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Artists, advertisers, and photographers are routinely presented with the task of creating an image that a viewer will remember. While it may seem like image memorability is purely subjective, recent work shows that it is not an inexplicable phenomenon: variation in memorability of images is consistent across subjects, suggesting that some images are intrinsically more memorable than others, independent of a subjects ’ contexts and biases. In this paper, we used the publicly available memorability dataset of Isola et al. [13], and augmented the object and scene annotations with interpretable spatial, content, and aesthetic image properties. We used a feature-selection scheme with desirable explaining-away properties to determine a compact set of attributes that characterizes the memorability of any individual image. We find that images of enclosed spaces containing people with visible faces are memorable, while images of vistas and peaceful scenes are not. Contrary to popular belief, unusual or aesthetically pleasing scenes do not tend to be highly memorable. This work represents one of the first attempts at understanding intrinsic image memorability, and opens a new domain of investigation at the interface between human cognition and computer vision.

Perfect memory and recall provides a mixed blessing. While flawless recollection of episodic data allows for increased reasoning, photographic memory can hinder a robot’s ability to operate in real-time dynamic environments. Human-inspired forgetting methods may enable robotic systems to rid themselves of out-dated, irrelevant, and erroneous data. This paper presents the ActSimple algorithm and an associated experimental analysis. The Act-Simple algorithm is a novel approach to improving robotic performance by filtering data available to existing algorithms. The experimental analysis tested the effectiveness of five forgetting algorithms in a WiFi signal strength estimation task. The results suggest that forgetting can improve estimation accuracy while reducing the number of sensor readings required. The simplified version of Act-Simple outperformed the other forgetting methods and appears to be a flexible and adaptable means of incorporating human-inspired forgetting into robotic systems.

In three experiments, we examined connections between item-recognition memory and memory for itemposition information. With sequences of compound gratings as study and probe items, subjects made either itemposition judgments (Experiments 1 and 2), by identifying the serial position of the study item that matched the probe, or recognition judgments (Experiment 3), by judging whether the probe had or had not been presented in the study series. Integrating a summed-similarity account of recognition into a signal detection framework shows that the variance of summed similarities on lure trials (probe not present in the study series) exceeds the variance on target trials (probe present in the study series). This prediction is borne out by the empirical zROC functions, all of which had slopes that were greater than 1. Additionally, about 25 % of correct recognitions were accompanied by incorrect item position identification. Misidentifications of item position arose from two sources—structural similarity and positional similarity—which combined in an approximately additive fashion. Working within a framework of exemplar-similarity

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We investigated the mechanisms by which concepts are learned from examples by manipulating the presentation order in which the examples were presented to subjects. We introduce the idea of a rule-based presentation order, which is a sequence that respects the internal organization of the examples within a category. We find that such an order substantially facilitates learning, as compared with previously known beneficial orders, such as a similarity-based order. We discuss this result in light of the central distinction between rule-based and similaritybased learning models. A number of studies have investigated whether category learning is influenced by the order in which examples are presented. Elio and Anderson (1981) found that categories are learned faster when training is blocked into groups of mutually similar examples (see also Elio & Anderson, 1984). More recently, Medin and Bettger (1994) demonstrated a strong learning advantage when training objects were presented in an order that tended to maximize similarity between successive examples. Other

Is forgetting in the short term due to decay with the mere passage of time, interference from other memoranda, or both? Past research on short-term memory has revealed some evidence for decay and a plethora of evidence showing that short-term memory is worsened by interference. However, none of these studies has directly contrasted decay and interference in short-term memory in a task that rules out the use of rehearsal processes. In this article the authors present a series of studies using a novel paradigm to address this problem directly, by interrogating the operation of decay and interference in short-term memory without rehearsal confounds. The results of these studies indicate that short-term memories are subject to very small decay effects with the mere passage of time but that interference plays a much larger role in their degradation. The authors discuss the implications of these results for existing models of memory decay and interference.

We present a Dynamical Integrated Memory Hypothesis (DIMH) and illustrate its use by arguing that forgetting curves are emergent properties of dynamical memory that includes decay and influences of complex context on memory traces. Because forgetting curves are emergent, it is not likely that a single simple function will be able to model them. Forgetting at different time scales is similar because similar dynamics occur at each scale and not because there is a single underlying mechanism that produces them. We argue that the dynamical systems approach is particularly suited for investigating systems that evolve, such as memory, at a very abstract level.