This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called “deep networks”—systems that build on key ideas presented in the PDP volumes. (shrink)

We review how leaky competing accumulators (LCAs) can be used to model decision making in two‐alternative, forced‐choice tasks, and we show how they reduce to drift diffusion (DD) processes in special cases. As continuum limits of the sequential probability ratio test, DD processes are optimal in producing decisions of specified accuracy in the shortest possible time. Furthermore, the DD model can be used to derive a speed–accuracy trade‐off that optimizes reward rate for a restricted class of two alternative forced‐choice decision (...) tasks. We review findings that compare human performance with this benchmark, and we reveal both approximations to and deviations from optimality. We then discuss three potential sources of deviations from optimality at the psychological level—avoidance of errors, poor time estimation, and minimization of the cost of control—and review recent theoretical and empirical findings that address these possibilities. We also discuss the role of cognitive control in changing environments and in modulating exploitation and exploration. Finally, we consider physiological factors in which nonlinear dynamics may also contribute to deviations from optimality. (shrink)

The processes mediating dynamic and flexible responding to rapidly changing task-environments are not well understood. In the present research we employ a Stroop procedure to clarify the contribution of context-sensitive control processes to online performance. In prior work Stroop interference varied as a function of probe location context, with larger Stroop interference occurring for contexts associated with a high proportion of congruent items [Crump, M. J., Gong, Z., & Milliken, B. . The context-specific proportion congruent stroop effect: location as a (...) contextual cue. Psychonomic Bulletin & Review, 13, 316–321.] Here, we demonstrate that this effect does not depend on awareness of the context manipulation, but that it can depend on attention to the predictive context dimension, and on the relative salience of the target and predictive context dimensions. We discuss the implications of our results for current theories of cognitive control. (shrink)

We offer an ecological (Gibsonian) alternative to cognitive (im)penetrability. Whereas Pylyshyn explains cognitive (im)penetrability by focusing solely on computations carried out by the nervous system, according to the ecological approach the perceiver as a knowing agent influences the entire animal-environmental system: in the determination of what constitutes the environment (affordances), what constitutes information, what information is detected and, thus, what is perceived.

have recently provided a compelling demonstration of enhanced attentional control under post-hypnotic suggestion. Using the classic color-word interference paradigm, in which the task is to ignore a word and to name the color in which it is printed (e.g., RED in green, say ''green''), they gave a post-hypnotic instruction to participants that they would be unable to read. This eliminated Stroop interference in high suggestibility participants but did not alter interference in low suggestibility participants. replicated this pattern and further demonstrated (...) that it is not due to a visual strategy (such as blurring or looking at a different location). As a historical footnote, we describe a ''case study'' from 18 years ago in which we observed the same result using a hypnotic instruction to a single highly suggestible individual that he could not read. The elimination of Stroop interference has important implications for both the study of attention and the study of hypnosis. (shrink)

In the current paper, we articulate a theory to explain the phenomenology of mental effort. The theory provides a working definition of mental effort, explains in what sense mental effort is a limited resource, and specifies the factors that determine whether or not mental effort is experienced as aversive. The core of our theory is the conjecture that the sense of effort is the output of a cost-benefit analysis. This cost-benefit analysis employs heuristics to weigh the current and anticipated costs (...) of mental effort for a particular activity against the anticipated benefits. This provides a basis for spelling out testable predictions to structure future research on the phenomenology of mental effort. (shrink)

Conflicts between target and distraction can occur at the level of both stimulus and response processing. However, the neural oscillations underlying occurrence of the interference in different levels have not been understood well. Here, we reveal such a neural oscillation modulation by combining a 4:2 mapping design (two targets are mapped into one response key) with a practice paradigm (pretest, practice, and posttest) when healthy human participants were performing a novel color-word flanker task. Response time (RT) results revealed constant stimulus (...) conflict (SC, stimulus incongruent minus congruent, SI-CO) but increased response conflict (RC, response incongruent minus stimulus incongruent, RI-SI) with practice. Event-related potential (ERP) results demonstrated stable P3 amplitude differences for the SI-CO in the centro-parietal region across practice, which may reflect maintenance of the stimulus processing; and significantly larger P3 amplitudes in the same region for the RI relative to SI trial type in posttest, which may reflect inhibition of the distraction response. Further, neural oscillatory results showed that with practice, the lower alpha band in the frontal region and the upper alpha band in the occipital-parietal region distinguished between stimulus- and response-conflicts, respectively, suggesting that practice reduces the alertness (sensitiveness) of the brain to conflict occurrence, and enhances stimulus-response associations. (shrink)

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data (...) can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. (PsycINFO Database Record (c) 2012 APA, all rights reserved). (shrink)

Because the visual system cannot process all of the objects, colors, and features present in a visual scene, visual attention allows some visual stimuli to be selected and processed over others. Most research on visual attention has focused on spatial or location-based attention, in which the locations occupied by stimuli are selected for further processing. Recent research, however, has demonstrated the importance of objects in organizing (or segregating) visual scenes and guiding attentional selection. Because of the long history of spatial (...) attention research, theories of spatial attention are more mature than theories of other visual processes, such as object segregation and object attention. In the present paper, I outline a biased competition account of object segregation and attention, following similar accounts that have been developed for spatial attention (Desimone and Duncan, 1995). In my biased competition account, I seek to understand how some objects can be segregated and selected over other objects in a complex visual scene. Under this account, there are two sources of visual information that allow an object to be processed over other objects: bottom-up information carried by the physical stimulus and top-down information based on an observer's goals. I use the biased competition account to combine many diverse findings from the object segregation and attention literatures into a common framework. (shrink)

All forms of cognition, whether natural or artificial, are subject to constraints of their computing architecture. This assumption forms the tenet of virtually all general theories of cognition, including those deriving from bounded optimality and bounded rationality. In this letter, we highlight an unresolved puzzle related to this premise: what are these constraints, and why are cognitive architectures subject to cognitive constraints in the first place? First, we lay out some pieces along the puzzle edge, such as computational tradeoffs inherent (...) to neural architectures that give rise to rational bounds of cognition. We then outline critical next steps for characterizing cognitive bounds, proposing that some of these bounds can be subject to modification by cognition and, as such, are part of what is being optimized when cognitive agents decide how to allocate cognitive resources. We conclude that these emerging views may contribute to a more holistic perspective on the nature of cognitive bounds, as well as their alteration subject to cognition. (shrink)

Theories of language production propose that utterances are constructed by a mechanism that separates linguistic content from linguistic structure, Linguistic content is retrieved from the mental lexicon, and is then inserted into slots in linguistic structures or frames. Support for this kind of model at the phonological level comes from patterns of phonological speech errors. W present an alternative account of these patterns using a connectionist or parallel distributed proceesing (PDP) model that learns to produce sequences of phonological features. The (...) model's errors exhibit some of the properties of human speech errors, specifically, properties that have been attributed to the action of phonological rules, frames, or other structural generalizations. (shrink)

Complete URL to this document: http://srsc.ulb.ac.be/axcWWW/93-Principles.html.

The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based (...) on a temporal‐differences reward‐prediction learning mechanism. Unlike prior models of this type, the present model develops all of its internal representations via learning mechanisms as shaped by the demands of continuous periodic task switching. This advance opens up a new domain of research into the interactions between working memory task demands and the representations that develop to meet them. Results on a version of the Wisconsin card sorting task are presented for the full model and a number of comparison networks that test the importance of various model features. Furthermore, we show that a lesioned model produces perseverative errors like those seen in frontal patients. (shrink)

Aspects of the history of behavioural science are reviewed, pointing to its fragmented and faction-ridden nature. The emergence of evolutionary psychology (EP) is viewed in this context. With the help of a dual-layered model of behavioural control, the case is made for a more integrative perspective towards EP. The model's application to both behaviour and complex human information processing is described. Similarities in their control are noted. It is suggested that one layer of control (‘on-line’) corresponds to the encapsulated modules (...) of EP whereas the off-line controls provide the plasticity and flexibility suggested by its critics. (shrink)

This study compared professional and amateur classical musicians matched for age, gender, and education on reaction times during the Stroop color-word test, brainwaves during an auditory ERP task and during paired reaction-time tasks, responses on the Gibbs Sociomoral Reflection questionnaire, and self-reported frequencies of peak experiences. Professional musicians were characterized by: lower color-word interference effects , faster categorization of rare expected stimuli , and a trend for faster processing of rare unexpected stimuli , higher scores on the Sociomoral Reflection questionnaire, (...) and more frequent peak experiences during rest, tasks, and sleep. Both groups had high values on the Brain Integration Scale. These findings are interpreted in light of a Unified Theory of Performance, which posits that effectiveness in any area is influenced by one’s level of mind-brain development—emotional, cognitive, moral, ego and cortical development—with higher mind-brain development supporting greater effectiveness in any domain. (shrink)

Researchers studying complex cognition have grown increasingly interested in mapping symbolic cognitive architectures onto subsymbolic brain models. Such a mapping seems essential for understanding cognition under all but the most extreme viewpoints (namely, that cognition consists exclusively of digitally implemented rules; or instead, involves no rules whatsoever). Making this mapping reduces to specifying an interface between symbolic and subsymbolic descriptions of brain activity. To that end, we propose parameterization techniques for building cognitive models as programmable, structured, recurrent neural networks. Feedback (...) strength in these models determines whether their components implement classically subsymbolic neural network functions (e.g., pattern recognition), or instead, logical rules and digital memory. These techniques support the implementation of limited production systems. Though inherently sequential and symbolic, these neural production systems can exploit principles of parallel, analog processing from decision-making models in psychology and neuroscience to explain the effects of brain damage on problem solving behavior. (shrink)

In this study, we examined the effects of emotional experience, a relatively new dimension of emotional knowledge that gauges the ease with which words evoke emotional experience, on abstract word processing in the Stroop task. In order to test the context-dependency of these effects, we accentuated the saliency of this dimension in Experiment 1A by blocking the stimuli such that one block consisted of the stimuli with the highest emotional experience ratings and the other block consisted of the stimuli with (...) the lowest emotional experience ratings. We attenuated the saliency of this dimension in Experiment 1B by intermixing the stimuli. We observed slower color naming performance for words with higher emotional experience ratings only in Experiment 1A, suggesting that the dimension of emotional experience is an aspect of semantic representation for abstract words but that its influence can be modulated by context. We interpret these results more generally using Vigliocco, Meteyard, Andrews, and Kousta's framework of semantic representation, and more specifically using Cohen, Dunbar, and McClelland's model of Stroop task performance. (shrink)

The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based (...) on a temporal‐differences reward‐prediction learning mechanism. Unlike prior models of this type, the present model develops all of its internal representations via learning mechanisms as shaped by the demands of continuous periodic task switching. This advance opens up a new domain of research into the interactions between working memory task demands and the representations that develop to meet them. Results on a version of the Wisconsin card sorting task are presented for the full model and a number of comparison networks that test the importance of various model features. Furthermore, we show that a lesioned model produces perseverative errors like those seen in frontal patients. (shrink)

The commentaries reflect three core themes that pertain not just to our theory, but to the enterprise of connectionist modeling more generally. The first concerns the relationship between a cognitive theory and an implemented computer model. Specifically, how does one determine, when a model departs from the theory it exemplifies, whether the departure is a useful simplification or a critical flaw? We argue that the answer to this question depends partially upon the model's intended function, and we suggest that connectionist (...) models have important functions beyond the commonly accepted goals of fitting data and making predictions. The second theme concerns perceived in-principle limitations of the connectionist approach to cognition, and the specific concerns these perceived limitations raise for our theory. We argue that the approach is not in fact limited in the ways our critics suggest. One common misconception, that connectionist models cannot address abstract or relational structure, is corrected through new simulations showing directly that such structure can be captured. The third theme concerns the relationship between parallel distributed processing (PDP) models and structured probabilistic approaches. In this case we argue that there the difference between the approaches is not merely one of levels. Our PDP approach differs from structured statistical approaches at all of Marr's levels, including the characterization of the goals of cognitive computations, and of the representations and algorithms used. (shrink)

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what “mental processes” exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe (...) a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas, and outline how this project has the potential to drive novel discoveries about both mind and brain. (shrink)

Page's proposal to stipulate representations in which individual units correspond to meaningful entities is too unconstrained to support effective theorizing. An approach combining general computational principles with domain-specific assumptions, in which learning is used to discover representations that are effective in solving tasks, provides more insight into why cognitive and neural systems are organized the way they are.