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      The Scene Perception & Event Comprehension Theory (SPECT) Applied to Visual Narratives

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          Abstract

          Understanding how people comprehend visual narratives (including picture stories, comics, and film) requires the combination of traditionally separate theories that span the initial sensory and perceptual processing of complex visual scenes, the perception of events over time, and comprehension of narratives. Existing piecemeal approaches fail to capture the interplay between these levels of processing. Here, we propose the Scene Perception & Event Comprehension Theory (SPECT), as applied to visual narratives, which distinguishes between front‐end and back‐end cognitive processes. Front‐end processes occur during single eye fixations and are comprised of attentional selection and information extraction. Back‐end processes occur across multiple fixations and support the construction of event models, which reflect understanding of what is happening now in a narrative (stored in working memory) and over the course of the entire narrative (stored in long‐term episodic memory). We describe relationships between front‐ and back‐end processes, and medium‐specific differences that likely produce variation in front‐end and back‐end processes across media (e.g., picture stories vs. film). We describe several novel research questions derived from SPECT that we have explored. By addressing these questions, we provide greater insight into how attention, information extraction, and event model processes are dynamically coordinated to perceive and understand complex naturalistic visual events in narratives and the real world.

          Abstract

          Comprehension of visual narratives like comics, picture stories, and films involves both decoding the visual content and construing the meaningful events they represent. The Scene Perception & Event Comprehension Theory (SPECT) proposes a framework for understanding how a comprehender perceptually negotiates the surface of a visual representation and integrates its meaning into a growing mental model.

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Speed of processing in the human visual system.

            How long does it take for the human visual system to process a complex natural image? Subjectively, recognition of familiar objects and scenes appears to be virtually instantaneous, but measuring this processing time experimentally has proved difficult. Behavioural measures such as reaction times can be used, but these include not only visual processing but also the time required for response execution. However, event-related potentials (ERPs) can sometimes reveal signs of neural processing well before the motor output. Here we use a go/no-go categorization task in which subjects have to decide whether a previously unseen photograph, flashed on for just 20 ms, contains an animal. ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset. We conclude that the visual processing needed to perform this highly demanding task can be achieved in under 150 ms.
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              The magical number 4 in short-term memory: a reconsideration of mental storage capacity.

              M N Cowan (2001)
              Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described. Four basic conditions in which chunks can be identified and capacity limits can accordingly be observed are: (1) when information overload limits chunks to individual stimulus items, (2) when other steps are taken specifically to block the recording of stimulus items into larger chunks, (3) in performance discontinuities caused by the capacity limit, and (4) in various indirect effects of the capacity limit. Under these conditions, rehearsal and long-term memory cannot be used to combine stimulus items into chunks of an unknown size; nor can storage mechanisms that are not capacity-limited, such as sensory memory, allow the capacity-limited storage mechanism to be refilled during recall. A single, central capacity limit averaging about four chunks is implicated along with other, noncapacity-limited sources. The pure STM capacity limit expressed in chunks is distinguished from compound STM limits obtained when the number of separately held chunks is unclear. Reasons why pure capacity estimates fall within a narrow range are discussed and a capacity limit for the focus of attention is proposed.
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                Author and article information

                Contributors
                loschky@ksu.edu
                Journal
                Top Cogn Sci
                Top Cogn Sci
                10.1111/(ISSN)1756-8765
                TOPS
                Topics in Cognitive Science
                John Wiley and Sons Inc. (Hoboken )
                1756-8757
                1756-8765
                04 September 2019
                January 2020
                : 12
                : 1 , 2017 Rumelhart Prize Issue Honoring Lila R. Gleitman Editor: Barbara Landau – Visual Narrative Research: An Emerging Field in Cognitive Science Editors: Neil Cohn and Joseph P. Magliano – Best of Papers from the 2019 Cognitive Science Society Conference Editor: Wayne D. Gray ( doiID: 10.1111/tops.v12.1 )
                : 311-351
                Affiliations
                [ 1 ] Department of Psychological Sciences Kansas State University
                [ 2 ] Department of Psychology University of Findlay
                [ 3 ] Department of Psychological Sciences Birkbeck, University of London
                [ 4 ] College of Education & Human Development Georgia State University
                Author notes
                [*] [* ] Correspondence should be sent to Lester C. Loschky, Department of Psychological Sciences, Kansas State University, 471 Bluemont Hall, 1114 Mid-Campus Dr. North, Manhattan, KS 66506, USA. E‐mail: loschky@ 123456ksu.edu

                Article
                TOPS12455
                10.1111/tops.12455
                9328418
                31486277
                a1f96007-12c1-43a6-b86c-83f8078d70e1
                © 2019 The Authors. Topics in Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 05 August 2019
                : 26 November 2018
                : 05 August 2019
                Page count
                Figures: 9, Tables: 0, Pages: 41, Words: 19539
                Categories
                Forthcoming Topic: Visual narrative research: An emerging field in cognitive science
                Article
                Custom metadata
                2.0
                January 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:27.07.2022

                scene perception,event perception,narrative comprehension,visual narratives,film,comics,eye movements,attention

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