Fig 9 - uploaded by Marina Pavlovskaya
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Average normalized RT for each category object rank. Objects were ranked from 1 to 30 for each category according to the RT in the scoring object typicality test, where observers simply indicated which of two objects belonged to a previously named category. We then normalized the actual RTs, averaged over participants, and compare the
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A bombardment of information overloads our sensory, perceptual and cognitive systems, which must integrate new information with memory of past scenes and events. Mechanisms employed to overcome sensory system bottlenecks include selective attention, Gestalt gist perception, categorization, and the recently investigated ensemble encoding of set summ...
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... average RTs were then normalized by: Normalized RT = (RT − minRT) / (maxRT − minRT), where minRT and maxRT are the minimum and maximum RTs for that category, and (maxRT − minRT) is the range of average (across participant) response times for each category. Figure 9 (blue symbols) demonstrates the average normalized RT for each category object rank. There is a high degree of across-category similarity, evidenced by the small standard error among the categories. ...
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... There is a high degree of across-category similarity, evidenced by the small standard error among the categories. Interestingly, RT dependence on rank is steeper at the edges of the category objects, near the prototype (rank = 1) and far from it (rank = 30). We also measured the acrossparticipant ranking and found small standard deviations (see Fig. 9, red symbols). We shall now use this ranking as a typicality index for each item in its category, to measure the impact of typicality on object memory in the RSVP sequence ...
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... the Experiment 2 nonlinear dependence of typicality rank on image RT, Fig. 10a and c data fit well a linear regression. This may be because of the near linearity of the Fig. 9 curve, except at its extremes, and because Fig. 10a averages over nonmember rank, Fig. 10c over member rank, and Fig. 11a over ...
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... Additionally, (one-tailed) t-tests between the averaged results over participants for different subtype combinations were performed to investigate ancestor and range representation effects. Some of these experiments have been reported previously in meeting abstract or brief communication format (e.g., Hochstein, 2019;Hochstein et al., 2018Hochstein et al., , 2020Hochstein, Khayat, Pavlovskaya, Bonneh, Soroker, & Fusi, 2019;Khayat & Hochstein, 2019b). ...
Perception, representation, and memory of ensemble statistics has attracted growing interest. Studies found that, at different abstraction levels, the brain represents similar items as unified percepts. We found that global ensemble perception is automatic and unconscious, affecting later perceptual judgments regarding individual member items. Implicit effects of set mean and range for low-level feature ensembles (size, orientation, brightness) were replicated for high-level category objects. This similarity suggests that analogous mechanisms underlie these extreme levels of abstraction. Here, we bridge the span between visual features and semantic object categories using the identical implicit perception experimental paradigm for intermediate novel visual-shape categories, constructing ensemble exemplars by introducing systematic variations of a central category base or ancestor. In five experiments, with different item variability, we test automatic representation of ensemble category characteristics and its effect on a subsequent memory task. Results show that observer representation of ensembles includes the group’s central shape, category ancestor (progenitor), or group mean. Observers also easily reject memory of shapes belonging to different categories, i.e. originating from different ancestors. We conclude that complex categories, like simple visual form ensembles, are represented in terms of statistics including a central object, as well as category boundaries. We refer to the model proposed by Benna and Fusi ( bioRxiv 624239, 2019) that memory representation is compressed when related elements are represented by identifying their ancestor and each one’s difference from it. We suggest that ensemble mean perception, like category prototype extraction, might reflect employment at different representation levels of an essential, general representation mechanism.
... Ensemble perception has been studied for basic parameters, including size (Allik, Toom, Raidvee, Averin, & Kreegipuu, 2014;Ariely, 2001;Corbett & Oriet, 2011;Morgan, Chubb, & Solomon, 2008;Solomon, 2010), orientation (Alvarez & Oliva, 2009;Hochstein, Pavlovskaya, Bonneh, & Soroker, 2018), brightness (Bauer, 2009), spatial position (Alvarez & Oliva, 2008), and speed and direction of motion (Sweeny, Haroz, & Whitney, 2013). Summary statistics perception appears to be a general mechanism operating on various stimulus attributes, including these low-level parameters as noted above, and complex characteristics, such as facial expression or emotion and gender (Haberman & Whitney, 2007;Haberman & Whitney, 2009;Neumann, Schweinberger, & Burton, 2013), object lifelikeness (Yamanashi Leib, Kosovicheva, & Whitney, 2016), biological motion of human crowds (Sweeny, Haroz, & Whitney, 2013), numerical averaging (Brezis, Bronfman, & Usher, 2015) and even category membership Hochstein, Khayat, Pavlovskaya, Bonneh, Soroker, & Fusi, 2019) for recent reviews, see Bauer, 2015;Cohen et al., 2016;Haberman & Whitney, 2012;Hochstein, Pavlovskaya, Bonneh, & Soroker, 2015;Whitney & Yamanashi Leib, 2018; and an upcoming Attention, Perception, and Psychophysics special issue). ...
Previous studies have demonstrated a complex relationship between ensemble perception and outlier detection. We presented two array of heterogeneously oriented stimulus bars and different mean orientations and/or a bar with an outlier orientation, asking participants to discriminate the mean orientations or detect the outlier. Perceptual learning was found in every case, with improved performance accuracy and speeded responses. Testing for improved accuracy through cross-task transfer, we found considerable transfer from training outlier detection to mean discrimination performance, and none in the opposite direction. Implicit learning in terms of increased accuracy was not found in either direction when participants performed one task, and the second task's stimulus features were present. Reaction time improvement was found to transfer in all cases. This study adds to the already broad knowledge concerning perceptual learning and cross-task transfer of training effects.
... The situation with categorization is unlike that with sets, where we derive the set mean, on the fly, as we are presented with set members. Instead, when encountering an object (or group of objects belonging to a single category), we know the category to which it belongs, and we also know what is the prototype of that category and the category boundaries; there is no need, and no possibility, of deriving anew the category, prototype, and boundaries of a group of familiar objects (though we can learn new categories of unfamiliar objects; see Hochstein et al., 2019). Furthermore, categories may be learned and recognized semantically, while the basic features of sets are often nonsemantic. ...
Two cognitive processes have been explored that compensate for the limited information that can be perceived and remembered at any given moment. The first parsimonious cognitive process is object categorization. We naturally relate objects to their category, assume they share relevant category properties, often disregarding irrelevant characteristics. Another scene organizing mechanism is representing aspects of the visual world in terms of summary statistics. Spreading attention over a group of objects with some similarity, one perceives an ensemble representation of the group. Without encoding detailed information of individuals, observers process summary data concerning the group, including set mean for various features (from circle size to face expression). Just as categorization may include/depend on prototype and intercategory boundaries, so set perception includes property mean and range. We now explore common features of these processes. We previously investigated summary perception of low-level features with a rapid serial visual presentation (RSVP) paradigm and found that participants perceive both the mean and range extremes of stimulus sets, automatically, implicitly, and on-the-fly, for each RSVP sequence, independently. We now use the same experimental paradigm to test category representation of high-level objects. We find participants perceive categorical characteristics better than they code individual elements. We relate category prototype to set mean and same/different category to in/out-of-range elements, defining a direct parallel between low-level set perception and high-level categorization. The implicit effects of mean or prototype and set or category boundaries are very similar. We suggest that object categorization may share perceptual-computational mechanisms with set summary statistics perception.
