Professor (By courtesy), Linguistics
Professor (By courtesy), Computer Science
Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Member, Wu Tsai Neurosciences Institute
Professor, Department of Psychology (2006 - Present)
Director, Center for Mind, Brain, and Computation (2006 - Present)
Honors & Awards
Distinguished Scientific Contribution Award, American Psychological Association (1996)
Member, National Academy of Sciences (2001-)
Symbolic Systems Program
Ph. D., University of Pennsylvania, Cognitive Psychology (1975)
Current Research and Scholarly Interests
My research addresses topics in perception and decision making; learning and memory; language and reading; semantic cognition; and cognitive development. I view cognition as emerging from distributed processing activity of neural populations, with learning occurring through the adaptation of connections among neurons. A new focus of research in the laboratory is mathematical cognition, with an emphasis on the learning and representation of mathematical concepts and relationships.
Please visit my web page for more information.
- Neural Network Models of Cognition
PSYCH 209 (Win)
Independent Studies (10)
- Directed Reading in Neurosciences
NEPR 299 (Aut, Win, Spr, Sum)
- Graduate Research
NEPR 399 (Aut, Win, Spr, Sum)
- Graduate Research
PSYCH 275 (Aut, Win, Spr, Sum)
- Independent Study
SYMSYS 196 (Win, Spr)
- Independent Study
SYMSYS 296 (Aut, Win, Spr)
- Master's Degree Project
SYMSYS 290 (Aut, Win, Spr)
- Practicum in Teaching
PSYCH 281 (Aut, Win, Spr)
- Reading and Special Work
PSYCH 194 (Aut, Win, Spr, Sum)
- Senior Honors Tutorial
SYMSYS 190 (Aut, Win, Spr)
- Special Laboratory Projects
PSYCH 195 (Aut, Win, Spr, Sum)
- Directed Reading in Neurosciences
Prior Year Courses
- Foundations of Cognition
PSYCH 205 (Spr)
- Neural Network Models of Cognition
PSYCH 209 (Win)
- Research Seminar: Mind, Brain, and Computation
PSYCH 373 (Aut, Win, Spr)
- Neural Network Models of Cognition
PSYCH 209 (Win)
- Research Seminar: Mind, Brain, and Computation
PSYCH 373 (Aut, Win, Spr)
- Foundations of Cognition
Graduate and Fellowship Programs
The dynamics of multimodal integration: The averaging diffusion model.
Psychonomic bulletin & review
We combine extant theories of evidence accumulation and multi-modal integration to develop an integrated framework for modeling multimodal integration as a process that unfolds in real time. Many studies have formulated sensory processing as a dynamic process where noisy samples of evidence are accumulated until a decision is made. However, these studies are often limited to a single sensory modality. Studies of multimodal stimulus integration have focused on how best to combine different sources of information to elicit a judgment. These studies are often limited to a single time point, typically after the integration process has occurred. We address these limitations by combining the two approaches. Experimentally, we present data that allow us to study the time course of evidence accumulation within each of the visual and auditory domains as well as in a bimodal condition. Theoretically, we develop a new Averaging Diffusion Model in which the decision variable is the mean rather than the sum of evidence samples and use it as a base for comparing three alternative models of multimodal integration, allowing us to assess the optimality of this integration. The outcome reveals rich individual differences in multimodal integration: while some subjects' data are consistent with adaptive optimal integration, reweighting sources of evidence as their relative reliability changes during evidence integration, others exhibit patterns inconsistent with optimality.
View details for DOI 10.3758/s13423-017-1255-2
View details for PubMedID 28275990
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
TRENDS IN COGNITIVE SCIENCES
2016; 20 (7): 512-534
We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure. Finally, we note the relevance of the theory to the design of artificial intelligent agents, highlighting connections between neuroscience and machine learning.
View details for DOI 10.1016/j.tics.2016.05.004
View details for Web of Science ID 000379106100007
View details for PubMedID 27315762
- Bayesian analysis of simulation-based models JOURNAL OF MATHEMATICAL PSYCHOLOGY 2016; 72: 191-199
- You shall know an object by the company it keeps: An investigation of semantic representations derived from object co-occurrence in visual scenes. Neuropsychologia 2015; 76: 52-61
Payoff Information Biases a Fast Guess Process in Perceptual Decision Making under Deadline Pressure: Evidence from Behavior, Evoked Potentials, and Quantitative Model Comparison.
journal of neuroscience
2015; 35 (31): 10989-11011
We used electroencephalography (EEG) and behavior to examine the role of payoff bias in a difficult two-alternative perceptual decision under deadline pressure in humans. The findings suggest that a fast guess process, biased by payoff and triggered by stimulus onset, occurred on a subset of trials and raced with an evidence accumulation process informed by stimulus information. On each trial, the participant judged whether a rectangle was shifted to the right or left and responded by squeezing a right- or left-hand dynamometer. The payoff for each alternative (which could be biased or unbiased) was signaled 1.5 s before stimulus onset. The choice response was assigned to the first hand reaching a squeeze force criterion and reaction time was defined as time to criterion. Consistent with a fast guess account, fast responses were strongly biased toward the higher-paying alternative and the EEG exhibited an abrupt rise in the lateralized readiness potential (LRP) on a subset of biased payoff trials contralateral to the higher-paying alternative ∼150 ms after stimulus onset and 50 ms before stimulus information influenced the LRP. This rise was associated with poststimulus dynamometer activity favoring the higher-paying alternative and predicted choice and response time. Quantitative modeling supported the fast guess account over accounts of payoff effects supported in other studies. Our findings, taken with previous studies, support the idea that payoff and prior probability manipulations produce flexible adaptations to task structure and do not reflect a fixed policy for the integration of payoff and stimulus information.Humans and other animals often face situations in which they must make choices based on uncertain sensory information together with information about expected outcomes (gains or losses) about each choice. We investigated how differences in payoffs between available alternatives affect neural activity, overt choice, and the timing of choice responses. In our experiment, in which participants were under strong time pressure, neural and behavioral findings together with model fitting suggested that our human participants often made a fast guess toward the higher reward rather than integrating stimulus and payoff information. Our findings, taken with findings from other studies, support the idea that payoff and prior probability manipulations produce flexible adaptations to task structure and do not reflect a fixed policy.
View details for DOI 10.1523/JNEUROSCI.0017-15.2015
View details for PubMedID 26245962
Connectionist perspectives on language learning, representation and processing.
Wiley interdisciplinary reviews. Cognitive science
2015; 6 (3): 235-247
The field of formal linguistics was founded on the premise that language is mentally represented as a deterministic symbolic grammar. While this approach has captured many important characteristics of the world's languages, it has also led to a tendency to focus theoretical questions on the correct formalization of grammatical rules while also de-emphasizing the role of learning and statistics in language development and processing. In this review we present a different approach to language research that has emerged from the parallel distributed processing or 'connectionist' enterprise. In the connectionist framework, mental operations are studied by simulating learning and processing within networks of artificial neurons. With that in mind, we discuss recent progress in connectionist models of auditory word recognition, reading, morphology, and syntactic processing. We argue that connectionist models can capture many important characteristics of how language is learned, represented, and processed, as well as providing new insights about the source of these behavioral patterns. Just as importantly, the networks naturally capture irregular (non-rule-like) patterns that are common within languages, something that has been difficult to reconcile with rule-based accounts of language without positing separate mechanisms for rules and exceptions. WIREs Cogn Sci 2015, 6:235-247. doi: 10.1002/wcs.1340 For further resources related to this article, please visit the WIREs website.The authors have declared no conflicts of interest for this article.
View details for DOI 10.1002/wcs.1340
View details for PubMedID 26263227
- Connectionist perspectives on language learning, representation and processing WILEY INTERDISCIPLINARY REVIEWS-COGNITIVE SCIENCE 2015; 6 (3): 235-247
Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition
2014; 38 (6): 1024-1077
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.
View details for DOI 10.1111/cogs.12148
View details for Web of Science ID 000340557000002
View details for PubMedID 25087578
Interactive activation and mutual constraint satisfaction in perception and cognition.
2014; 38 (6): 1139-1189
In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representation in real time as information arrives. Building on Rumelhart's arguments, we present the Interactive Activation hypothesis-the idea that the mechanism used in perception and comprehension to achieve these feats exploits an interactive activation process implemented through the bidirectional propagation of activation among simple processing units. We then examine the interactive activation model of letter and word perception and the TRACE model of speech perception, as early attempts to explore this hypothesis, and review the experimental evidence relevant to their assumptions and predictions. We consider how well these models address the computational challenge posed by the problem of perception, and we consider how consistent they are with evidence from behavioral experiments. We examine empirical and theoretical controversies surrounding the idea of interactive processing, including a controversy that swirls around the relationship between interactive computation and optimal Bayesian inference. Some of the implementation details of early versions of interactive activation models caused deviation from optimality and from aspects of human performance data. More recent versions of these models, however, overcome these deficiencies. Among these is a model called the multinomial interactive activation model, which explicitly links interactive activation and Bayesian computations. We also review evidence from neurophysiological and neuroimaging studies supporting the view that interactive processing is a characteristic of the perceptual processing machinery in the brain. In sum, we argue that a computational analysis, as well as behavioral and neuroscience evidence, all support the Interactive Activation hypothesis. The evidence suggests that contemporary versions of models based on the idea of interactive activation continue to provide a basis for efforts to achieve a fuller understanding of the process of perception.
View details for DOI 10.1111/cogs.12146
View details for PubMedID 25098813
Why bilateral damage is worse than unilateral damage to the brain.
Journal of cognitive neuroscience
2013; 25 (12): 2107-2123
Human and animal lesion studies have shown that behavior can be catastrophically impaired after bilateral lesions but that unilateral damage often produces little or no effect, even controlling for lesion extent. This pattern is found across many different sensory, motor, and memory domains. Despite these findings, there has been no systematic, computational explanation. We found that the same striking difference between unilateral and bilateral damage emerged in a distributed, recurrent attractor neural network. The difference persists in simple feedforward networks, where it can be understood in explicit quantitative terms. In essence, damage both distorts and reduces the magnitude of relevant activity in each hemisphere. Unilateral damage reduces the relative magnitude of the contribution to performance of the damaged side, allowing the intact side to dominate performance. In contrast, balanced bilateral damage distorts representations on both sides, which contribute equally, resulting in degraded performance. The model's ability to account for relevant patient data suggests that mechanisms similar to those in the model may operate in the brain.
View details for DOI 10.1162/jocn_a_00441
View details for PubMedID 23806177
Context, cortex, and associations: a connectionist developmental approach to verbal analogies
FRONTIERS IN PSYCHOLOGY
We present a PDP model of binary choice verbal analogy problems (A:B as C:[D1|D2], where D1 and D2 represent choice alternatives). We train a recurrent neural network in item-relation-item triples and use this network to test performance on analogy questions. Without training on analogy problems per se, the model explains the developmental shift from associative to relational responding as an emergent consequence of learning upon the environment's statistics. Such learning allows gradual, item-specific acquisition of relational knowledge to overcome the influence of unbalanced association frequency, accounting for association effects of analogical reasoning seen in cognitive development. The network also captures the overall degradation in performance after anterior temporal damage by deleting a fraction of learned connections, while capturing the return of associative dominance after frontal damage by treating frontal structures as necessary for maintaining activation of A and B while seeking a relation between C and D. While our theory is still far from being complete it provides a unified explanation of findings that need to be considered together in any integrated account of analogical reasoning.
View details for DOI 10.3389/fpsyg.2013.00857
View details for Web of Science ID 000331583200001
View details for PubMedID 24312068
Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory.
Journal of experimental psychology. General
2013; 142 (4): 1190-1210
The complementary learning systems theory of the roles of hippocampus and neocortex (McClelland, McNaughton, & O'Reilly, 1995) holds that the rapid integration of arbitrary new information into neocortical structures is avoided to prevent catastrophic interference with structured knowledge representations stored in synaptic connections among neocortical neurons. Recent studies (Tse et al., 2007, 2011) showed that neocortical circuits can rapidly acquire new associations that are consistent with prior knowledge. The findings challenge the complementary learning systems theory as previously presented. However, new simulations extending those reported in McClelland et al. (1995) show that new information that is consistent with knowledge previously acquired by a putatively cortexlike artificial neural network can be learned rapidly and without interfering with existing knowledge; it is when inconsistent new knowledge is acquired quickly that catastrophic interference ensues. Several important features of the findings of Tse et al. (2007, 2011) are captured in these simulations, indicating that the neural network model used in McClelland et al. has characteristics in common with neocortical learning mechanisms. An additional simulation generalizes beyond the network model previously used, showing how the rate of change of cortical connections can depend on prior knowledge in an arguably more biologically plausible network architecture. In sum, the findings of Tse et al. are fully consistent with the idea that hippocampus and neocortex are complementary learning systems. Taken together, these findings and the simulations reported here advance our knowledge by bringing out the role of consistency of new experience with existing knowledge and demonstrating that the rate of change of connections in real and artificial neural networks can be strongly prior-knowledge dependent.
View details for DOI 10.1037/a0033812
View details for PubMedID 23978185
A Differentiation Account of Recognition Memory: Evidence from fMRI
JOURNAL OF COGNITIVE NEUROSCIENCE
2013; 25 (3): 421-435
Differentiation models of recognition memory predict a strength-based mirror effect in the distributions of subjective memory strength. Subjective memory strength should increase for targets and simultaneously decrease for foils following a strongly encoded list compared with a weakly encoded list. An alternative explanation for the strength-based mirror effect is that participants adopt a stricter criterion following a strong list than a weak list. Behavioral experiments support the differentiation account. The purpose of this study was to identify the neural bases for these differences. Encoding strength was manipulated (strong, weak) in a rapid event-related fMRI paradigm. To investigate the effect of retrieval context on foils, foils were presented in test blocks containing strong or weak targets. Imaging analyses identified regions in which activity increased faster for foils tested after a strong list than a weak list. The results are interpreted in support of a differentiation account of memory and are suggestive that the angular gyrus plays a role in evaluating evidence related to the memory decision, even for new items.
View details for Web of Science ID 000314363200008
View details for PubMedID 23092213
Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review.
Frontiers in psychology
2013; 4: 503-?
This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered.
View details for DOI 10.3389/fpsyg.2013.00503
View details for PubMedID 23970868
Retrospective. R. Duncan Luce (1925-2012).
2012; 337 (6102): 1619-?
View details for PubMedID 23019641
Generalization Through the Recurrent Interaction of Episodic Memories: A Model of the Hippocampal System
2012; 119 (3): 573-616
In this article, we present a perspective on the role of the hippocampal system in generalization, instantiated in a computational model called REMERGE (recurrency and episodic memory results in generalization). We expose a fundamental, but neglected, tension between prevailing computational theories that emphasize the function of the hippocampus in pattern separation (Marr, 1971; McClelland, McNaughton, & O'Reilly, 1995), and empirical support for its role in generalization and flexible relational memory (Cohen & Eichenbaum, 1993; Eichenbaum, 1999). Our account provides a means by which to resolve this conflict, by demonstrating that the basic representational scheme envisioned by complementary learning systems theory (McClelland et al., 1995), which relies upon orthogonalized codes in the hippocampus, is compatible with efficient generalization-as long as there is recurrence rather than unidirectional flow within the hippocampal circuit or, more widely, between the hippocampus and neocortex. We propose that recurrent similarity computation, a process that facilitates the discovery of higher-order relationships between a set of related experiences, expands the scope of classical exemplar-based models of memory (e.g., Nosofsky, 1984) and allows the hippocampus to support generalization through interactions that unfold within a dynamically created memory space.
View details for DOI 10.1037/a0028681
View details for Web of Science ID 000306029300007
View details for PubMedID 22775499
- Can native Japanese listeners learn to differentiate /r-l/ on the basis of F3 onset frequency? BILINGUALISM-LANGUAGE AND COGNITION 2012; 15 (2): 255-274
Two Mechanisms of Human Contingency Learning
2012; 23 (1): 59-68
How do humans learn contingencies between events? Both pathway-strengthening and inference-based process models have been proposed to explain contingency learning. We propose that each of these processes is used in different conditions. Participants viewed displays that contained single or paired objects and learned which displays were usually followed by the appearance of a dot. Some participants predicted whether the dot would appear before seeing the outcome, whereas other participants were required to respond quickly if the dot appeared shortly after the display. In the prediction task, instructions guiding participants to infer which objects caused the dot to appear were necessary in order for contingencies associated with one object to influence participants' predictions about the object with which it had been paired. In the response task, contingencies associated with one object affected responses to its pair mate irrespective of whether or not participants were given causal instructions. Our results challenge single-mechanism accounts of contingency learning and suggest that the mechanisms underlying performance in the two tasks are distinct.
View details for DOI 10.1177/0956797611429577
View details for Web of Science ID 000300955100012
View details for PubMedID 22198929
- Using time-varying evidence to test models of decision dynamics: bounded diffusion vs. the leaky competing accumulator model FRONTIERS IN NEUROSCIENCE 2012; 6
- Predicting native English-like performance by native Japanese speakers JOURNAL OF PHONETICS 2011; 39 (4): 571-584
Dynamic Integration of Reward and Stimulus Information in Perceptual Decision-Making
2011; 6 (3)
In perceptual decision-making, ideal decision-makers should bias their choices toward alternatives associated with larger rewards, and the extent of the bias should decrease as stimulus sensitivity increases. When responses must be made at different times after stimulus onset, stimulus sensitivity grows with time from zero to a final asymptotic level. Are decision makers able to produce responses that are more biased if they are made soon after stimulus onset, but less biased if they are made after more evidence has been accumulated? If so, how close to optimal can they come in doing this, and how might their performance be achieved mechanistically? We report an experiment in which the payoff for each alternative is indicated before stimulus onset. Processing time is controlled by a "go" cue occurring at different times post stimulus onset, requiring a response within msec. Reward bias does start high when processing time is short and decreases as sensitivity increases, leveling off at a non-zero value. However, the degree of bias is sub-optimal for shorter processing times. We present a mechanistic account of participants' performance within the framework of the leaky competing accumulator model , in which accumulators for each alternative accumulate noisy information subject to leakage and mutual inhibition. The leveling off of accuracy is attributed to mutual inhibition between the accumulators, allowing the accumulator that gathers the most evidence early in a trial to suppress the alternative. Three ways reward might affect decision making in this framework are considered. One of the three, in which reward affects the starting point of the evidence accumulation process, is consistent with the qualitative pattern of the observed reward bias effect, while the other two are not. Incorporating this assumption into the leaky competing accumulator model, we are able to provide close quantitative fits to individual participant data.
View details for DOI 10.1371/journal.pone.0016749
View details for Web of Science ID 000287965200005
View details for PubMedID 21390225
- Testing multi-alternative decision models with non-stationary evidence FRONTIERS IN NEUROSCIENCE 2011; 5
- A PDP model of the simultaneous perception of multiple objects CONNECTION SCIENCE 2011; 23 (2): 161-172
Are there mental lexicons? The role of semantics in lexical decision
2010; 1365: 66-81
What is the underlying representation of lexical knowledge? How do we know whether a given string of letters is a word, whereas another string of letters is not? There are two competing models of lexical processing in the literature. The first proposes that we rely on mental lexicons. The second claims there are no mental lexicons; we identify certain items as words based on semantic knowledge. Thus, the former approach - the multiple-systems view - posits that lexical and semantic processing are subserved by separate systems, whereas the latter approach - the single-system view - holds that the two are interdependent. Semantic dementia patients, who have a cross-modal semantic impairment, show an accompanying and related lexical deficit. These findings support the single-system approach. However, a report of an SD patient whose impairment on lexical decision was not related to his semantic deficits in item-specific ways has presented a challenge to this view. If the two types of processing rely on a common system, then shouldn't damage impair the same items on all tasks? We present a single-system model of lexical and semantic processing, where there are no lexicons, and performance on lexical decision involves the activation of semantic representations. We show how, when these representations are damaged, accuracy on semantic and lexical tasks falls off together, but not necessarily on the same set of items. These findings are congruent with the patient data. We provide an explicit explanation of this pattern of results in our model, by defining and measuring the effects of two orthogonal factors - spelling consistency and concept consistency.
View details for DOI 10.1016/j.brainres.2010.09.057
View details for Web of Science ID 000285816900006
View details for PubMedID 20869349
- Emergence in Cognitive Science TOPICS IN COGNITIVE SCIENCE 2010; 2 (4): 751-770
Letting structure emerge: connectionist and dynamical systems approaches to cognition
TRENDS IN COGNITIVE SCIENCES
2010; 14 (8): 348-356
Connectionist and dynamical systems approaches explain human thought, language and behavior in terms of the emergent consequences of a large number of simple noncognitive processes. We view the entities that serve as the basis for structured probabilistic approaches as abstractions that are occasionally useful but often misleading: they have no real basis in the actual processes that give rise to linguistic and cognitive abilities or to the development of these abilities. Although structured probabilistic approaches can be useful in determining what would be optimal under certain assumptions, we propose that connectionist, dynamical systems, and related approaches, which focus on explaining the mechanisms that give rise to cognition, will be essential in achieving a full understanding of cognition and development.
View details for DOI 10.1016/j.tics.2010.06.002
View details for Web of Science ID 000281099600009
View details for PubMedID 20598626
Integration of Sensory and Reward Information during Perceptual Decision-Making in Lateral Intraparietal Cortex (LIP) of the Macaque Monkey
2010; 5 (2)
Single neurons in cortical area LIP are known to carry information relevant to both sensory and value-based decisions that are reported by eye movements. It is not known, however, how sensory and value information are combined in LIP when individual decisions must be based on a combination of these variables. To investigate this issue, we conducted behavioral and electrophysiological experiments in rhesus monkeys during performance of a two-alternative, forced-choice discrimination of motion direction (sensory component). Monkeys reported each decision by making an eye movement to one of two visual targets associated with the two possible directions of motion. We introduced choice biases to the monkeys' decision process (value component) by randomly interleaving balanced reward conditions (equal reward value for the two choices) with unbalanced conditions (one alternative worth twice as much as the other). The monkeys' behavior, as well as that of most LIP neurons, reflected the influence of all relevant variables: the strength of the sensory information, the value of the target in the neuron's response field, and the value of the target outside the response field. Overall, detailed analysis and computer simulation reveal that our data are consistent with a two-stage drift diffusion model proposed by Diederich and Bussmeyer for the effect of payoffs in the context of sensory discrimination tasks. Initial processing of payoff information strongly influences the starting point for the accumulation of sensory evidence, while exerting little if any effect on the rate of accumulation of sensory evidence.
View details for DOI 10.1371/journal.pone.0009308
View details for Web of Science ID 000274923700012
View details for PubMedID 20174574
View details for PubMedCentralID PMC2824817
- Modeling Unsupervised Perceptual Category Learning IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2009; 1 (1): 35-43
A connectionist model of a continuous developmental transition in the balance scale task
2009; 110 (3): 395-411
A connectionist model of the balance scale task is presented which exhibits developmental transitions between 'Rule I' and 'Rule II' behavior [Siegler, R. S. (1976). Three aspects of cognitive development. Cognitive Psychology,8, 481-520.] as well as the 'catastrophe flags' seen in data from Jansen and van der Maas [Jansen, B. R. J., & van der Maas, H. L. J. (2001). Evidence for the phase transition from Rule I to Rule II on the balance scale task. Developmental Review, 21, 450-494]. The model extends a connectionist model of this task [McClelland, J. L. (1989). Parallel distributed processing: Implications for cognition and development. In R. G. M. Morris (Ed.), Parallel distributed processing: Implications for psychology and neurobiology (pp. 8-45). Oxford: Clarendon Press] by introducing intrinsic variability into processing and by allowing the network to adapt during testing in response to its own outputs. The simulations direct attention to several aspects of the experimental data indicating that children generally show gradual change in sensitivity to the distance dimension on the balance scale. While a few children show larger changes than are characteristic of the model, its ability to account for nearly all of the data using continuous processes is consistent with the view that the transition from Rule I to Rule II behavior is typically continuous rather than discrete in nature.
View details for DOI 10.1016/j.cognition.2008.11.017
View details for Web of Science ID 000264039900006
View details for PubMedID 19171326
- Is a Machine Realization of Truly Human-Like Intelligence Achievable? COGNITIVE COMPUTATION 2009; 1 (1): 17-21
- The Place of Modeling in Cognitive Science TOPICS IN COGNITIVE SCIENCE 2009; 1 (1): 11-38
- Precis of Semantic Cognition: A Parallel Distributed Processing Approach BEHAVIORAL AND BRAIN SCIENCES 2008; 31 (6): 689-?
Objective assessment of deformable image registration in radiotherapy: A multi-institution study
2008; 35 (12): 5944-5953
The looming potential of deformable alignment tools to play an integral role in adaptive radiotherapy suggests a need for objective assessment of these complex algorithms. Previous studies in this area are based on the ability of alignment to reproduce analytically generated deformations applied to sample image data, or use of contours or bifurcations as ground truth for evaluation of alignment accuracy. In this study, a deformable phantom was embedded with 48 small plastic markers, placed in regions varying from high contrast to roughly uniform regional intensity, and small to large regional discontinuities in movement. CT volumes of this phantom were acquired at different deformation states. After manual localization of marker coordinates, images were edited to remove the markers. The resulting image volumes were sent to five collaborating institutions, each of which has developed previously published deformable alignment tools routinely in use. Alignments were done, and applied to the list of reference coordinates at the inhale state. The transformed coordinates were compared to the actual marker locations at exhale. A total of eight alignment techniques were tested from the six institutions. All algorithms performed generally well, as compared to previous publications. Average errors in predicted location ranged from 1.5 to 3.9 mm, depending on technique. No algorithm was uniformly accurate across all regions of the phantom, with maximum errors ranging from 5.1 to 15.4 mm. Larger errors were seen in regions near significant shape changes, as well as areas with uniform contrast but large local motion discontinuity. Although reasonable accuracy was achieved overall, the variation of error in different regions suggests caution in globally accepting the results from deformable alignment.
View details for DOI 10.1118/1.3013563
View details for Web of Science ID 000261210000071
View details for PubMedID 19175149
View details for PubMedCentralID PMC2673610
- Effects of attention on the strength of lexical influences on speech perception: Behavioral experiments and computational mechanisms COGNITIVE SCIENCE 2008; 32 (2): 398-417
Modeling Unsupervised Perceptual Category Learning
7th IEEE International Conference on Development and Learning
IEEE. 2008: 25–30
View details for Web of Science ID 000265407300005
A single-system account of semantic and lexical deficits in five semantic dementia patients
2008; 25 (2): 136-164
In semantic dementia (SD), there is a correlation between performance on semantic tasks such as picture naming and lexical tasks such as reading aloud. However, there have been a few case reports of patients with spared reading despite profound semantic impairment. These reports have sparked an ongoing debate about how the brain processes conceptual versus lexical knowledge. One possibility is that there are two functionally distinct systems in the brain-one for semantic and one for lexical processing. Alternatively, there may be a single system involved in both. We present a computational investigation of the role of individual differences in explaining the relationship between naming and reading performance in five SD patients, among whom there are cases of both association and dissociation of deficits. We used a connectionist model where information from different modalities feeds into a single integrative layer. Our simulations successfully produced the overall relationship between reading and naming seen in SD and provided multiple fits for both association and dissociation data, suggesting that a single, cross-modal, integrative system is sufficient for both semantic and lexical tasks and that individual differences among patients are essential in accounting for variability in performance.
View details for DOI 10.1080/02643290701723948
View details for Web of Science ID 000257087600002
View details for PubMedID 18568816
Language is not just for talking - Redundant labels facilitate learning of novel categories
2007; 18 (12): 1077-1083
In addition to having communicative functions, verbal labels may play a role in shaping concepts. Two experiments assessed whether the presence of labels affected category formation. Subjects learned to categorize "aliens" as those to be approached or those to be avoided. After accuracy feedback on each response was provided, a nonsense label was either presented or not. Providing nonsense category labels facilitated category learning even though the labels were redundant and all subjects had equivalent experience with supervised categorization of the stimuli. A follow-up study investigated differences between learning verbal and nonverbal associations and showed that learning a nonverbal association did not facilitate categorization. The findings show that labels make category distinctions more concrete and bear directly on the language-and-thought debate.
View details for Web of Science ID 000251206100011
View details for PubMedID 18031415
Unsupervised learning of vowel categories from infant-directed speech
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2007; 104 (33): 13273-13278
Infants rapidly learn the sound categories of their native language, even though they do not receive explicit or focused training. Recent research suggests that this learning is due to infants' sensitivity to the distribution of speech sounds and that infant-directed speech contains the distributional information needed to form native-language vowel categories. An algorithm, based on Expectation-Maximization, is presented here for learning the categories from a sequence of vowel tokens without (i) receiving any category information with each vowel token, (ii) knowing in advance the number of categories to learn, or (iii) having access to the entire data ensemble. When exposed to vowel tokens drawn from either English or Japanese infant-directed speech, the algorithm successfully discovered the language-specific vowel categories (/I, i, epsilon, e/ for English, /I, i, e, e/ for Japanese). A nonparametric version of the algorithm, closely related to neural network models based on topographic representation and competitive Hebbian learning, also was able to discover the vowel categories, albeit somewhat less reliably. These results reinforce the proposal that native-language speech categories are acquired through distributional learning and that such learning may be instantiated in a biologically plausible manner.
View details for DOI 10.1073/pnas.0705369104
View details for Web of Science ID 000248899600013
View details for PubMedID 17664424
Using domain-general principles to explain children's causal reasoning abilities
2007; 10 (3): 333-356
A connectionist model of causal attribution is presented, emphasizing the use of domain-general principles of processing and learning previously employed in models of semantic cognition. The model categorizes objects dependent upon their observed 'causal properties' and is capable of making several types of inferences that 4-year-old children have been shown to be capable of. The model gives rise to approximate conformity to normative models of causal inference and gives approximate estimates of the probability that an object presented in an ambiguous situation actually possesses a particular causal power, based on background knowledge and recent observations. It accounts for data from three sets of experimental studies of the causal inferencing abilities of young children. The model provides a base for further efforts to delineate the intuitive mechanisms of causal inference employed by children and adults, without appealing to inherent principles or mechanisms specialized for causal as opposed to other forms of reasoning.
View details for DOI 10.1111/j.1467-7687.2007.00586.x
View details for Web of Science ID 000245812200006
View details for PubMedID 17444974
Success and failure of new speech category learning in adulthood: Consequences of learned Hebbian attractors in topographic maps
COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE
2007; 7 (1): 53-73
The influence of a native language on learning new speech sounds in adulthood is addressed using a network model in which speech categories are attractors implemented through interactive activation and Hebbian learning. The network has a representation layer that receives topographic projections from an input layer and has reciprocal excitatory connections with deeper layers. When applied to an experiment in which Japanese adults were trained to distinguish the English /r/-/l/ contrast (McCandliss, Fiez, Protopapas, Conway, & McClelland, 2002), the model can account for many aspects of the experimental results, such as the time course and outcome of the learning, how it varies as a function of feedback, the relative efficacy of adaptive and initially easy training stimuli versus nonadaptive and difficult stimuli, and the development of a discrimination peak at the acquired category boundary. The model is also able to capture some aspects of the individual differences in learning.
View details for Web of Science ID 000247048500006
View details for PubMedID 17598735
- Gradience of gradience: A reply to Jackendoff (Ray Jackendoff) LINGUISTIC REVIEW 2007; 24 (4): 437-455
A homeostatic rule for inhibitory synapses promotes temporal sharpening and cortical reorganization
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2006; 103 (44): 16526-16531
Experience with transient stimuli leads to stronger neural responses that also rise and fall more sharply in time. This sharpening enhances the processing of transients and may be especially relevant for speech perception. We consider a learning rule for inhibitory connections that promotes this sharpening effect by adjusting these connections to maintain a target homeostatic level of activity in excitatory neurons. We analyze this rule in a recurrent network model of excitatory and inhibitory units. Strengthening inhibitory-->excitatory connections along with excitatory-->excitatory connections is required to obtain a sharpening effect. Using the homeostatic rule, we show that repeated presentations of a transient signal will "teach" the network to respond to the signal with both higher amplitude and shorter duration. The model also captures reorganization of receptive fields in the sensory hand area after amputation or peripheral nerve resection.
View details for DOI 10.1073/pnas.0607589103
View details for Web of Science ID 000241879500082
View details for PubMedID 17050684
ON THE CONTROL OF AUTOMATIC PROCESSES - A PARALLEL DISTRIBUTED-PROCESSING ACCOUNT OF THE STROOP EFFECT
1990; 97 (3): 332-361
Traditional views of automaticity are in need of revision. For example, automaticity often has been treated as an all-or-none phenomenon, and traditional theories have held that automatic processes are independent of attention. Yet recent empirical data suggest that automatic processes are continuous, and furthermore are subject to attentional control. A model of attention is presented to address these issues. Within a parallel distributed processing framework, it is proposed that the attributes of automaticity depend on the strength of a processing pathway and that strength increases with training. With the Stroop effect as an example, automatic processes are shown to be continuous and to emerge gradually with practice. Specifically, a computational model of the Stroop task simulates the time course of processing as well as the effects of learning. This was accomplished by combining the cascade mechanism described by McClelland (1979) with the backpropagation learning algorithm (Rumelhart, Hinton, & Williams, 1986). The model can simulate performance in the standard Stroop task, as well as aspects of performance in variants of this task that manipulate stimulus-onset asynchrony, response set, and degree of practice. The model presented is contrasted against other models, and its relation to many of the central issues in the literature on attention, automaticity, and interference is discussed.
View details for Web of Science ID A1990DN33800002
View details for PubMedID 2200075
A SIMULATION-BASED TUTORIAL SYSTEM FOR EXPLORING PARALLEL DISTRIBUTED-PROCESSING
BEHAVIOR RESEARCH METHODS INSTRUMENTS & COMPUTERS
1988; 20 (2): 263-275
View details for Web of Science ID A1988M935500040