Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole-word model, or a (...) model that seeks for an optimal balance between these two extremes. Our results showed that human word recognition was predicted best by a combination of two models: a model that decomposes words at some morpheme boundaries while keeping others unsegmented and a whole-word model. The results support dual-route models that assume that both decomposed and full-form representations are utilized to optimally process complex words within the mental lexicon. (shrink)

A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well-suited to predict and explain mental processes that best exemplify our ability to be rational. However, evidence from belief acquisition and change appears to show that we do not acquire and update information in a Bayesian way. Instead, the principles of belief acquisition and updating seem grounded in maintaining a psychological immune system rather than in approximating a Bayesian processor.

We explore the idea that eye-movement strategies in reading are precisely adapted to the joint constraints of task structure, task payoff, and processing architecture. We present a model of saccadic control that separates a parametric control policy space from a parametric machine architecture, the latter based on a small set of assumptions derived from research on eye movements in reading (Engbert, Nuthmann, Richter, & Kliegl, 2005; Reichle, Warren, & McConnell, 2009). The eye-control model is embedded in a decision architecture (a (...) machine and policy space) that is capable of performing a simple linguistic task integrating information across saccades. Model predictions are derived by jointly optimizing the control of eye movements and task decisions under payoffs that quantitatively express different desired speed-accuracy trade-offs. The model yields distinct eye-movement predictions for the same task under different payoffs, including single-fixation durations, frequency effects, accuracy effects, and list position effects, and their modulation by task payoff. The predictions are compared to—and found to accord with—eye-movement data obtained from human participants performing the same task under the same payoffs, but they are found not to accord as well when the assumptions concerning payoff optimization and processing architecture are varied. These results extend work on rational analysis of oculomotor control and adaptation of reading strategy (Bicknell & Levy, ; McConkie, Rayner, & Wilson, 1973; Norris, 2009; Wotschack, 2009) by providing evidence for adaptation at low levels of saccadic control that is shaped by quantitatively varying task demands and the dynamics of processing architecture. (shrink)

We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of (...) rational action. Theories are specified as optimal program problems, defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing “levels” approaches to explanation, and to other optimality‐based modeling approaches. (shrink)

Inspired by, and in close relation with, the contributions of this special issue, Kuperberg elegantly links event comprehension, production, and learning. She proposes an overarching hierarchical generative framework of processing events enabling us to make sense of the world around us and to interact with it in a competent manner.

Bayesian probability calculus has recently emerged as a model for how the mind learns about fictional and cultural environments. This essay considers reading a narrative as a process of learning about the probabilities of the fictional world and explores two novels about Quixotic readers from a Bayesian perspective. The juxtaposition of the fictional and the real, which the standard understanding of the Quixote relies on, is replaced by an outline of the dynamics of the learning process in Quixotes who already (...) know ‘the rule’, that is, the probabilities of their fictional environment, but need to use this knowledge with discernment. The conclusion draws connexions between the Quixotic learning process and the ways in which fiction contributes to human mental, social and cultural development. (shrink)

In this article, we apply a special case of holographic representations to letter position coding. We translate different well-known schemes into this format, which uses distributed representations and supports constituent structure. We show that in addition to these brain-like characteristics, performances on a standard benchmark of behavioral effects are improved in the holographic format relative to the standard localist one. This notably occurs because of emerging properties in holographic codes, like transposition and edge effects, for which we give formal demonstrations. (...) Finally, we outline the limits of the approach as well as its possible future extensions. (shrink)

When we read, we do not fixate on each word! How does that work? By deep theory those sorts of decisions must be the result of complex decisions involving the specific “word,” the linguistic context in which it appears, and visual information. But is reading really all that difficult? After all, simple heuristics models of reading seem to do sort of okay by only considering the additive effects of word and context. Entropy measures do well at predicting word “skipping” and (...) suggest that “word frequency” may play the dominant role in the “decision” to skip or not skip! (shrink)

We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set (...) of interacting subfunctions are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level. (shrink)

When word pairs having a familiar order are sequentially flashed on a computer in their non-familiar order, , observers have a strong phenomenology of seeing them in familiar order . Reversal errors remained frequent even when participants obtained perceptual experience of reverse-display items by beginning with a block of longer-duration trials. A forced-choice order-detection procedure reduced but did not eliminate reversal errors, showing that “fast pairs” is a robust perceptual illusion. Even adjective + noun pairs showed reversal errors, and reversal (...) errors increased with the log frequency of the word pair, consistent with a strong role for statistical processing at the level of multi-word units. (shrink)

In recent works, Kind has argued that imagination is a skill, since it possesses the two hallmarks of skill: improvability by practice, and control. I agree with Kind that and are indeed hallmarks of skill, and I also endorse her claim that imagination is a skill in virtue of possessing these two features. However, in this paper, I argue that Kind’s case for imagination’s being a skill is unsatisfactory, since it lacks robust empirical evidence. Here, I will provide evidence for (...) by considering data from mental rotation experiments and for by considering data from developmental experiments. I conclude that imagination is a skill, but there is a further pressing question of how the cognitive architecture of imagination has to be structured to make this possible. I begin by considering how can be implemented sub-personally. I argue that this can be accounted for by positing a selection mechanism which selects content from memory representations to be recombined into imaginings, using Bayesian generation. I then show that such an account can also explain. On this basis, I hold that not only is imagination a skill, but that it is also plausibly implemented sub-personally by a Bayesian selection mechanism. (shrink)