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)

:Reasons-responsiveness theories of moral responsibility are currently among the most popular. Here, I present the fallibility paradox, a novel challenge to these views. The paradox involves an agent who is performing a somewhat demanding psychological task across an extended sequence of trials and who is deeply committed to doing her very best at this task. Her action-issuing psychological processes are outstandingly reliable, so she meets the criterion of being reasons-responsive on every single trial. But she is human after all, so (...) it is inevitable that she will make rare errors. The reasons-responsiveness view, it is claimed, is forced to reach a highly counterintuitive conclusion: she is morally responsible for these rare errors, even though making rare errors is something she is powerless to prevent. I review various replies that a reasons-responsiveness theorist might offer, arguing that none of these replies adequately addresses the challenge. (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)

It is thought that just as hunger itself, the expectancy to eat impacts attention and cognitive control toward food stimuli, but this theory has not been extensively explored at a behavioral level. In order to study the effect of expectancy to eat on attentional and cognitive control mechanisms, 63 healthy fasting participants were presented with an affective priming spatial compatibility Simon task that included both food and object distracters. The participants were randomly assigned to two groups: an “immediate expectancy” group (...) made up of participants who expected to eat immediately after the task and a “delayed expectancy” cohort made up of individuals who expected to eat a few hours later. Slower reaction times toward the food and non-food distracters and a more pronounced effect on the RTs in the incompatible condition [i.e., the Simon effect ] were noted in both groups. The effect of the food and non-food distracters on the RTs was more pronounced in the immediate with respect to the delayed expectancy group. The magnitude of the SE for the food and the non-food distracters was also greater in the immediate with respect to the delayed expectancy group. These results seem to indicate that when the expectancy to eat is short, the RTs are delayed, and the SE is more pronounced when food and non-food distracters are presented. Instead, when the expectancy to eat is more distant, the distracters have less of an effect on the RTs and the correspondence effect is smaller. Our results suggest that the expectancy to eat can modulate both attention orienting and cognitive control mechanisms in healthy fasting individuals when distracting details are competing with information processing during goal directed behavior. (shrink)

Oversimplified conceptions of cognitive neuroscience regard the goal of this discipline as the localization of previously discovered and validated cognitive processes. Research however is showing how brain data goes far beyond this translation role, as it can be used to help in explaining human cognition. Knowing about the brain is useful in building and redefining taxonomies of the mind and also in describing the mechanisms by which cognitive phenomena proceed. The present paper takes the cognitive system of attention as a (...) model research field to exemplify how biological knowledge can be used to advance the psychological theories explaining mental phenomena. (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 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)

In this prcis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (...) (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed-forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed. (shrink)

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)

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)

This article examines the anatomy and circuitry of skills that, like reading, calculating, recognizing, or remembering, are common abilities of humans. While the anatomical areas active are unique to each skill there are features common to all tasks. For example, all skills produce activation of a small number of widely separated neural areas that appear necessary to perform the task. These neural areas relate to internal codes that may not be observed by any external behavior nor be reportable by the (...) performer. There is considerable plasticity to the performance of skills. Task components can be given priority through attention, which serves to increase activation of the relevant brain areas. Attention can also cause reactivation of sensory areas driven by input, but usually only after a delay. The threshold for activation for any area may be temporarily reduced by prior activation . Skill components requiring attention tend to cause interference resulting in the dual tasks effects and unified focus of attention described in many cognitive studies. Practice may change the size or number of brain areas involved and alter the pathways used by the skill. By combining cognitive and anatomical analyses, a more general picture of the nature of skill emerges. (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.

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)

The effects of lexical context on phonological processing are pervasive and there have been indications that such effects may be modulated by attention. However, attentional modulation in speech processing is neither well documented nor well understood. Experiment 1 demonstrated attentional modulation of lexical facilitation of speech sound recognition when task and critical stimuli were identical across attention conditions. We propose modulation of lexical activation as a neurophysiologically plausible computational mechanism that can account for this type of modulation. Contrary to the (...) claims of critics, this mechanism can account for attentional modulation without violating the principle of interactive processing. Simulations of the interactive TRACE model extended to include two different ways of modulating lexical activation showed that each can account for attentional modulation of lexical feedback effects. Experiment 2 tested conflicting predictions from the two implementations and provided evidence that is consistent with bias input as the mechanism of attentional control of lexical activation. (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)

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)