This paper presents a general computational treatment of how mammals are able to deal with visual objects and environments. The model tries to cover the entire range from behavior and phenomenological experience to detailed neural encodings in crude but computationally plausible reductive steps. The problems addressed include perceptual constancies, eye movements and the stable visual world, object descriptions, perceptual generalizations, and the representation of extrapersonal space.The entire development is based on an action-oriented notion of perception. The observer is assumed to (...) be continuously sampling the ambient light for information of current value. The central problem of vision is taken to be categorizing and locating objects in the environment. The critical step in this process is the linking of visual information to symbolic object descriptions; this is calledindexing, from the analogy of identifying a book from index terms. The system must also identifysituationsand use this knowledge to guide movement and other actions in the environment. The treatment focuses on the different representations of information used in the visual system.The four representational frames capture information in the following forms: retinotopic, head-based, symbolic, and allocentric. The functional roles of the four frames, the communication among them, and their suggested neurophysiological realization constitute the core of the paper. The model is perforce crude, but appears to be consistent with all relevant findings. (shrink)

The way of coding letter position has been extensively assessed during the recognition of native words, leading to the development of a new generation of models that assume more flexible letter position coding schemes compared to classical computational models such as the interactive activation model. However, determining whether similar letter position encoding mechanisms occur during the bilingual word recognition has been largely less explored despite its implications for the leading model of bilingual word recognition as it assumes the input-coding scheme (...) of the IA model. In this study, we aimed to examine this issue through the manipulation of the position of the deviant letter of cognate words. Two experiments were conducted with Catalan–Spanish bilinguals and their respective monolingual controls. The results revealed a differential processing for the first letter in comparison to the other letters as well as modulations as a function of language cue, suggesting amendments to the input-coding scheme of the multilink model. (shrink)

Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper develops two models of human text document classification based on random walk and accumulator sequential sampling processes. The models are evaluated using data from (...) an experiment where participants classify text documents presented one word at a time under task instructions that emphasize either speed or accuracy, and rate their confidence in their decisions. Fitting the random walk and accumulator models to these data shows that the accumulator provides a better account of the decisions made, and a “balance of evidence” measure provides the best account of confidence. Both models are also evaluated in the applied information retrieval context, by comparing their performance to established machine learning techniques on the standard Reuters‐21578 corpus. It is found that they are almost as accurate as the benchmarks, and make decisions much more quickly because they only need to examine a small proportion of the words in the document. In addition, the ability of the accumulator model to produce useful confidence measures is shown to have application in prioritizing the results of classification decisions. (shrink)

Mental chronometry is a classical paradigm in cognitive psychology that uses response time and accuracy data in perceptual-motor tasks to elucidate the architecture and mechanisms of the underlying cognitive processes of human decisions. The redundant signals paradigm investigates the response behavior in Experimental tasks, where an integration of signals is required for a successful performance. The common finding is that responses are speeded for the redundant signals condition compared to single signals conditions. On a mean level, this redundant signals effect (...) can be accounted for by several cognitive architectures, exhibiting considerable model mimicry. Jeff Miller formalized the maximum speed-up explainable by separate activations or race models in form of a distributional bound – the race model inequality. Whenever data violates this bound, it excludes race models as a viable account for the redundant signals effect. The common alternative is a coactivation account, where the signals integrate at some stage in the processing. Coactivation models have mostly been inferred on and rarely explicated though. Where coactivation is explicitly modeled, it is assumed to have a decisional locus. However, in the literature there are indications that coactivation might have at least a partial locus in the nondecisional or motor stage. There are no studies that have tried to compare the fit of these coactivation variants to empirical data to test different effect generating loci. Ever since its formulation, the race model inequality has been used as a test to infer the cognitive architecture for observers’ performance in redundant signals Experiments. Subsequent theoretical and empirical analyses of this RMI test revealed several challenges. On the one hand, it is considered to be a conservative test, as it compares data to the maximum speed-up possible by a race model account. Moreover, simulation studies could show that the base time component can further reduce the power of the test, as violations are filtered out when this component has a high variance. On the other hand, another simulation study revealed that the common practice of RMI test can introduce an estimation bias, that effectively facilitates violations and increases the type I error of the test. Also, as the RMI bound is usually tested at multiple points of the same data, an inflation of type I errors can reach a substantial amount. Due to the lack of overlap in scope and the usage of atheoretic, descriptive reaction time models, the degree to which these results can be generalized is limited. State-of-the-art models of decision making provide a means to overcome these limitations and implement both race and coactivation models in order to perform large scale simulation studies. By applying a state-of-the-art model of decision making to the investigation of the redundant signals effect, the present study addresses research questions at different levels. On a conceptual level, it raises the question, at what stage coactivation occurs – at a decisional, a nondecisional or a combined decisional and nondecisional processing stage and to what extend? To that end, two bimodal detection tasks have been conducted. As the reaction time data exhibits violations of the RMI at multiple time points, it provides the basis for a comparative fitting analysis of coactivation model variants, representing different loci of the effect. On a test theoretic level, the present study integrates and extends the scopes of previous studies within a coherent simulation framework. The effect of experimental and statistical parameters on the performance of the RMI test is analyzed via Monte Carlo simulations. Specifically, the simulations treated the following questions: what is the power of the RMI test, is there an estimation bias for coactivated data as well and if so, in what direction, what is the effect of a highly varying base time component on the estimation bias, type I errors and power rates, and are the results of previous simulation studies replicable, when current models of decision making are used for the reaction time generation. For this purpose, the Ratcliff diffusion model was used to implement race models with controllable amount of correlation and coactivation models with varying integration strength, and independently specifying the base time component. The results of the fitting suggest that for the two bimodal detection tasks, coactivation has a shared decisional and nondecisional locus. For the focused attention experiment the decisional part prevails, whereas in the divided attention task the motor component is dominating the redundant signals effect. The simulation study could reaffirm the conservativeness of the RMI test as latent coactivation is frequently missed. An estimation bias was found also for coactivated data however, both biases become negligible once more than 10 samples per condition are taken to estimate the respective distribution functions. A highly varying base time component reduces both the type I errors and the power of the test, while not affecting the estimation biases. The outcome of the present study has theoretical and practical implications for the investigations of decisions in a multisignal context. Theoretically, it contributes to the locus question of coactivation and offers evidence for a combined decisional and nondecisional coactivation account. On a practical level, the modular simulation approach developed in the present study enables researchers to further investigate the RMI test within a coherent and theoretically grounded framework. It effectively provides a means to optimally set up the RMI test and thus helps to solidify and substantiate its outcomes. On a conceptual level the present study advocates the application of current formal models of decision making to the mental chronometry paradigm and develops future research questions in the field of the redundant signals paradigm. (shrink)