Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks.

https://proceedings.neurips.cc/paper/2014/file/3fe94a002317b5f9259f82690aeea4cd-Paper.pdf

Learning Deep Features for Scene Recognition using Places Database

The concept of similarity, or a sense of 'sameness' among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step-by-step fashion how MDS is performed, and key issues that may arise during analysis. WIREs Cogn Sci 2013, 4:93-103. doi: 10.1002/wcs.1203 This article is categorized under: Psychology > Perception and Psychophysics.

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Multidimensional scaling: Multidimensional scaling

Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration

One of the major lessons of memory research has been that human memory is fallible, imprecise, and subject to interference. Thus, although observers can remember thousands of images, it is widely assumed that these memories lack detail. Contrary to this assumption, here we show that long-term memory is capable of storing a massive number of objects with details from the image. Participants viewed pictures of 2,500 objects over the course of 5.5 h. Afterward, they were shown pairs of images and indicated which of the two they had seen. The previously viewed item could be paired with either an object from a novel category, an object of the same basic-level category, or the same object in a different state or pose. Performance in each of these conditions was remarkably high (92%, 88%, and 87%, respectively), suggesting that participants successfully maintained detailed representations of thousands of images. These results have implications for cognitive models, in which capacity limitations impose a primary computational constraint (e.g., models of object recognition), and pose a challenge to neural models of memory storage and retrieval, which must be able to account for such a large and detailed storage capacity.

/pdf/visual-long-term-memory-has-a-massive-storage-capacity-for-175nury4yu.pdf

Visual long-term memory has a massive storage capacity for object details

Tulving (2000) provided a concise, general definition of memory as the “neurocognitive capacity to encode, store, and retrieve information,” and suggested the possibility that there are many separate memory systems that fit this definition. Indeed, one of the primary aims of modern memory research has been to identify these different memory systems (Schacter & Tulving, 1994). This approach has lead to an extensive taxonomy of memory systems which are characterized by differences in timing, storage capacity, conscious access, active maintenance, and mechanisms of operation.

Early on, William James (1890) proposed the distinction between primary memory – the information held in the ‘conscious present’ – and secondary memory, which consists of information that is acquired, stored outside of conscious awareness, and then later remembered. This distinction maps directly onto the modern distinction between short-term memory (henceforth working memory) and long-term memory (Atkinson & Shiffrin, 1968; Scoville & Milner, 1957; Waugh & Norman, 1965). The most salient difference between these systems is their capacity: the active, working memory system has an extremely limited capacity of only a few items (Cowan, 2001; Cowan, 2005; Miller, 1956), whereas the passive, long-term memory system can store thousands of items (Brady et al. 2008; Standing, 1973; Voss, 2009) with remarkable fidelity (Brady et al., 2008; Konkle et al., 2010a).

The emphasis on memory systems and memory processes has been quite valuable in shaping cognitive and neural models of memory. In general, this approach aims to characterize memory systems in a way that generalizes over representational content (Schacter & Tulving, 1994). For example, working memory is characterized by a severely limited capacity regardless of whether items are remembered visually or verbally (e.g., Baddeley, 1986), and long-term memory has a very high capacity whether the items remembered are pictures (e.g., Standing, 1973), words (e.g., Shepard, 1967), or associations (e.g., Voss, 2009). However, generalization across content leaves many basic questions unanswered regarding the nature of stored representations: What is the structure and format of those representations?

Research on visual perception takes the opposite approach, attempting to determine what is being represented, and to generalize across processes. For example, early stages of visual representation consist of orientation and spatial frequency features. Vision research has measured the properties of these features, such as their tuning curves and sensitivity (e.g., Blakemore & Campbell, 1969), and shown that these tuning properties are constant across several domains of processing (e.g., from simple detection to visual search).

Thus, the intersection between memory and vision is a particularly interesting domain of research because it concerns both the processes of memory and the nature of the stored representations (Luck & Hollingworth, 2008). Recent research within the vision science community at this intersection between memory and vision has been quite fruitful. For example, working memory research has shown an important item/resolution tradeoff: as the number of items remembered increases, the precision with which each one is remembered decreases, possibly with an upper bound on the number of items that may be stored (Alvarez & Cavanagh, 2004; Zhang & Luck, 2008), or possibly without an upper bound (Bays, Catalao & Husain, 2009; Wilken & Ma, 2004). In long-term memory, it is possible to store thousands of detailed object representations (Brady et al. 2008; Konkle et al., 2010b) but only for meaningful items that connect with stored knowledge (Konkle et al., 2010b; Wiseman and Neisser, 1974).

Here we review recent research in the domains of visual working memory and visual long-term memory, focusing on how models of these memory systems are altered and refined by taking the contents of memory into account.

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A review of visual memory capacity: Beyond individual items and toward structured representations.

Humans have a massive capacity to store detailed information in visual long-term memory. The present studies explored the fidelity of these visual long-term memory representations and examined how conceptual and perceptual features of object categories support this capacity. Observers viewed 2,800 object images with a different number of exemplars presented from each category. At test, observers indicated which of 2 exemplars they had previously studied. Memory performance was high and remained quite high (82% accuracy) with 16 exemplars from a category in memory, demonstrating a large memory capacity for object exemplars. However, memory performance decreased as more exemplars were held in memory, implying systematic categorical interference. Object categories with conceptually distinctive exemplars showed less interference in memory as the number of exemplars increased. Interference in memory was not predicted by the perceptual distinctiveness of exemplars from an object category, though these perceptual measures predicted visual search rates for an object target among exemplars. These data provide evidence that observers’ capacity to remember visual information in long-term memory depends more on conceptual structure than perceptual distinctiveness.

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Conceptual distinctiveness supports detailed visual long-term memory for real-world objects.

http://web.mit.edu/~tkonkle/www/PAPERS/Konkle_2012_Neuron.pdf

A Real-World Size Organization of Object Responses in Occipitotemporal Cortex

Observers can store thousands of object images in visual long-term memory with high fidelity, but the fidelity of scene representations in long-term memory is not known. Here, we probed scene-representation fidelity by varying the number of studied exemplars in different scene categories and testing memory using exemplar-level foils. Observers viewed thousands of scenes over 5.5 hr and then completed a series of forced-choice tests. Memory performance was high, even with up to 64 scenes from the same category in memory. Moreover, there was only a 2% decrease in accuracy for each doubling of the number of studied scene exemplars. Surprisingly, this degree of categorical interference was similar to the degree previously demonstrated for object memory. Thus, although scenes have often been defined as a superset of objects, our results suggest that scenes and objects may be entities at a similar level of abstraction in visual long-term memory.

/pdf/scene-memory-is-more-detailed-than-you-think-the-role-of-4ykppqgvf2.pdf

Talia Konkle

Papers

Visual long-term memory has a massive storage capacity for object details

A review of visual memory capacity: Beyond individual items and toward structured representations.

Conceptual distinctiveness supports detailed visual long-term memory for real-world objects.

A Real-World Size Organization of Object Responses in Occipitotemporal Cortex

Scene Memory Is More Detailed Than You Think The Role of Categories in Visual Long-Term Memory