Evidence accumulation has been the core component in recent development of perceptual and value-based decision-making theories. Most studies have focused on the evaluation of evidence between alternative options. What remains largely unknown is the process that prepares evidence: how may the decision-maker sample different sources of information sequentially, if they can only sample one source at a time? Here we propose a normative framework in prescribing how different sources of information should be sampled proactively to facilitate the decision process: beliefs (...) for different noisy sources are updated in a Bayesian manner and participants can proactively allocate resource for sampling (i.e. saccades) among different sources to maximize the information gain in such process. We show that our framework can account for human participants’ actual choice and saccade behaviors in a two-alternative value-based decision-making task. Moreover, our framework makes novel predictions about the empirical eye movement patterns. (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)

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a (...) more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways. (shrink)

The dynamics of neural populations capture the laws of the mindThis paper focuses on the level of neural networks. Examining the case of recurrent neural networks, the paper argues that the dynamics of neural populations form a privileged level of explanation in cognitive science. According to Schöner, this level is privileged, because it enables cognitive scientists to discover the laws governing organisms’ cognition and behaviour.

Previous studies have shown that multiple reference frames are available and compete for selection during the use of spatial terms such as “above.” However, the mechanisms that underlie the selection process are poorly understood. In the current paper we present two experiments and a comparison of three computational models of selection to shed further light on the nature of reference frame selection. The three models are drawn from different areas of human cognition, and we assess whether they may be applied (...) to a reference frame selection by examining their ability to account for both existing and new empirical data comprising acceptance rates, response times, and response time distributions. These three models are the competitive shunting model, the leaky competing accumulator model, and a lexical selection model. Model simulations show that only the LCA model satisfactorily accounts for the empirical observations. The key properties of this model that seem to drive its success are its bounded linear activation function, its number and type of processing stages, and its use of decay. Uncovering these critical properties has important implications for our understanding not only of spatial term use, in particular, but also of conflict and selection in human cognition more generally. (shrink)

Cognitive Science, Volume 46, Issue 5, May 2022.

A central goal in Cognitive Science is understanding the mechanisms that underlie cognition. Here, we contend that Cognitive Science, despite intense multidisciplinary efforts, has furnished surprisingly few mechanistic insights. We attribute this slow mechanistic progress to the fact that cognitive scientists insist on performing underdetermined exercises, deriving overparametrized mechanistic theories of complex behaviors and seeking validation of these theories to the elusive notions of optimality and biological plausibility. We propose that mechanistic progress in Cognitive Science will accelerate once cognitive scientists (...) start focusing on simpler explananda that will enable them to chart an atlas of elementary cognitive operations. Looking forward, the next challenge for Cognitive Science will be to understand how these elementary cognitive processes are pieced together to explain complex behavior. (shrink)

Complex problem solving is often an integration of perceptual processing and deliberate planning. But what balances these two processes, and how do novices differ from experts? We investigate the interplay between these two in the game of SET. This article investigates how people combine bottom-up visual processes and top-down planning to succeed in this game. Using combinatorial and mixed-effect regression analysis of eye-movement protocols and a cognitive model of a human player, we show that SET players deploy both bottom-up and (...) top-down processes in parallel to accomplish the same task. The combination of competition and cooperation of both types of processes is a major factor of success in the game. Finally, we explore strategies players use during the game. Our findings suggest that within-trial strategy shifts can occur without the need of explicit meta-cognitive control, but rather implicitly as a result of evolving memory activations. (shrink)

Neuroscientific studies have shown that brain activity correlated with a decision to move can be observed before a person reports being consciously aware of having made that decision . Given that a later event cannot cause an earlier one , such results have been interpreted as evidence that decisions are made unconsciously . We argue that this interpretation depends upon an all-or-none view of consciousness, and we offer an alternative interpretation of the early decision-related brain activity based on models in (...) which conscious awareness of the decision to move develops gradually up to the level of a reporting criterion. Under this interpretation, the early brain activity reflects sub-criterion levels of awareness rather than complete absence of awareness and thus does not suggest that decisions are made unconsciously. (shrink)

I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification has no downsides; (...) it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000‐fold increase? (shrink)

The study of human intelligence was once dominated by symbolic approaches, but over the last 30 years an alternative approach has arisen. Symbols and processes that operate on them are often seen today as approximate characterizations of the emergent consequences of sub- or nonsymbolic processes, and a wide range of constructs in cognitive science can be understood as emergents. These include representational constructs (units, structures, rules), architectural constructs (central executive, declarative memory), and developmental processes and outcomes (stages, sensitive periods, neurocognitive (...) modules, developmental disorders). The greatest achievements of human cognition may be largely emergent phenomena. It remains a challenge for the future to learn more about how these greatest achievements arise and to emulate them in artificial systems. (shrink)

We share with Anderson & Lebiere (A&L) (and with Newell before them) the goal of developing a domain-general framework for modeling cognition, and we take seriously the issue of evaluation criteria. We advocate a more focused approach than the one reflected in Newell's criteria, based on analysis of failures as well as successes of models brought into close contact with experimental data. A&L attribute the shortcomings of our parallel-distributed processing framework to a failure to acknowledge a symbolic level of thought. (...) Our framework does acknowledge a symbolic level, contrary to their claim. What we deny is that the symbolic level is the level at which the principles of cognitive processing should be formulated. Models cast at a symbolic level are sometimes useful as high-level approximations of the underlying mechanisms of thought. The adequacy of this approximation will continue to increase as symbolic modelers continue to incorporate principles of parallel distributed processing. (shrink)

Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependency completion difficulty are investigated, the Lewis and Vasishth (2005) activation-based model and the direct-access model (DA; McElree, 2000). These models' predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self-paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k-fold (...) cross-validation. For both IWAs and controls, the activation-based model furnishes a somewhat better quantitative fit to the data than the DA. Model comparisons using Bayes factors show that, assuming an activation-based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs, although slowed syntax and lexical delayed access may also play a role. This is the first computational evaluation of different models of dependency completion using data from impaired and unimpaired individuals. This evaluation develops a systematic approach that can be used to quantitatively compare the predictions of competing models of language processing. (shrink)

Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependency completion difficulty are investigated, the Lewis and Vasishth (2005) activation‐based model and the direct‐access model (DA; McElree, 2000). These models' predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self‐paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k‐fold (...) cross‐validation. For both IWAs and controls, the activation‐based model furnishes a somewhat better quantitative fit to the data than the DA. Model comparisons using Bayes factors show that, assuming an activation‐based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs, although slowed syntax and lexical delayed access may also play a role. This is the first computational evaluation of different models of dependency completion using data from impaired and unimpaired individuals. This evaluation develops a systematic approach that can be used to quantitatively compare the predictions of competing models of language processing. (shrink)

IntroductionAlthough the motor signs of Parkinson’s disease are well defined, nonmotor symptoms, including higher-level language deficits, have also been shown to be frequent in patients with PD. In the present study, we used a lexical decision task to find out whether access to the mental lexicon is impaired in patients with PD, and whether task performance is affected by bradykinesia.Materials and MethodsParticipants were 34 nondemented patients with PD, either without medication or under optimum medication. A total of 19 age-matched control (...) volunteers were also recruited. We recorded reaction times to the LDT and a simple RT task. In each task, stimuli were either visual or auditory. Statistical analyses consisted of repeated-measures analyses of variance and Tukey’s HSD post hoc tests.ResultsIn the LDT, participants with PD both off and on medication exhibited intact access to the mental lexicon in both modalities. In the visual modality, patients off medication were just as fast as controls when identifying real words, but slower when identifying pseudowords. In the visual modality of the control task, RTs for pseudowords were significantly longer for PD patients off medication than for controls, revealing an unexpected but significant lexicality effect in patients that was not observed in the auditory modality. Performances of patients on medication did not differ from those of age-matched controls.DiscussionMotor execution was not slowed in patients with PD either off or on medication, in comparison with controls. Regarding lexical access, patients off medication seemed to have difficulty inhibiting a cognitive-linguistic process when it was not required, and exhibit a specific pseudoword processing deficit in the LDT, which may have been related to impaired lateral word inhibition within the mental lexicon. These deficits seemed to be compensated by medication. (shrink)