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Some informational aspects of visual perception
... The brain is believed to utilize efficient neural representations for perceptual processing, aiming to maximize information content while minimizing metabolic expenditure [4,5]. Evidence of adaptive information compression spans various cognitive domains, including perception [6,7,8], working memory [9,10,11,12,13], cognitive mapping [14,15], and decision making [16,17,18,19]. ...
... Considering the ordering of images as described in Section 4.3, we observe that, on average, pairs of images at the center of the square of Panel A are pushed farther apart when reducing the capacity. Note that the observations made in this paragraph about panels A, B, F, G and K hold true for the Figures 4,5,7,8,9,10 and 11. In fact, they all represent the same baseline model, except for the fact that that data points have been assigned different colors to facilitate comparison with other models. ...
... This allows us to measure the magnitude of dilation or compression of the embeddings of a model with respect to those of an other model. Panel K of Figures 4,5,7,8,9, 10 and 11 reports the distortion matrix arising from a reduction of the encoding capacity of the baseline model. In particular, we consider the baseline model at high capacity as M 1 and the same model at low capacity as M 2. To compute such matrix, we first assign a label to each image according to the position of the corridors: for a image with corridors in position (i , j ), we assign the label l = 13 ⇤ i + j . ...
Living organisms rely on internal models of the world to act adaptively. These models cannot encode every detail and hence need to compress information. From a cognitive standpoint, information compression can manifest as a distortion of latent representations, resulting in the emergence of representations that may not accurately reflect the external world or its geometry. Rate-distortion theory formalizes the optimal way to compress information, by considering factors such as capacity limitations, the frequency and the utility of stimuli. However, while this theory explains why the above factors distort latent representations, it does not specify which specific distortions they produce. To address this question, here we systematically explore the geometry of the latent representations that emerge in generative models that operate under the principles of rate-distortion theory ($\beta$-VAEs). Our results highlight that three main classes of distortions of internal representations -- prototypization, specialization, orthogonalization -- emerge as signatures of information compression, under constraints on capacity, data distributions and tasks. These distortions can coexist, giving rise to a rich landscape of latent spaces, whose geometry could differ significantly across generative models subject to different constraints. Our findings contribute to explain how the normative constraints of rate-distortion theory distort the geometry of latent representations of generative models of artificial systems and living organisms.
... resource constraints [14][15][16][17]. In one instantiation of this theory, the redundancy reduction hypothesis posits that sensory circuits transform natural signals into representations to minimize or eliminate statistical dependencies between coordinates, essentially producing factorized response distributions [14,15,18]. ...
... resource constraints [14][15][16][17]. In one instantiation of this theory, the redundancy reduction hypothesis posits that sensory circuits transform natural signals into representations to minimize or eliminate statistical dependencies between coordinates, essentially producing factorized response distributions [14,15,18]. In a separate, but related, instantiation, sparse coding theory posits that population responses are optimized for sparsity [19][20][21], which is naturally interpreted as a constraint on the shape of the distribution of responses. ...
... We apply our algorithm to the problem of efficient nonlinear encoding of natural signals, specifically oriented filter responses to visual images. 3 Redundancy reduction theories posit that early sensory systems transform natural signal into neural representations with reduced statistical redundancies [14,15,17]. In support of this hypothesis, early sensory representations exhibit far less spatial and temporal correlations than natural signals [54,55] and methods such as linear ICA have been used to derive optimal representations of natural signals that are approximately matched to early sensory neuron responses [18,19,56]. ...
Efficient coding theory posits that sensory circuits transform natural signals into neural representations that maximize information transmission subject to resource constraints. Local interneurons are thought to play an important role in these transformations, shaping patterns of circuit activity to facilitate and direct information flow. However, the relationship between these coordinated, nonlinear, circuit-level transformations and the properties of interneurons (e.g., connectivity, activation functions, response dynamics) remains unknown. Here, we propose a normative computational model that establishes such a relationship. Our model is derived from an optimal transport objective that conceptualizes the circuit's input-response function as transforming the inputs to achieve a target response distribution. The circuit, which is comprised of primary neurons that are recurrently connected to a set of local interneurons, continuously optimizes this objective by dynamically adjusting both the synaptic connections between neurons as well as the interneuron activation functions. In an application motivated by redundancy reduction theory, we demonstrate that when the inputs are natural image statistics and the target distribution is a spherical Gaussian, the circuit learns a nonlinear transformation that significantly reduces statistical dependencies in neural responses. Overall, our results provide a framework in which the distribution of circuit responses is systematically and nonlinearly controlled by adjustment of interneuron connectivity and activation functions.
... 28 Predictive coding is an influential idea in sensory and cognitive neuroscience (Attneave 1954, 29 Barlow 1961, Srinivasan et al 1982, Rao andBallard 1991, Clark 2013;Friston 2018). ing sensory patterns are compared with predicted patterns, and the outcome of this comparison 31 -the error -serves to validate the predictive model, and as an input for the next stage of pro-32 cessing. ...
... The result of 171 subtraction is zero for the class of stimuli that fit that model, whereas non-zero output implies 172 a mismatch with the model, for example indicating the presence of an additional, weaker sound 173 source. The cancellation filter achieves both redundancy reduction, using stimulus regularity 174 (periodicity and/or spectral sparsity) to simplify the sensory code and remove structured inter-175 ference, and redundancy exploitation (Attneave 1954;Barlow 1961), in that delay estimates 176 ! characterize the stimulus structure. ...
... trends, or patterns may be predicted and factored out, as in the present in-channel can-314 cellation model. Third, the parameters of the predictive model, obtained by fitting it to sensory 315 input, reflect regularities within the world(Attneave 1954). Regularities are distributed une-316 venly in space and time, and their extent and boundaries may provide useful information, for 317 example to delimit an object and segregate it within a scene. ...
Predictive coding is an influential concept in sensory and cognitive neuroscience. It is often understood as involving top-down prediction of bottom-up sensory patterns, but the term also applies to feed-forward predictive mechanisms, for example in the retina. Here, I discuss a recent model of low-level predictive processing in the auditory brainstem that is of the feed-forward flavor. Past sensory input from the cochlea is delayed and compared with the current input, via a predictive model that is tuned with the objective of minimizing prediction error. This operation is performed independently within each peripheral channel, with parameters determined from information within that channel. The result is a sensory representation that is invariant to a certain class of interfering sounds (harmonic, quasi-harmonic, or spectrally sparse), thus contributing to Auditory Scene Analysis. The purpose of this paper is to discuss that model in the light of predictive coding, and examine how it might fit within a wider hierarchical model that supports the perceptual representation of objects and events in the world.
... As shown in the Figure 6, the information represented by the small blue squares along the diagonal of the ink screen is not merely the sum of individual black square information. In the research of Attneave et al. (1954), it is pointed out that the total entropy of a pattern is the sum of the entropy of non-repeating patterns and the entropy related to repeating patterns [34]. The entropy of repeating patterns is less than the cumulative sum of the entropy of individual repeating patterns. ...
... As shown in the Figure 6, the information represented by the small blue squares along the diagonal of the ink screen is not merely the sum of individual black square information. In the research of Attneave et al. (1954), it is pointed out that the total entropy of a pattern is the sum of the entropy of non-repeating patterns and the entropy related to repeating patterns [34]. The entropy of repeating patterns is less than the cumulative sum of the entropy of individual repeating patterns. ...
Existing methods for measuring the spatial information of area maps fail to take into account the diversity of adjacency relations and the heterogeneity of adjacency distances among area objects, resulting in insufficient measurement information. This article proposes a method for measuring area map information that considers the diversity of the node–edge and Gestalt principles. Firstly, this method utilizes the adjacency relations between the Voronoi diagram of area objects to construct an adjacency graph that characterizes the spatial distribution of area objects in area maps. This adjacency graph serves as the information representation of area maps. Secondly, the method selects four characteristic indicators, namely geometric information, node degree, adjacency distance, and adjacency strength, to represent the diversity of nodes and edges in the graph that affect spatial information. Finally, nodes in the adjacency graph are taken as the basic units, and the spatial information of area maps is comprehensively calculated by integrating the four characteristics that represent spatial information. To verify the validity and rationality of the proposed method, a dataset of continuously simplified area maps and a dataset of artificially simulated degrees of randomness were designed to evaluate the performance of the existing method and the method proposed in this paper. The results indicate that the correlation between the measurement results obtained by the method proposed in this paper and the degree of disorder is as high as 0.94, outperforming the existing representative methods. Additionally, the correlation between the measurement results of this method and the degree of simplification reaches 1, indicating that the variation range of the measured values is more consistent with the cognitive assumptions based on artificial simulations compared to the existing methods. The experimental results show that the method proposed in this paper is an effective metric approach for representing spatial information in area maps.
... Specifically, for the aligned condition, the color and location sequence share a common spatial trajectory, i.e., separated by the same distance between successive items between maps ( Figure 1B ), whereas for the misaligned condition, they have distinct relative trajectories ( Figure 1C ). We hypothesize that humans would naturally detect and combine the structure shared by the two sequences to facilitate memory formation, even though it is unsupervised and non-mandatory, as proposed by efficient coding theory (Attneave, 1954 ). ...
... Finally, our study is also different from recent works on structure learning and generalization (Dekker et al., 2022 ;Garvert et al., 2017 ;Liu, Mattar, et al., 2021 ;Ren et al., 2022 ;Schapiro et al., 2013 ), as our task does not involve pre-exposure training or task-related rewards. The fact that without task requirement, participants still spontaneously extracted underlying common structure and leveraged it to organize multiple item storage reflects the intelligence of our brain to achieve efficient information coding (Attneave, 1954 ). Indeed, the common structure we manipulated here is inspired by the theories of cognitive map ( domains follows similar computational principles as in spatial domains. ...
Daily experiences often involve the processing of multiple sequences, such as speech processing and spatial navigation, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences, one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between the reproduced color and location sequences. Interestingly, color sequence undergoes spontaneous forward neural replay when recalling trajectory-consistent location sequence. These results reveal that shared common structure is spontaneously leveraged to integrate and facilitate WM of multiple sequences through neural replay and imply a role of common cognitive map in efficient information organization in WM.
... Hochberg and McAIister (1953) and Hochberg and Brooks (1960) used factors such as the total number of interior angles and the total number of line segments to analyze which two-dimensional figures give the impression of representing three-dimensional objects. Another general measure falls under the category of information theory (Attneave, 1954(Attneave, , 1959Fitts, 1956;Garner, 1962Garner, ,1974Klemmer, 1963;Quastler, 1955). This measure says that the uncertainty of a figure is a measure of its complexity. ...
... For example, when interpreting Figure 6A, which interpretation has better continuation, Figure 6B or 6C, and why? When interpreting Figure 6A as 4 See Attneave (1954) for a related idea. 5 In this sense, a corner seems to be equivalent to an L-junction. ...
In figure segregation that may accompany figural completion, only 1 interpretation is dominant for some figures, whereas several different interpretations can be selected almost equally for other figures. This article addresses 2 problems in explaining this fact: (a) how to define quantitative measures for features (including gestalt principles of grouping) and (b) how to estimate the probability of each interpretation on the basis of these definitions. Quantitative definitions are given to 7 features involved in figure segregation and completion: the relative number of corners, good continuation, symmetry, curvature constancy, convexity, coincidence, and similarity. Each feature's value can be calculated from the total curvature function of the contour. The probability of each interpretation drawn by the participants is calculated by applying linear multiple regression analysis. Experimental results indicate that the proposed method can estimate human performance on a figure segregation and figural completion task fairly well.
... Importantly, contour junctions are nonaccidental properties that convey information about spatial relationships of objects and surfaces in 3D space. These features help people recognize shapes presented in varying orientations and under perceptually ambiguous situations, and they even underlie our ability to accurately categorize objects and scenes (Attneave, 1954;Cavanagh, 2005;Walther & Shen, 2014;Wilder et al., 2019). Viewpoint-invariant visual features are represented along the ventral visual stream and shown to play an important role in detecting shape skeletons and aesthetic qualities (Ayzenberg et al., 2022;Sun & Firestone, 2021), as well as affective responses to contour (Bar & Neta, 2006;Damiano et al., 2021;Vartanian et al., 2013). ...
... By contrast, natural scene categories are liked more as photographs than as line drawings, presumably because the decrease in complexity in the drawings moves them away from the optimal arousal point. Similarly, perceptual grouping cues such as symmetry that increase information gain are associated with aesthetic responses (Arnheim, 1974;Attneave, 1954;Bertamini et al., 1997;Van de Cruys & Wagemans, 2011;Koffka, 1935;Palmer, 1992;Wagemans, 1993) and have been found to contribute to aesthetic judgments to some degree in our study. Given the facilitative effect of prototypicality on aesthetic valuations across a range of stimuli (Brielmann & Pelli, 2018;Martindale & Moore, 1988;Palmer et al., 2013), we propose that visual features that enhance our perception of aesthetic value are likely also involved in category learning. ...
To what extent do aesthetic experiences arise from the human ability to perceive and extract meaning from visual features? Ordinary scenes, such as a beach sunset, can elicit a sense of beauty in most observers. Although it appears that aesthetic responses can be shared among humans, little is known about the cognitive mechanisms that underlie this phenomenon. We developed a contour model of aesthetics that assigns values to visual properties in scenes, allowing us to predict aesthetic responses in adults from around the world. Through a series of experiments, we manipulate contours to increase or decrease aesthetic value while preserving scene semantic identity. Contour manipulations directly shift subjective aesthetic judgments. This provides the first experimental evidence for a causal relationship between contour properties and aesthetic valuation. Our findings support the notion that visual regularities underlie the human capacity to derive pleasure from visual information.
... Nevertheless, with~67% of all recorded cells, the numerical abundance of chick colour-opponent OnOff RGCs is superficially at odds with efficient coding theory [59][60][61] . Spectral variance makes up only a small fraction of information in natural scenes, which are dominated by achromatic contrasts 5 . ...
... The resulting 23 'scores' matrices were then concatenated into a single matrix ready for clustering. The following numbers of principal components were used -CS: 24 components in total (3 R components, 3 Y components, 4 G components, 5 C components, 2 B components and 7 U components); WS: 61 components in total ( [4,4,5,6,6,7,7,7,7,8] [100, 90,80,70,60,50,40,30,20,10]% contrast components); Chirp: 47 components; SK: 8 components in total (2 R components, 2 G components, 2 C components and 2 B components), giving a grand total of 140 PCA components. ...
In vertebrate vision, early retinal circuits divide incoming visual information into functionally opposite elementary signals: On and Off, transient and sustained, chromatic and achromatic. Together these signals can yield an efficient representation of the scene for transmission to the brain via the optic nerve. However, this long-standing interpretation of retinal function is based on mammals, and it is unclear whether this functional arrangement is common to all vertebrates. Here we show that male poultry chicks use a fundamentally different strategy to communicate information from the eye to the brain. Rather than using functionally opposite pairs of retinal output channels, chicks encode the polarity, timing, and spectral composition of visual stimuli in a highly correlated manner: fast achromatic information is encoded by Off-circuits, and slow chromatic information overwhelmingly by On-circuits. Moreover, most retinal output channels combine On- and Off-circuits to simultaneously encode, or multiplex, both achromatic and chromatic information. Our results from birds conform to evidence from fish, amphibians, and reptiles which retain the full ancestral complement of four spectral types of cone photoreceptors.
... A long history of research successfully explains how the brain represents sensory stimuli within constraints of limited coding resources. Many of these addressed sensory coding [17,18,19,20,21,22]. A few considered uncertainty explicitly [23,24], and one addressed efficient coding in the service of control [25]. ...
We develop a version of stochastic control that accounts for computational costs of inference. Past studies identified efficient coding without control, or efficient control that neglects the cost of synthesizing information. Here we combine these concepts into a framework where agents rationally approximate inference for efficient control. Specifically, we study Linear Quadratic Gaussian (LQG) control with an added internal cost on the relative precision of the posterior probability over the world state. This creates a trade-off: an agent can obtain more utility overall by sacrificing some task performance, if doing so saves enough bits during inference. We discover that the rational strategy that solves the joint inference and control problem goes through phase transitions depending on the task demands, switching from a costly but optimal inference to a family of suboptimal inferences related by rotation transformations, each misestimate the stability of the world. In all cases, the agent moves more to think less. This work provides a foundation for a new type of rational computations that could be used by both brains and machines for efficient but computationally constrained control.
... In a different domain of neuroscience (sensory processing), it has been hypothesized that neuronal codes are optimized for efficiency, that is, to convey as much information as possible with given budgets for the number of neurons and the number of action potentials 4,5 . The efficient coding hypothesis has long been used to account for the response properties of sensory neurons [6][7][8] . ...
We use efficient coding principles borrowed from sensory neuroscience to derive the optimal neural population to encode a reward distribution. We show that the responses of dopaminergic reward prediction error neurons in mouse and macaque are similar to those of the efficient code in the following ways: the neurons have a broad distribution of midpoints covering the reward distribution; neurons with higher thresholds have higher gains, more convex tuning functions and lower slopes; and their slope is higher when the reward distribution is narrower. Furthermore, we derive learning rules that converge to the efficient code. The learning rule for the position of the neuron on the reward axis closely resembles distributional reinforcement learning. Thus, reward prediction error neuron responses may be optimized to broadcast an efficient reward signal, forming a connection between efficient coding and reinforcement learning, two of the most successful theories in computational neuroscience.
... Such complexities suggest that the conditions and objective functions defined here represent only a subset of the various influences shaping the acquisition of anatomically observed structures. The reduction of redundancy in information processing and the selective separation of trajectories, as observed in this study, align with characteristics reported in actual sensory systems 43,44,[49][50][51][52][53][54] . These phenomena underscore the efficiency of neural circuits in filtering and representing information in a manner that maximizes transmission while minimizing unnecessary overlap, resonating with the findings of Bell and others who have emphasized the importance of circuit structures that bolster information transmission within the visual system. ...
Network structures of the brain have wiring patterns specialized for specific functions. These patterns are partially determined genetically or evolutionarily based on the type of task or stimulus. These wiring patterns are important in information processing; however, their organizational principles are not fully understood. This study frames the maximization of information transmission alongside the reduction of maintenance costs as a multi-objective optimization challenge, utilizing information theory and evolutionary computing algorithms with an emphasis on the visual system. The goal is to understand the underlying principles of circuit formation by exploring the patterns of wiring and information processing. The study demonstrates that efficient information transmission necessitates sparse circuits with internal modular structures featuring distinct wiring patterns. Significant trade-offs underscore the necessity of balance in wiring pattern development. The dynamics of effective circuits exhibit moderate flexibility in response to stimuli, in line with observations from prior visual system studies. Maximizing information transfer may allow for the self-organization of information processing functions similar to actual biological circuits, without being limited by modality. This study offers insights into neuroscience and the potential to improve reservoir computing performance.
... Another longstanding idea in neuroscience is that brains are adapted to the statistics of the environment. Efficient coding states that brains represent as much information about the environment as possible while minimizing neural resource use [58,59]. ...
Variational autoencoders (VAE) employ Bayesian inference to interpret sensory inputs, mirroring processes that occur in primate vision across both ventral (Higgins et al., 2021) and dorsal (Vafaii et al., 2023) pathways. Despite their success, traditional VAEs rely on continuous latent variables, which deviates sharply from the discrete nature of biological neurons. Here, we developed the Poisson VAE (P-VAE), a novel architecture that combines principles of predictive coding with a VAE that encodes inputs into discrete spike counts. Combining Poisson-distributed latent variables with predictive coding introduces a metabolic cost term in the model loss function, suggesting a relationship with sparse coding which we verify empirically. Additionally, we analyze the geometry of learned representations, contrasting the P-VAE to alternative VAE models. We find that the P-VAEencodes its inputs in relatively higher dimensions, facilitating linear separability of categories in a downstream classification task with a much better (5x) sample efficiency. Our work provides an interpretable computational framework to study brain-like sensory processing and paves the way for a deeper understanding of perception as an inferential process.
... Other studies have shown how specific functional objectives can account for simple cell-like and in some cases complex cell-like properties. These objectives include efficient [62,63] and sparse coding [64][65][66], which assume that V1 efficiently encodes sensory input under certain metabolic constraints; independent component analysis [67], which stipulates that V1 reduces input redundancies by encoding independent features within sensory stimuli [68,69]; predictive coding, which also posits that neurons remove statistical redundancies but in this case by signalling unpredictable features within sensory input [70][71][72][73]; temporal coherence [74], slow subspace analysis [75] and slow feature analysis [76,77], which hypothesize that V1 encodes slowly-varying features within the stimulus; and the information bottleneck hypothesis [78], which suggests that V1 encodes sensory features with maximum mutual information about the future, whilst minimizing information about the past [79]. However, these studies all used non-spiking models and did not include Dale's law. ...
Neurons in primary visual cortex (V1) respond to natural scenes with a sparse and irregular spike code that is carefully balanced by an interplay between excitatory and inhibitory neurons. These neuron classes differ in their spike statistics, tuning preferences, connectivity statistics and temporal dynamics. To date, no single computational principle has been able to account for these properties. We developed a recurrently connected spiking network of excitatory and inhibitory units trained for efficient temporal prediction of natural movie clips. We found that the model exhibited simple and complex cell-like tuning, V1-like spike statistics, and, notably, also captured key differences between excitatory and inhibitory V1 neurons. This suggests that these properties collectively serve to facilitate efficient prediction of the sensory future.
... Indeed, it introduced the incipient concept of random variable (Chebyshev, 1867;Kolmogoroff, 1933) in psychophysics. Then, the development of computer science and information theory had major impact in perception studies, bringing concepts such as redundancy reduction and information maximization (Attneave, 1954;Barlow et al., 1961). More specifically, when applied to texture perception, these concepts led to Julesz' hypothesis that perception of textures is statistical (Victor et al., 2017) i.e. textures with similar statistics are indistinguishable. ...
The predictions of perceptual scales based on Fisher information metrics are tested in a series of experiments. This will allow us to go beyond perceptual distances and get closer to perceptual geometry.
... The foundational works of Attneave [1] and Barlow [2] suggest that neural structures on the visual pathway are optimized to accommodate the statistical properties of stimuli to which we are most commonly exposed (i.e., natural scenes). A thorough review of this concept is provided by Simoncelli & Olshausen [24]. ...
... Contrast is often measured using different mathematical measures and metrics [14], such as the Michelson contrast [24], RMS contrast, Weber contrast [25], Local contrast [26], and contrast-to-noise ratio [27]. However, these metrics are less susceptible to individual preferences as they are based on objective measures of image contrast rather than subjective perception. ...
This paper is an investigation in the field of personalized image quality assessment with the focus of studying individual contrast preferences for natural images. To achieve this objective, we conducted an in-lab experiment with 22 observers who assessed 499 natural images and collected their contrast level preferences. We used a three-alternative forced choice comparison approach coupled with a modified adaptive staircase algorithm to dynamically adjust the contrast for each new triplet. Through cluster analysis, we clustered observers into three groups based on their preferred contrast ranges: low contrast, natural contrast, and high contrast. This finding demonstrates the existence of individual variations in contrast preferences among observers. To facilitate further research in the field of personalized image quality assessment, we have created a database containing 10,978 original contrast level values preferred by observers, which is publicly available online.
... The concept of efficient coding [1], [2] in early biological sensory processing systems hypothesized that the internal representation of images in the human visual system is optimized to encode the visual information it processes efficiently. In other words, the brain effectively compresses visual information. ...
... To address our normative question regarding the optimal adjustment of responsiveness, we work within the framework of efficient coding theory (Attneave, 1954;Nadal and Parga, 1999;Laughlin, 1981;Barlow, 2012;Linsker, 1988;Ganguli and Simoncelli, 2014;Wei and Stocker, 2015;Atick and Redlich, 1990). Efficient coding theory begins by asking two questions: What is this neural system attempting to encode, and what features of biology constrain the quality of its encoding? ...
Neurons are typically sensitive to a small fraction of stimulus space. If the environment changes, making certain stimuli more prevalent, neurons sensitive to those stimuli would respond more often and therefore, if the stimulus-response mapping remains fixed, have a higher average firing rate. However, sufficiently prolonged exposure to the new environment typically causes such neurons to adapt by responding less vigorously. If adaptation consistently returns the average firing rate of neurons, or populations of similarly tuned neurons, to its value prior to environmental shift, it is termed firing rate homeostasis. In sensory cortex, adaptation is typically also stimulus specific, with neurons maintaining or even increasing their responsiveness to stimuli far from over-represented ones. Here, we present a normative explanation of firing rate homeostasis grounded in the efficient coding principle. Unlike previous theories based on efficient coding, we formulate the problem in a computation-agnostic manner, enabling our framework to apply far from the sensory periphery. We show that homeostasis can provide an optimal solution to a trade-off between coding fidelity and the metabolic cost of neural firing. We provide quantitative conditions necessary for the optimality of firing rate homeostasis, and predict how adaptation should deviate from homeostasis when these conditions are violated. Based on biological estimates of relevant parameters, we show that these conditions do hold in areas of cortex where homeostatic adaptation has been observed. Finally, we apply our framework to distributed distributional codes, a specific computational theory of neural representations serving Bayesian inference. We show that the resultant coding scheme can be accomplished by divisive normalisation with adaptive weights. We further demonstrate how homeostatic coding, coupled with such Bayesian neural representations, explains stimulus-specific adaptation, as observed, e.g., in the primary visual cortex.
... Dating back at least to the work of Attneave and Barlow, it has been proposed that the sensory coding scheme used by the brain is "efficient" (Attneave, 1954;Barlow, 1961). The relevant form of efficiency in this context refers to the physical resources used by the brain, such as the metabolic cost of spike generation, wiring costs, and so on. ...
The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision‐making, and motor control. This article provides an up‐to‐date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2–5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6–8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision‐making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.
... These findings reveal that an appropriate level of complexity and effective representation and layout lead to higher perceived information quality and visual informativeness. Numerous aspects of visual complexity have been identified, including the number and level of detail of objects (Berlyne, 1958;Palmer, 1999); the irregularity, dissimilarity, and asymmetry of objects (Attneave, 1954;Palmer, 1999); and the irregularity of arrangement (Donderi, 2006), and each aspect influences the level of complexity of a design. These aspect variables interact with each other and influence the overall level of complexity and uniqueness of a design. ...
Social media is a valuable tool that enables public health organizations to communicate effectively. To enhance the reach of health communication on social media, scholars have proposed that emoji be used to convey scientific information. The current study explored the influence of emoji on the effectiveness of health communication on social media. Automated content analysis revealed that the presence of emoji in online health information resulted in higher levels of social media engagement (SME) than the absence of emoji did. Additionally, a 2 (emoji: present versus absent) × 3 (visual complexity of information design: low versus medium versus high) online experiments revealed that the presence of emoji in health information sequentially increased perceived enjoyment and perceived interactivity, thereby promoting SME. However, this effect is influenced by the visual complexity of health information designs. The presence of emoji is only effective in increasing SME with health information presented using a design with low or medium visual complexity. This study provides theoretical and practical insights into visual health communication and health information design.
... Hochberg and McAlister. Hochberg and McAlister (1953) should be credited with initiating the efforts to place the notoriously qualitative notion of figural "goodness" on a quantitative footing (see also Attneave, 1954). These investigators used line drawings which could give rise to alternative perceptual organizations, and they assumed that the "better" of these organizations would be perceived more often or for a longer span of time than the alternatives. ...
We examine a number of investigations of perceptual economy or, more specifically, of minimum tendencies and minimum principles in the visual perception of form, depth, and motion. A minimum tendency is a psychophysical finding that perception tends toward simplicity, as measured in accordance with a specified metric. A minimum principle is a theoretical construct imputed to the visual system to explain minimum tendencies. After examining a number of studies of perceptual economy, we embark on a systematic analysis of this notion. We examine the notion that simple perceptual representations must be defined within the “geometric constraints” provided by proximal stimulation. We then take up metrics of simplicity. Any study of perceptual economy must use a metric of simplicity; the choice of metric may be seen as a matter of convention, or it may have deep theoretical and empirical implications. We evaluate several answers to the question of why the visual system might favor economical representations. Finally, we examine several accounts of the process for achieving perceptual economy, concluding that those which favor massively parallel processing are the most plausible.
... Orthogonal channeling could have the advantage of conciseness in neurally representing the informational features of the visual world that are important for purposive motor activity. (The role of visual codes in reducing redundancy has been discussed by Attneave, 1954, and Barlow, 1972 ...
Presents a theory of visual information processing that comprises 3 principal assumptions: (a) Retinal image information passes through parallel functional subunits called sets of filters, each of which is selectively sensitive to a different abstract feature of the retinal image. (b) Each of the abstract features discussed, including color, spatial frequency, motion, and depth, is a continuous variable and is analyzed into limited ranges by multiple, overlapping, selectively sensitive information filters. (c) Acute discrimination along a given continuous variable can be explained in terms of a subsequent processing stage at which the relative outputs of different overlapping filters are evaluated. Implications for skilled performance and neuro-ophthalmology include the following predictions: (1) In some situations (such as landing an airplane in fog), skilled eye–hand coordination involves only a few filters so that specific tests of the relevant filters may predict intersubject differences of performance. (2) Lack of selectivity or undue interaction between sets of filters may cause an individual's visual skills to deteriorate in a complex visual environment. (3) A clinical visual test that assesses a single filter may be diagnostically more sensitive than a test that incorporates several filters. (210 ref)
... There are, of course, other views about preference in ambiguous stimulus conditions. Chief among these is the notion of preference based on pragnanz, or simplicity, first propounded by Gestalt psychology (Koffka, 1935;Kopfermann, 1930) and later by those influenced by information theory (Attneave, 1954;Hochberg & Brooks, 1960;Hochberg & McAlister, 1953;Restle, 1979). In these later versions, the preference is always for the perceptual outcome that is most economically encoded. ...
It has been assumed that certain stimulus transformations lead directly to depth effects, that is, that such transformations are the necessary and sufficient conditions for kinetically generated depth perception. An alternative is to view such perception as the preferred solution to the problem posed by the transforming stimulus as to what event in the world is producing that transformation. In 3 experiments (80 Ss) it is shown that when other solutions are supportable by the stimulus, those same transformations will no longer lead to depth perception. These other solutions become preferred on the basis of rejection of certain coincidental features of the stimulus that otherwise would have to be accepted were the kinetic depth solution to be maintained. Findings are interpreted as challenging any theory that perception is simply the direct result of stimulation or of extraction of stimulus information and as supporting the Helmholtzian (H. Helmholtz, 1866) rule of perception as a construction of the most reasonable representation. (21 ref)
... It notes that the study of architecture is often based on perception theories. Gestalt Theory, Hebb Theory, and Information Theory have examined the human's visual perception of the environment (Alexander, 1959;Attneave, 1954;Hebb, 1949). ...
Objects: The purpose of the research is to obtain a general viewpoint of the quantity and frequency of visual factors in high-rise facades in Tehran, which have neuropsychological effects on the viewer.
Methods: The applied study was done with the quantitative method and through field study with photography. The images were prepared based on the criteria extracted from the background of the research. All images were quantitatively evaluated, and statistical analyses based on images were performed in Excel and SPSS software.
Findings: Investigating the sources related to design with a neuropsychological approach shows that these five factors include: daylight (level of opening), color combination (light and dark color), geometry (curved lines), complexity, and vegetation are more effective than other visual factors in enhancing the wellbeing of the residents in a building.
Results: Among the mentioned factors, "vegetation" with a frequency (of 2.6%) and "curved lines" with a frequency (of 7%) are among the least ranked factors in the facade of high-rise residential buildings in Tehran. This result emphasizes the necessity of paying attention to the use of vegetation and curved lines in new building designs.
... At the population level, neurons also adapt by reducing correlations between their responses [3; 4]. These adjustments enable the neurons to maximize the information that they transmit by utilizing their entire dynamic range and reducing redundancies in their representations [5][6][7][8]. A natural normative interpretation of these transformations is adaptive whitening, a context-dependent linear transformation of the sensory inputs yielding responses that have unit variance and are uncorrelated. ...
Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between their responses. Together, these transformations may be viewed as an adaptive form of statistical whitening. Existing mechanistic models of adaptive whitening exclusively use either synaptic plasticity or gain modulation as the biological substrate for adaptation; however, on their own, each of these models has significant limitations. In this work, we unify these approaches in a normative multi-timescale mechanistic model that adaptively whitens its responses with complementary computational roles for synaptic plasticity and gain modulation. Gains are modified on a fast timescale to adapt to the current statistical context, whereas synapses are modified on a slow timescale to learn structural properties of the input statistics that are invariant across contexts. Our model is derived from a novel multi-timescale whitening objective that factorizes the inverse whitening matrix into basis vectors, which correspond to synaptic weights, and a diagonal matrix, which corresponds to neuronal gains. We test our model on synthetic and natural datasets and find that the synapses learn optimal configurations over long timescales that enable the circuit to adaptively whiten neural responses on short timescales exclusively using gain modulation.
... The suitability of the periodic contour modifications for visual encoding is motivated by the findings of the existing psychophysical studies. Early research on visual perception stresses the role of contours as regions of a high information concentration, especially in the peaks of curvature [2], i.e., wave's peaks. Subsequently, the human discrimination performance related to the contours of observed visual elements has been investigated in several studies, the most of which are limited to regular circular shapes. ...
Metaphoric glyphs are intuitively related to the underlying problem domain and enhance the readability and learnability of the resulting visualization. Their construction, however, implies an appropriate modification of the base icon, which is a predominantly manual process. In this paper, we introduce the parametric contour-based modification (PACEMOD) approach that lays the foundations of automated, controllable icon manipulations. Technically, the PACEMOD parametric representation utilizes diffusion curves, enriched with new degrees of freedom in arc-length parameterization, which allows for manipulation of the icon contours’ geometry and the related color attributes. Moreover, we propose an implementation of our generic approach for a specific, automated design of metaphoric glyphs, based on periodic, wave-like contour modifications. Finally, the practicality of such periodic contour modifications is demonstrated by two visualization examples, which comprise uncertainty visualization of a rain forecast and gradient glyphs applied to COVID-19 data. In summary, with the PACEMOD approach we introduce an instrument that facilitates a user-centered design of metaphoric glyphs and provides a generic basis for potential further implementations according to specific applications.
Inference-based decision-making, which underlies a broad range of behavioral tasks, is typically studied using a small number of handcrafted models. We instead enumerate a complete ensemble of strategies that could be used to effectively, but not necessarily optimally, solve a dynamic foraging task. Each strategy is expressed as a behavioral “program” that uses a limited number of internal states to specify actions conditioned on past observations. We show that the ensemble of strategies is enormous—comprising a quarter million programs with up to five internal states—but can nevertheless be understood in terms of algorithmic “mutations” that alter the structure of individual programs. We devise embedding algorithms that reveal how mutations away from a Bayesian-like strategy can diversify behavior while preserving performance, and we construct a compositional description to link low-dimensional changes in algorithmic structure with high-dimensional changes in behavior. Together, this work provides an alternative approach for understanding individual variability in behavior across animals and tasks.
Normative models of synaptic plasticity use computational rationales to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work in this realm, but experimental confirmation remains limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata that, when satisfied, are designed to ensure that a given model demonstrates a clear link between plasticity and adaptive behavior, is consistent with known biological evidence about neural plasticity and yields specific testable predictions. As a prototype, we include a detailed analysis of the REINFORCE algorithm. We also discuss how new models have begun to improve on the identified criteria and suggest avenues for further development. Overall, we provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable.
Introduction
A remarkable phenomenon in perception is that the visual system spontaneously organizes sets of discrete elements into abstract shape representations. We studied perceptual performance with dot displays to discover what spatial relationships support shape perception.
Methods
In Experiment 1, we tested conditions that lead dot arrays to be perceived as smooth contours vs. having vertices. We found that the perception of a smooth contour vs. a vertex was influenced by spatial relations between dots beyond the three points that define the angle of the point in question. However, there appeared to be a hard boundary around 90° such that any angle 90° or less was perceived as a vertex regardless of the spatial relations of ancillary dots. We hypothesized that dot arrays whose triplets were perceived as smooth curves would be more readily perceived as a unitary object because they can be encoded more economically. In Experiment 2, we generated dot arrays with and without such “vertex triplets” and compared participants’ phenomenological reports of a unified shape with smooth curves vs. shapes with angular corners. Observers gave higher shape ratings for dot arrays from curvilinear shapes. In Experiment 3, we tested shape encoding using a mental rotation task. Participants judged whether two dot arrays were the same or different at five angular differences. Subjects responded reliably faster for displays without vertex triplets, suggesting economical encoding of smooth displays. We followed this up in Experiment 4 using a visual search task. Shapes with and without vertex triplets were embedded in arrays with 25 distractor dots. Participants were asked to detect which display in a 2IFC paradigm contained a shape against a distractor with random dots. Performance was better when the dots were sampled from a smooth shape than when they were sampled from a shape with vertex triplets.
Results and discussion
These results suggest that the visual system processes dot arrangements as coherent shapes automatically using precise smoothness constraints. This ability may be a consequence of processes that extract curvature in defining object shape and is consistent with recent theory and evidence suggesting that 2D contour representations are composed of constant curvature primitives.
Object coding in primate ventral pathway cortex progresses in sparseness/compression/efficiency, from many orientation signals in V1, to fewer 2D/3D part signals in V4, to still fewer multi-part configuration signals in AIT (anterior inferotemporal cortex). This progression could lead to individual neurons exclusively selective for unique objects, the sparsest code for identity, especially for highly familiar, important objects. To test this, we trained macaque monkeys to discriminate 8 simple letter-like shapes in a match-to-sample task, a design in which one-to-one coding of letters by neurons could streamline behavior. Performance increased from chance to >80% correct over a period of weeks, after which AIT neurons showed clear learning effects, with increased selectivity for multi-part configurations within the trained alphabet shapes. But these neurons were not exclusively tuned for unique letters based on training, since their responsiveness generalized to different, non-trained shapes containing the same configurations. This multi-part configuration coding limit in AIT is not maximally sparse, but it could explain the robustness of primate vision to partial object occlusion, which is common in the natural world and problematic for computer vision. Multi-part configurations are highly diagnostic of identity, and neural signals for various partial object structures can provide different but equally sufficient evidence for whole object identity across most occlusion conditions.
A method is presented that uses a diffusion-like process to describe the shape of a region. Convexity is not required, the descriptor is invariant under several common transformations, is applicable in the n-dimensional case, and is easy to compute.
The independent effects of short-and long-term experiences on visual perception have been discussed for decades. However, no study has investigated whether and how these experiences simultaneously affect our visual perception. To address this question, we asked participants to estimate their self-motion directions (i.e., headings) simulated from optic flow, in which a long-term experience learned in everyday life (i.e., straightforward motion being more common than lateral motion) plays an important role. The headings were selected from three distributions that resembled a peak, a hill, and a flat line, creating different short-term experiences. Importantly, the proportions of headings deviating from the straightforward motion gradually increased in the peak, hill, and flat distributions, leading to a greater conflict between long-and short-term experiences. The results showed that participants biased their heading estimates towards the straight-ahead direction and previously seen headings, which increased with the growing experience conflict. This suggests that both long-and short-term experiences simultaneously affect visual perception. Finally, we developed two Bayesian models (Model 1 vs. Model 2) based on two assumptions that the experience conflict altered the likelihood distribution of sensory representation or the motor response system. The results showed that both models accurately predicted participants' estimation biases. However, Model 1 predicted a higher variance of serial dependence compared to Model 2, while Model 2 predicted a higher variance of the bias towards the straight-ahead direction compared to Model 1. This suggests that the experience conflict can influence visual perception by affecting both sensory and motor response systems. Taken together, the current study systematically revealed the effects of long-and short-term experiences on visual perception and the underlying Bayesian processing mechanisms.
The cortical visual area, V4, has been considered to code contours that contribute to the intermediate-level representation of objects. The neural responses to the complex contour features intrinsic to natural contours are expected to clarify the essence of the representation. To approach the cortical coding of natural contours, we investigated the simultaneous coding of multiple contour features in monkey ( Macaca fuscata ) V4 neurons and their population-level representation. A substantial number of neurons showed significant tuning for two or more features such as curvature and closure, indicating that a substantial number of V4 neurons simultaneously code multiple contour features. A large portion of the neurons responded vigorously to acutely curved contours that surrounded the center of classical receptive field, suggesting that V4 neurons tend to code prominent features of object contours. The analysis of mutual information (MI) between the neural responses and each contour feature showed that most neurons exhibited similar magnitudes for each type of MI, indicating that many neurons showing the responses depended on multiple contour features. We next examined the population-level representation by using multidimensional scaling analysis. The neural preferences to the multiple contour features and that to natural stimuli compared with silhouette stimuli increased along with the primary and secondary axes, respectively, indicating the contribution of the multiple contour features and surface textures in the population responses. Our analyses suggested that V4 neurons simultaneously code multiple contour features in natural images and represent contour and surface properties in population.
principalmente auditivo, sonoro. Este volumen se concentra en el corpus de las obras que Beckett escribió para la radio entre 1956 y 1961, las revisa a profundidad, las analiza y las pone en el contexto de su obra para medios electrónicos. La autora acude a la vasta literatura en lengua inglesa sobre el tema de las tecnologías del sonido, el arte electrónico, también aquella que dedica su aten ción a la obra que Beckett produce en los estudios de grabación de la British Broad casting Corporation y de Radio Télévision Française desde 1957 y hasta mediados de los setenta, así como a la producción que Everett C. Frost realiza en la radio pú blica estadounidense a finales de los ochenta. Sánchez Cardona también integra a este corpus coclear aquellos textos concebidos para otros lenguajes y que encontra ron una de sus formas últimas en la producción sonora. Lejos de ser consideradas constructos marginales, las seis obras para el medio radiofónico que se analizan en este documento, han sido reconocidas como fundamentales en la formulación de un corpus en el que las posibilidades de la voz, escindida de la fuente emisora-gra cias a las tecnologías del sonido-, constituyó un punto de no retorno para toda la obra de Beckett a partir de 1962. De ahí la pertinencia de este acercamiento. Los documentos del Departamento de Artes y Hu ma ni dades (dah) exploran las prácticas, metodologías, historias, teorías, curadurías y arqueologías del arte digital. Estos documentos buscan aportar elementos a la arena de discusión en torno a las humanidades y las artes digi tales. Escritos y/o coordina dos por inves tigadores y creadores del dah, cada uno de estos títu los explora distintas vías del arte de nuestro tiempo. DOCUMENTOS DOCUMENTOS
An abundant literature reports on ‘sequential effects’ observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume non-stationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from 0.05 to 0.95. Although in this task the environment is static and the Bayesian belief converges, subjects’ predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this ‘attractive effect’, but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a ‘repulsive effect’, observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.
How each individual selects others within a group to interact with based on visual perception is a fundamental process underlying various collective motion behaviors. This process, however, remains poorly understood theoretically so far, partially due to the challenges of modeling neurological perception systems. Here, the classic zoom-lens visual attention model is introduced into collective perception, leading to a simple attention-based model that merely has a single parameter. Numerical experiments reveal that the proposed model offers a unified mechanism for collective perception, as reflected in three aspects. First, this model describes both conspecific (e.g., flocking) and heterospecific (e.g., predator-prey) collective behaviors. Second, this model unifies the well-known topological and visibility models in the context of neighbor selection in conspecific groups. Third, in the context of prey selection in heterospecific groups, the model can simultaneously replicate the well-known confusion and oddity effects. All these suggest the fundamental roles of visual attention underlying a diverse array of collective motion behaviors.
We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian hierarchical grouping (BHG). In BHG, we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters of the mixture components that generated the image, including estimating which image elements are “owned” by which objects. We present a tractable implementation of the framework, based on the hierarchical clustering approach of Heller and Ghahramani (2005). We illustrate it with examples drawn from a number of classical perceptual grouping problems, including dot clustering, contour integration, and part decomposition. Our approach yields an intuitive hierarchical representation of image elements, giving an explicit decomposition of the image into mixture components, along with estimates of the probability of various candidate decompositions. We show that BHG accounts well for a diverse range of empirical data drawn from the literature. Because BHG provides a principled quantification of the plausibility of grouping interpretations over a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz.
ELife digest
When light enters the eyes, it is focused onto the retina, a thin layer of brain tissue at the back of the eye. The retina converts light information into electrical signals that are transmitted to the rest of the brain to perceive vision. Unlike the rest of the brain, this light-processing tissue can continue working even when removed from an animal, making it easier for scientists to study how the retina works. This has helped it become one of the best-understood parts of the brain.
Most knowledge of retinal signal processing comes from studies of isolated retinas. However, it was still unclear if these samples behave the same way as they do in live animals, and whether findings in isolated retinas apply to natural visual processing in an awake state.
To determine this, Boissonnet et al. compared the visual responses of the retina in awake mice, anesthetised mice and when isolated from mice. Measurements of retinal electrical signals showed that awake mice responded to light substantially more quickly and strongly than the others. Computational analysis suggested that the amount of information carried to the brain was largely comparable across the different subjects, but the retina in awake mice used more energy.
The findings indicate that further studies are needed to better understand how the retina processes visual information in awake animals, rather than just in isolated conditions. Progressing this understanding could ultimately help to develop prosthetic devices that can act as a retina in the future.
Until recently, the transformational approach provided the only available formal analysis of visual regularities like repetition and mirror symmetry. This theoretical study presents a new analysis, based on the recently developed concept of holographic regularity. This concept applies to the intrinsic character of regularity and specifies the unique formal status of perceptually relevant regularities. The crucial point is that the two analyses imply the same structure for repetition but a different structure for mirror symmetry. Transformationally, mirror symmetry is an all-or-nothing property, whereas holographically, it is a graded property. This difference pervades the understanding of both perfect regularities and perturbed regularities. Whereas the transformational approach explains hardly any goodness phenomenon, the holographic approach explains a wide variety of goodness phenomena in a coherent way that is ecologically plausible as well.
Compared, in a divided attention situation, the processing-capacity and arousal-level effects on choice between computer-generated "melodies" differing in complexity. 100 female college students were exposed to either aversive (97 db, 350-Hz square wave) or mild (56 db) auditory stimulation prior to blocks of trials involving choice between simple (4.00 bits/tone) and complex (9.17 bits/tone) melodies. Concurrently with the processing of the chosen melody on each trial, Ss either had no task at all or worked on 1 of 4 tasks that systematically differed in (a) the processing effort demanded and (b) the likelihood of an arousal-level increase due to conceptual conflict and/or physical exertion. Measures were obtained of changes in choice and cardiac arousal due both to auditory stimulation and tasks. The processing-capacity factors influenced choice independently of the arousal-level fluctuations due to tasks. It is suggested that an arousal-level increase (due to stimulation), even when relevant for choice, may be mediated by a reduction in processing capacity. (44 ref)
Symmetry has long been viewed as a feature of objects that facilitates ease of perception. Three experiments investigated 4- to 5-month-old infants’ detection and processing of vertical symmetry, oblique symmetry, and asymmetry in novel patterns and faces. In Experiment 1, infants showed the fewest shifts in visual fixations to vertical symmetry in patterns and faces, supporting the view that vertical symmetry is processed more efficiently than oblique symmetry or asymmetry. In Experiment 2, stimulus presentation disallowed more than a single visual fixation, and infants looked longer at a face that is vertically symmetrical compared to obliquely symmetrical or asymmetrical, and they looked equally to patterns regardless of symmetry. In Experiment 3, where pattern exposures were prolonged and inverted faces viewed, infants discriminated vertical symmetry in patterns but lost the advantage with vertical symmetry in faces. Thus, symmetry in patterns requires more processing time from infants, and inverting the face costs infants the normal perceptual advantage of symmetry, even though components of the face remain symmetrical. These findings suggest that infants are prepared to exploit symmetry in their everyday perceptual worlds.
In the Gerbino illusion a regular but coincidentally occluded polygon appears distorted. Such a display represents a critical condition for amodal completion (AC), in which the smooth continuations of contour fragments—however small—conflict with their possible monotonic interpolation. Smoothness and monotonicity are considered the fundamental constraints of AC at the contour level. To account for the Gerbino illusion we contrasted two models derived from alternative AC frameworks: visual interpolation , based on the literal representation of contour fragments, vs. visual approximation , which tolerates a small misorientation of contour fragments, compatible with smoothness and monotonicity constraints. To measure the perceived misorientation of sides of coincidentally occluded angles we introduced a novel technique for analyzing data from a multiple probe adjustment task. An unsupervised cluster analysis of errors in extrapolation and tilt adjustments revealed that the distortion observed in the Gerbino illusion is consistent with visual approximation and, in particular, with the concatenation of misoriented and locally shrinked amodally completed angles. Implications of our technique and obtained results shed new light on visual completion processes.
This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition and various cognitive functionalities through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Given the complexity of the human mind and its manifestation in behavioral flexibility, process-based computational models may be necessary to explicate and elucidate the intricate details of the mind. The key to understanding cognitive processes is often in fine details. Computational models provide algorithmic specificity: detailed, exactly specified, and carefully thought-out steps, arranged in precise yet flexible sequences. These models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many examples.
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