. Our contribution is multi-fold. Firstly, while most previous research has focused on micro-reading approaches for temporal scoping, we pose it in a macro-reading fashion, as a change detection in a time series of facts' features computed from a large number of documents. Secondly, to the best of our knowledge, there is no other work that has used joint inference for temporal scoping. We show that joint inference is effective compared to doing temporal scoping of individual facts independently. We conduct our experiments on large scale open-domain publicly available time-stamped datasets, such as English Gigaword Corpus and Google Books Ngrams, demonstrating CoTS's effectiveness. The principles of recency and contiguity are two cornerstones of the theoretical and empirical analysis of human memory. Recency has been alternatively explained by mechanisms of decay, displacement, and retroactive interference. Another account of recency is based on the idea of variable context (Estes, 1955; Mensink & Raaijmakers, 1989). Such notions are typically cast in terms of a randomly fluctuating population of elements reflective of subtle changes in the environment or in the subjects' mental state. This random context view has recently been incorporated into distributed and neural network memory models (Murdock, 1997; Murdock, Smith, & Bai, 2001). Here we propose an alternative model. Rather than being driven by random fluctuations, this formulation, the temporal context model (TCM), uses retrieval of prior contextual states to drive contextual drift. In TCM, retrieved context is an inherently asymmetric retrieval cue. This allows the model to provide a principled explanation of the widespread advantage for forward recalls in free and serial recall. Modeling data from single-trial free recall, we demonstrate that TCM can simultaneously explain recency and contiguity effects across time scales. Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model ("generator") and a task solving model ("solver"). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks. This paper introduces a highly competitive contingent planner, that uses the novel idea of encoding belief states as disjunctive normal form formulae ( 2009), for the search for solutions in the belief state space. In ( 2009), a complete transition function for computing successor belief states in the presence of incomplete information has been defined. This work extends the function to handle non-deterministic and sensing actions in the AND OR forward search paradigm for contingent planning solutions. The function allows one, under reasonable assumptions, to compute successor belief states efficiently, i.e., in polynomial time. The paper also presents a novel variant of an AND OR search algorithm, called PrAO (Pruning AND OR search), which allows the planner to significantly prune the search space; furthermore, by the time a solution is found, the remaining search graph is also the solution tree for the contingent planing problem. The strength of these techniques is confirmed by the empirical results obtained from a large set of benchmarks available in the literature. Associative memories are devices that are able to learn messages and to recall them in presence of errors or erasures. Their mechanics is similar to that of error correcting decoders. However, the role of correlation is opposed in the two devices, used as the essence of the retrieval process in the first one and avoided in the latter. In this paper, original codes are introduced to allow the effective combination of the two domains. The main idea is to associate a clique in a binary neural network with each message to learn. The obtained performance is dramatically better than that given by the state of the art, for instance Hopfield Neural Networks. Moreover, the model proposed is biologically plausible; it uses sparse binary connections between clusters of neurons provided with only two operations: sum and selection of maximum.