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Psychological Review
Journal Prestige (SJR): 4.64
Citation Impact (citeScore): 7
Number of Followers: 163  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 0033-295X - ISSN (Online) 1939-1471
Published by APA Homepage  [74 journals]
  • Automatic control: How experts act without thinking.
    • Abstract: Experts act without thinking because their skill is hierarchical. A single conscious thought automatically produces a series of lower-level actions without top-down monitoring. This article presents a theory that explains how automatic control is possible in skilled typing, where thinking of a word automatically produces a rapid series of keystrokes. The theory assumes that keystrokes are selected by a context retrieval process that matches the current context to stored contexts and retrieves the key associated with the best match. The current context is generated by the typist’s own actions. It represents the goal (“type DOG”) and the motor commands for the keys struck so far. Top-down control is necessary to start typing. It sets the goal in the current context, which initiates the retrieval and updating processes, which continue without top-down control until the word is finished. The theory explains phenomena of hierarchical control in skilled typing, including differential loads on higher and lower levels of processing, the importance of words, and poor explicit knowledge of key locations and finger-to-key mappings. The theory is evaluated by fitting it to error corpora from 24 skilled typists and predicting error probabilities, magnitudes, and patterns. Some of the fits are quite good. The theory has implications beyond typing. It argues that control can be automatic and shows how it is possible. The theory extends to other sequential skills, like texting or playing music. It provides new insights into mechanisms of serial order in typing, speaking, and serial recall. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
      PubDate: Thu, 28 Jun 2018 04:00:00 GMT
       
  • Chunking as a rational strategy for lossy data compression in visual
           working memory.
    • Abstract: The nature of capacity limits for visual working memory has been the subject of an intense debate that has relied on models that assume items are encoded independently. Here we propose that instead, similar features are jointly encoded through a “chunking” process to optimize performance on visual working memory tasks. We show that such chunking can: (a) facilitate performance improvements for abstract capacity-limited systems, (b) be optimized through reinforcement, (c) be implemented by center-surround dynamics, and (d) increase effective storage capacity at the expense of recall precision. Human performance on a variant of a canonical working memory task demonstrated performance advantages, precision detriments, interitem dependencies, and trial-to-trial behavioral adjustments diagnostic of performance optimization through center-surround chunking. Models incorporating center-surround chunking provided a better quantitative description of human performance in our study as well as in a meta-analytic dataset, and apparent differences in working memory capacity across individuals were attributable to individual differences in the implementation of chunking. Our results reveal a normative rationale for center-surround connectivity in working memory circuitry, call for reevaluation of memory performance differences that have previously been attributed to differences in capacity, and support a more nuanced view of visual working memory capacity limitations: strategic tradeoff between storage capacity and memory precision through chunking contribute to flexible capacity limitations that include both discrete and continuous aspects. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
      PubDate: Thu, 28 Jun 2018 04:00:00 GMT
       
  • Multialternative decision by sampling: A model of decision making
           constrained by process data.
    • Abstract: Sequential sampling of evidence, or evidence accumulation, has been implemented in a variety of models to explain a range of multialternative choice phenomena. But the existing models do not agree on what, exactly, the evidence is that is accumulated. They also do not agree on how this evidence is accumulated. In this article, we use findings from process-tracing studies to constrain the evidence accumulation process. With these constraints, we extend the decision by sampling model and propose the multialternative decision by sampling (MDbS) model. In MDbS, the evidence accumulated is outcomes of pairwise ordinal comparisons between attribute values. MDbS provides a quantitative account of the attraction, compromise, and similarity effects equal to that of other models, and captures a wider range of empirical phenomena than other models. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
      PubDate: Thu, 28 Jun 2018 04:00:00 GMT
       
  • Hilbert space multidimensional theory.
    • Abstract: A general theory of measurement context effects, called Hilbert space multidimensional (HSM) theory, is presented. A measurement context refers to a subset of psychological variables that an individual evaluates on a particular occasion. Different contexts are formed by evaluating different but possibly overlapping subsets of variables. Context effects occur when the judgments across contexts cannot be derived from a single joint probability distribution over the complete set of values of the observed variables. HSM theory provides a way to model these context effects by using quantum probability theory, which represents all the variables within a low dimensional vector space. HSM models produce parameter estimates that provide a simple and informative interpretation of the complex collection of judgments across contexts. Comparisons of HSM model fits with Bayesian network model fits are reported for a new large experiment, demonstrating the viability of this new model. We conclude that the theory is broadly applicable to measurement context effects found in the social and behavioral sciences. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
      PubDate: Thu, 28 Jun 2018 04:00:00 GMT
       
  • Refining the law of practice.
    • Abstract: The “law of practice”—a simple nonlinear function describing the relationship between mean response time (RT) and practice—has provided a practically and theoretically useful way of quantifying the speed-up that characterizes skill acquisition. Early work favored a power law, but this was shown to be an artifact of biases caused by averaging over participants who are individually better described by an exponential law. However, both power and exponential functions make the strong assumption that the speedup always proceeds at a steadily decreasing rate, even though there are sometimes clear exceptions. We propose a new law that can both accommodate an initial delay resulting in a slower–faster–slower rate of learning, with either power or exponential forms as limiting cases, and which can account for not only mean RT but also the effect of practice on the entire distribution of RT. We evaluate this proposal with data from a broad array of tasks using hierarchical Bayesian modeling, which pools data across participants while minimizing averaging artifacts, and using inference procedures that take into account differences in flexibility among laws. In a clear majority of paradigms our results supported a delayed exponential law. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
      PubDate: Thu, 28 Jun 2018 04:00:00 GMT
       
  • Don’t blame the model: Reconsidering the network approach to
           psychopathology.
    • Abstract: The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue that this rationale for the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders. As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches that can provide a better framework for conceptualizing mental disorders. Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
      PubDate: Thu, 28 Jun 2018 04:00:00 GMT
       
  • The emergence of polychronization and feature binding in a spiking neural
           network model of the primate ventral visual system.
    • Abstract: We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the input training images. The performance is improved by including top-down and lateral synaptic connections, as well as introducing multiple synaptic contacts between each pair of pre- and postsynaptic neurons, with different synaptic contacts having different axonal delays. Spike-timing-dependent plasticity thus allows the model to select the most effective axonal transmission delay between neurons. Furthermore, neurons representing the binding relationship between low-level and high-level visual features emerge through visually guided learning. This begins to provide a way forward to solving the classic feature binding problem in visual neuroscience and leads to a new hypothesis concerning how information about visual features at every spatial scale may be projected upward through successive neuronal layers. We name this hypothetical upward projection of information the “holographic principle.” (PsycINFO Database Record (c) 2018 APA, all rights reserved)
      PubDate: Mon, 04 Jun 2018 04:00:00 GMT
       
 
 
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