«Features, objects, and ensembles in visual perception and memory» тема диссертации и автореферата по ВАК РФ 19.00.01, доктор наук Уточкин Игорь Сергеевич

  • Уточкин Игорь Сергеевич
  • доктор наукдоктор наук
  • 2021, ФГАОУ ВО «Национальный исследовательский университет «Высшая школа экономики»
  • Специальность ВАК РФ19.00.01
  • Количество страниц 109
Уточкин Игорь Сергеевич. «Features, objects, and ensembles in visual perception and memory»: дис. доктор наук: 19.00.01 - Общая психология, психология личности, история психологии. ФГАОУ ВО «Национальный исследовательский университет «Высшая школа экономики». 2021. 109 с.

Оглавление диссертации доктор наук Уточкин Игорь Сергеевич

Table of Contents

1. Introduction

2. Features vs. objects as representational units of perception and memory

3. The richness of ensemble perception

4. Ensemble statistics shape representations of individual objects in working memory

5. Conclusion



Appendix A

Appendix B

Appendix C

Appendix D

Appendix E

Appendix F

Appendix G

Рекомендованный список диссертаций по специальности «Общая психология, психология личности, история психологии», 19.00.01 шифр ВАК

Введение диссертации (часть автореферата) на тему ««Features, objects, and ensembles in visual perception and memory»»

1. Introduction

1.1. General research problem

Human visual experience is introspectively organized in an object-based fashion. That is, people normally see the world consisting of a number of various meaningful things without effort. The same can be said of visual memory: People can remember thousands of images of objects and easily recognize them among other objects, even if they have seen them only once (Brady et al., 2008; Standing, 1973).

Do we indeed have complete and stable representations of all objects we encounter in visual cognition? There are impressive demonstrations that this completeness and stability are an illusion, sometimes referred to as the Grand Illusion of our consciousness (Noe, 2002). Inattentional blindness is one such example showing a failure to spot an otherwise salient object when attention is engaged in a difficult task (Mack & Rock, 1998). Change blindness, an inability to see a large change in a visual scene when the change is masked by a global transient (such as eye movements, eyeblinks, brief occlusions), shows a failure of conscious object perception even when we intentionally look for such changes (Rensink, 2002). These and other demonstrations suggest that our capacity of conscious object perception is limited to a handful of items at one time. This processing "bottleneck" is often associated with the capacity of attention (Pylyshyn & Storm, 1988) or with working memory (Cowan, 2001). One influential idea why this bottleneck arises within the perceptual system is that correct object perception requires binding of independently processed basic features of multiple different objects (Treisman, 1996; Wolfe et al., 2011). The enormous combinatorial complexity of the visible world given the potential variety of the features (Tsotsos, 1988) makes binding hardly possible to run for all objects in parallel. If we imagine that the complete parallel object recognition would require not only correct binding of visible features but also linking these bindings to correct memory recordings, this would raise computational complexity of perception and recognition enormously (Tsotsos, 1988).

If capacity limits of full object recognition are granted, then what underlies this

introspective ease of perceiving and remembering the world consisting of many different objects? Can it be accomplished with sparse representations that do not need separate access to each object in the entirety of its details? If yes, then how is it done? How can these representations be used for various visual and memory tasks? The present work summarizes research that I and my collaborators have run to investigate these questions.

1.2. Theoretical basis of the current work includes the contemporary conceptual architectures of visual perception with "shallow" and "deep" processing (e.g., the distinction between preattentive and attentional processing - Neisser, 1967; Treisman, Gelade, 1980; selective and non-selective pathways for object and scene processing - Wolfe, Vo, Greene, Evans, 2011; feedforward and reverse hierarchies in conscious visual perception - Hochstein, Ahissar, 2002; the functional continuum of attentional states from focused to distributed attention" - Treisman, 2006, etc.); the theory of global perceptual representation of multiple objects and scenes as the computation of ensemble statistical representations (Alvarez, 2011; Chong, Treisman, 2003; Haberman, Whitney, 2012; Whitney, Yamanashi Leib, 2018; Treisman, 2006); the idea of hierarchical encoding theory in visual memory (Brady, Konkle, Alvarez, 2011; Brady, Alvarez, 2011; Corbett, 2017).

1.3. Summary of scientific novelty

1. The present dissertation suggests a general view of the format used to represent multiple objects in the visual system, from one-shot visual perception to long-term memory. The basic idea underlying this view is that, given the severe limits of deep object processing, the perception of multiple objects and their maintenance in memory can be carried out using relatively shallow and sparse feature representations of different kinds. These feature representations, in turn, can be summarized and consciously accessed as a broad spectrum of ensemble statistics.

2. We present experimental evidence that, even though complex real-world objects are subjectively experienced as holistic things (which is also reflected in some the-


ories), the real-world objects can be stored as separate features related to various plausible independent transformations of object appearances (for example, differences between object exemplars within same basic category, or differences between states of the same object), but that the features can be misbound at retrieval.

3. We present evidence for the efficient coding of numerous properties of multiple objects in a form of ensemble representation, a generalized visual impression of multiple objects in a form of statistical summaries. We demonstrated that various types of ensemble statistics (average feature, feature variability, or the approximate number of objects) can be easily extracted from the same set of items without any loss in accuracy associated with the distribution of attention between them. In addition, we demonstrated that the visual system takes into account the rich contextual information about individual objects when summaries their ensemble statistics.

4. A new theory of ensemble-based rapid visual categorization and segmentation of multiple objects is proposed and tested in different visual tasks (e.g., visual search, texture discrimination). The theory suggests that the shape of a feature distribution in an ensemble (smooth unimodal or uniform vs. sharp, polymodal) determines whether all objects in a set are perceived as a single categorical group or parsed into several groups belonging to different categories.

5. We present new empirical evidence for the hierarchical interaction and influence of the ensemble information on individual object representations in visual working memory. In particular, we showed that the recall precision of an individual feature is not fixed and that it inherits the amount of noise from an ensemble representation this object belonged to and that the individual reports are systematically biased toward the mean feature of all objects.

1.4. Theoretical significance

The theoretical significance of the current work can be characterized by its contribution to the general cognitive theory and architecture of visual representations. Moreover, the author's work, such as the theory of rapid visual categorization and

segmentation, contribute to understanding the role of various representations (e.g., ensemble representations) in the variety of visual tasks.

1.5. Applied significance is relevant for the possible use of the reported findings and conclusions for psychologically grounded principles of efficient information displays and visualization. The reported findings about ensemble summary statistics can be useful for teaching regular statistics. The results of the reported work are partially used in undergraduate courses, "Cognitive Psychology" and "Psychology and Neurophysiology of Perception and Attention", at the HSE University.

1.6. Statements for the defense

1. Although the visible world is introspectively perceived and remembered in an object-based manner, the information about big sets of objects can be conveyed without the need to deeply process each object as a whole. Sparse representations of various features and ensemble statistics of these features can serve as efficient proxies of complete and detailed object representations. Feature, object, and ensemble representations can be parts of a flexible hierarchical system that gives an access to a general impression of the environment filled with multiple objects and also guides deep processing of individual objects.

2. Meaningful features of complex real-world objects (the features corresponding to physically separable variations in appearance, such as exemplar and state features) are stored independently in visual long-term memory

3. Ensemble representations of large sets of objects in the form of feature summary statistics (for example, an average feature, feature variability, or the number of items) are an effective means of organizing the information about the set given the fundamental capacity limitations on deep object processing. In particular, different summary statistics are read out from the same set in parallel, that is, without the cost of dividing attention between these statistics. In addition, the computation of ensemble statistics correctly takes into account context features in which individual objects are

presented (for example, distance and depth cues are taken into account when the average size is estimated), and, hence, the resulting summary statistics adequately reflect veridical properties of visible objects in the real world.

4. The shape of an ensemble feature distribution can be used as a cue for rapid visual segmentation and categorisation of multiple spatially intermixed objects. Therefore, information about the qualitative diversity of objects in the visual scene can be accessed via ensemble statistics, without the need for deep processing of all objects.

5. Ensemble statistical representations can be used to organize and optimize information about individual objects under the limited capacity of holding the information about individual objects, for example, in a working memory task with several individuals. These organizing and optimizing effects are demonstrated by findings that memory reports of individual object features inherit the statistical properties of an ensemble these objects belong to.

1.7. Data collection

Six out of seven articles selected for the defense describe sets of psychophysical experiments. Overall, twenty separate experiments are described in these papers, with over 800 observers taken part in these experiments. Observers were tested either in a laboratory (n = 340), or online via Amazon Mechanical Turk (n = 496). The laboratory experiments were run at the Cognitive Research Laboratory (HSE University, Moscow, Russia), Vision and Memory Laboratory (University of California, San Diego, USA), Visual Attention Laboratory (Brigham and Women's Hospital and Harvard Medical School, Boston, USA).

1.8. Public presentations on the topic and grant support

The results of the present work have been publicly presented since 2012 to 2020 in 46 talks and posters at 20 conferences in Russia and worldwide. These included: Annual Vision Sciences Society Meeting (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020), European Conference on Visual Perception (2013, 2015, 2018, 2019),

Conference "Cognitive Science in Moscow: New Research" (2013, 2019), IACS International Conference on Cognitive Science (2014, 2016), etc. Eight colloquium talks have been presented in the HSE Laboratory for Cognitive Research (2019), Visual Attention Laboratory at Brigham and Women's Hospital (2015, 2018, 2020), Vision and Memory Laboratory at University of California San Diego (2016, 2019), Vision Science Laboratory at Harvard University (2015), Schacter Memory Laboratory at Harvard University (2018).

The studies presented as different parts of the dissertations have been funded by the Russian Science Foundation (grant #18-18-00334, 2018-2020), by the Russian Foundation for Basic Research (grants #12-06-31223, 2012; #15-06-07514, 2015-2016), and by the Basic Research Programme at the HSE University (2012-2019).

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Заключение диссертации по теме «Общая психология, психология личности, история психологии», Уточкин Игорь Сергеевич

8. Conclusion

The results of our study advance our understanding of how the visual system splits poorly organized sets of multiple items (variable in attributes and intermixed in the space, which often corresponds to the natural organization of the real world) into groups representing categorically different classes of objects. Ensemble summary statistics, proven as an efficient tool to organize visual cognition in various ways, seem to guide the massive transformation of continuously distributed visual features into discrete categories of objects. Here we showed that segmenability, a previously described emergent property associated with the shape of feature distribution, plays a key role in the categorization of objects whose categorical differences are defined by particular conjunctions of more simple features.

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