Parallel distributed processing and neuropsychology: A neural network model of Wisconsin card sorting and verbal fluency

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.     

The Role of Emotional Content and Perceptual Saliency During the Programming of Saccades Toward Faces

Previous studies have shown that the human visual system can detect a face and elicit a saccadic eye movement toward it very efficiently compared to other categories of visual stimuli. In the first experiment, we tested the influence of facial expressions on fast face detection using a saccadic choice task. Face-vehicle pairs were simultaneously presented and participants were asked to saccade toward the target (the face or the vehicle). We observed that saccades toward faces were initiated faster, and more often in the correct direction, than saccades toward vehicles, regardless of the facial expressions (happy, fearful, or neutral). We also observed that saccade endpoints on face images were lower when the face was happy and higher when it was neutral. In the second experiment, we explicitly tested the detection of facial expressions. We used a saccadic choice task with emotional-neutral pairs of faces and participants were asked to saccade toward the emotional (happy or fearful) or the neutral face. Participants were faster when they were asked to saccade toward the emotional face. They also made fewer errors, especially when the emotional face was happy. Using computational modeling, we showed that this happy face advantage can, at least partly, be explained by perceptual factors. Also, saccade endpoints were lower when the target was happy than when it was fearful. Overall, we suggest that there is no automatic prioritization of emotional faces, at least for saccades with short latencies, but that salient local face features can automatically attract attention.     

A comparison of accumulated and distributed reinforcement periods with children exhibiting escape-maintained problem behavior

Differential reinforcement is a common treatment for escape-maintained problem behavior in which compliance is reinforced on a fixed-ratio (FR) 1 schedule with brief access to positive and/or negative reinforcement. Recent research suggests some individuals prefer to complete longer work requirements culminating in prolonged (i.e. accumulated) reinforcement periods relative to brief (i.e. distributed) periods, but prolonged work exposure may evoke problem behavior and prevent compliance from contacting reinforcement when treating escape-maintained problem behavior. We exposed 3 children with escape-maintained problem behavior to both distributed (FR 1 resulting in 30 s of reinforcement) and accumulated (FR 15 resulting in 7.5 min of reinforcement) arrangements to compare their efficacy in maintaining low levels of problem behavior. We then assessed participants' preferences for these conditions in a concurrent-chains arrangement. Accumulated-reinforcement arrangements did not occasion additional problem behavior, but rather resulted in consistently lower levels of problem behavior for 2 of 3 participants. Participants demonstrated idiosyncratic preferences.     

Learning Painting Styles: Spacing is Advantageous when it Promotes Discriminative Contrast

Repetitions that are distributed over time benefit long‐term retention more than when massed. Recent research has suggested that the advantage of spacing may extend to induction learning‐‐learners were better able to identify the artists of previously unseen paintings when, during training, artists' paintings were spaced (paintings by different artists were interleaved) rather than massed (a given artist's paintings were blocked and presented consecutively). Increasing temporal spacing between paintings while maintaining a presentation sequence that was blocked by artist produced test performance no better than massed presentation (both worse than interleaved presentation) (Experiment 1). Displaying paintings by different artists simultaneously produced test performance as good as interleaved presentation and better than massed presentation (Experiment 2). Our findings argue that spacing benefits perceptual induction learning not because of increased temporal spacing per se but rather because interleaving paintings by different artists enhances discriminative contrast between the artists' styles. Copyright © 2011 John Wiley & Sons, Ltd.     

Making too many enemies: Hutto and Myin’s attack on computationalism†

We analyse Hutto & Myin's three arguments against computationalism [Hutto, D., E. Myin, A. Peeters, and F. Zahnoun. Forthcoming. “The Cognitive Basis of Computation: Putting Computation In Its Place.” In The Routledge Handbook of the Computational Mind, edited by M. Sprevak, and M. Colombo. London: Routledge.; Hutto, D., and E. Myin. 2012. Radicalizing Enactivism: Basic Minds Without Content. Cambridge, MA: MIT Press; Hutto, D., and E. Myin. 2017. Evolving Enactivism: Basic Minds Meet Content. Cambridge, MA: MIT Press]. The Hard Problem of Content targets computationalism that relies on semantic notion of computation, claiming that it cannot account for the natural origins of content. The Intentionality Problem is targeted against computationalism using non-semantic accounts of computation, arguing that it fails in explaining intentionality. The Abstraction Problem claims that causal interaction between concrete physical processes and abstract computational properties is problematic. We argue that these arguments are flawed and are not enough to rule out computationalism.     

Is the Brain a Quantum Computer?

We argue that computation via quantum mechanical processes is irrelevant to explaining how brains produce thought, contrary to the ongoing speculations of many theorists. First, quantum effects do not have the temporal properties required for neural information processing. Second, there are substantial physical obstacles to any organic instantiation of quantum computation. Third, there is no psychological evidence that such mental phenomena as consciousness and mathematical thinking require explanation via quantum theory. We conclude that understanding brain function is unlikely to require quantum computation or similar mechanisms.     

Conceptual Structure and the Emergence of the Language Faculty: Much Ado about Knotting

Abstract

One perspective in contemporary linguistic theory defends the idea that the language faculty may result from the combinations of diverse systems and principles. As a case study, I critique a recent proposal by Juan Uriagereka and colleagues according to which the evolutionary emergence of the language faculty can be identified through studying the computational structure of knots as present within the fossil record. I here argue that the ability to conceptualize and, thereby, create knots is not parasitic on the ability to conceptualize and create language. On the contrary, these two domains are entirely distinct, unrelatable in terms of their computational complexity, expressive power or, most importantly, in terms of their requisite mental operations and principles. The overall approach defended here can profitably be employed in the study of the relationship between language and thought, as I briefly discuss in the case of the role of language in spatial reorientation.     

What is the Philosophy of Information?

Computational and information-theoretic research in philosophy has become increasingly fertile and pervasive, giving rise to a wealth of interesting results. In consequence, a new and vitally important field has emerged, the philosophy of information (PI). This essay is the first attempt to analyse the nature of PI systematically. PI is defined as the philosophical field concerned with the critical investigation of the conceptual nature and basic principles of information, including its dynamics, utilisation, and sciences, and the elaboration and application of information-theoretic and computational methodologies to philosophical problems. I argue that PI is a mature discipline for three reasons: it represents an autonomous field of research; it provides an innovative approach to both traditional and new philosophical topics; and it can stand beside other branches of philosophy, offering a systematic treatment of the conceptual foundations of the world of information and the information society.