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Perceptual learning

About: Perceptual learning is a(n) research topic. Over the lifetime, 3923 publication(s) have been published within this topic receiving 168939 citation(s).
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Journal ArticleDOI
TL;DR: Tested the 2-process theory of detection, search, and attention presented by the current authors (1977) in a series of experiments and demonstrated the qualitative difference between 2 modes of information processing: automatic detection and controlled search.
Abstract: Tested the 2-process theory of detection, search, and attention presented by the current authors (1977) in a series of experiments. The studies (a) demonstrate the qualitative difference between 2 modes of information processing: automatic detection and controlled search; (b) trace the course of the

6,868 citations


Journal ArticleDOI
TL;DR: A perceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal symbol systems and implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.
Abstract: Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

4,923 citations


Journal ArticleDOI
TL;DR: The aims of this article are to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective and to provide a principled way to understand many aspects of cortical organization and responses.
Abstract: This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain’s free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain’s attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.

3,035 citations


Journal ArticleDOI
TL;DR: A new framework for a more adequate theoretical treatment of perception and action planning is proposed, in which perceptual contents and action plans are coded in a common representational medium by feature codes with distal reference, showing that the main assumptions are well supported by the data.
Abstract: Traditional approaches to human information processing tend to deal with perception and action planning in isolation, so that an adequate account of the perception-action interface is still missing On the perceptual side, the dominant cognitive view largely underestimates, and thus fails to account for, the impact of action-related processes on both the processing of perceptual information and on perceptual learning On the action side, most approaches conceive of action planning as a mere continuation of stimulus processing, thus failing to account for the goal-directedness of even the simplest reaction in an experimental task We propose a new framework for a more adequate theoretical treatment of perception and action planning, in which perceptual contents and action plans are coded in a common representational medium by feature codes with distal reference Perceived events (perceptions) and to-be-produced events (actions) are equally represented by integrated, task-tuned networks of feature codes – cognitive structures we call event codes We give an overview of evidence from a wide variety of empirical domains, such as spatial stimulus-response compatibility, sensorimotor synchronization, and ideomotor action, showing that our main assumptions are well supported by the data

2,521 citations


Journal ArticleDOI
TL;DR: The experiments that are reported were designed to explore the relationship between the more aware autobiographical form of memory that is measured by a recognition memory test and the less aware form ofMemory that is expressed in perceptual learning.
Abstract: Although the majority of research on human memory has concentrated on a person's ability to recall or recognize items as having been presented in a particular situation, the effects of memory are also revealed in a person's performance of a perceptual task. Prior experience with material can make that material more easily identified or comprehended in perceptually difficult situations. Unlike with standard retention tests, effects of prior experience on a perceptual task do not logically require that a person be aware that he or she is remembering. Indeed, amnesic patients purportedly show effects of practice in their subsequent performance of a perceptual or motor task even though they profess that they do not remember having engaged in that prior experience. The experiments that are reported were designed to explore the relationship between the more aware autobiographical form of memory that is measured by a recognition memory test and the less aware form of memory that is expressed in perceptual learning. Comparisons of effects on perceptual learning and recognition memory reveal two classes of variables. Variables such as the level of processing of words during study influenced recognition memory, although they had no effect on subsequent perceptual recognition. A study presentation of a word had as large an effect on its later perceptual recognition when recognition memory performance was very poor as it did when recognition memory performance was near perfect. In contrast, variables such as the number and the spacing of repetitions produced parallel effects on perceptual recognition and recognition memory. Following Mandler and others, it is suggested that there are two bases for recognition memory. If an item is readily perceived so that it seems to "jump out" from the page, a person is likely to judge that he or she has previously seen the item in the experimental situation. Variables that influence ease of perceptual recognition, then, can also have an effect on recognition memory, so parallel effects are found. The second basis for recognition memory involves elaboration of a word's study context and depends on such factors as level of processing during study--factors that are not important for perceptual recognition of isolated words. Comparisons of perceptual recognition and recognition memory are shown to be useful for determining how a variable has its effect. Effects of study on perceptual recognition appear to be totally due to memory for physical or graphemic information. Results reported are also relevant to theories of perceptual learning. A single presentation of an item is shown to have large and long-lasting effects on its later perceptual recognition. At least partially, effects of study on perceptual recognition depend on the same variables as do effects on more standard memory tests.

2,503 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
2021107
2020133
2019151
2018138
2017173