A reexamination of the faithful subject role

Fillenbaum (Journal of Personality and Social Psychology, 1966, 4, 532–537; Fillenbaum & Frey, Journal of Personality, 1970, 38, 43–51) has proposed that a relatively large number of subjects within certain experiments will adopt a faithful subject role, that is, they will intentionally avoid basing their behavior on any suspicions they may have regarding the experimenter's hypothesis. However, examination of the studies on which this conclusion was based casts doubt on whether Fillenbaum's subjects were truly faithful or whether they may have become aware of the nature of the deception after all opportunity for awareness to influence their responses had passed. To test this hypothesis, awareness measures were administered to subjects either before they took an incidental learning test or (as in Fillenbaum's studies) after the test. As predicted, fewer subjects were classified as faithful in the first condition than in the second. It was concluded that, in fact, very few if any subjects are actively faithful. Discussion also concerned the problems associated with role analyses of subject behavior.     

Emotions and their computations: Three computer models

Abstract

Three computational models: a narrative reader (BORIS), an editorial reader (OpEd), and a stream of thought generator (DAYDREAMER), are presented and discussed, with specific focus on the emotion-related processing and representational elements of each. These models exhibit comprehension and/or generation of emotional behaviour through the interaction of cognitive processes (memory retrieval, planning, and reasoning) over intentional constructs (goals and beliefs).     

A clarification of self-terminating versus exhaustive variances in serial and parallel models

A recent study published in Perception & Psychophysics (Donnelly, Found, & Miller, 1999) employs response time (RT) variances (in the form of standard deviations) in addition to mean RTs. Variances can contribute greatly to model testing. However, there is a danger of perpetuating the kinds of logical and methodological errors that have long attended research employing mean RTs alone. This commentary clarifies the theoretical and methodological issues, points out some new results concerning variability in search processes, and indicates how to resolve the global and specific challenges associated with identifying psychological mechanisms.     

A clarification of self-terminating versus exhaustive variances in serial and parallel models

A recent study published in Perception & Psychophysics (Donnelly, Found, & Müller, 1999) employs response time (RT) variances (in the form of standard deviations) in addition to mean RTs. Variances can contribute greatly to model testing. However, there is a danger of perpetuating the kinds of logical and methodological errors that have long attended research employing mean RTs alone. This commentary clarifies the theoretical and methodological issues, points out some new results concerning variability in search processes, and indicates how to resolve the global and specific challenges associated with identifying psychological mechanisms.     

Merging race models and adaptive networks: A parallel race network

This article presents a generalization of race models involving multiple channels. The major contribution of this article is the implementation of a learning rule that enables networks based on such a parallel race model to learn stimulus—response associations. This model is called aparallel race network. Surprisingly, with a two-layer architecture, a parallel race network learns the XOR problem without the benefit of hidden units. The model described here can be seen as a reduction-of-information system (Haider & Frensch, 1996). An emergent property of this model is seriality: In some conditions, responses are performed with a fixed order, although the system is parallel. The mere existence of this supervised network demonstrates that networks can perform cognitive processes without the weighted sum metric that characterizes strength-based networks.     

"Serial" effects in parallel models of reading

There is now considerable evidence showing that the time to read a word out loud is influenced by an interaction between orthographic length and lexicality. Given that length effects are interpreted by advocates of dual-route models as evidence of serial processing this would seem to pose a serious challenge to models of single word reading which postulate a common parallel processing mechanism for reading both words and nonwords (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Rastle, Havelka, Wydell, Coltheart, & Besner, 2009). However, an alternative explanation of these data is that visual processes outside the scope of existing parallel models are responsible for generating the word-length related phenomena (Seidenberg & Plaut, 1998). Here we demonstrate that a parallel model of single word reading can account for the differential word-length effects found in the naming latencies of words and nonwords, provided that it includes a mapping from visual to orthographic representations, and that the nature of those orthographic representations are not preconstrained. The model can also simulate other supposedly "serial" effects. The overall findings were consistent with the view that visual processing contributes substantially to the word-length effects in normal reading and provided evidence to support the single-route theory which assumes words and nonwords are processed in parallel by a common mechanism.     

Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology.     

Using parent-infant dyads as models for interpreting the parallel process

Studies indicate that parents replicate with the therapist the behaviors and perceptions they experience with their infants. During parent-infant therapy, the therapist may validate perceptions by comparing the parent's behaviors with verbal disclosures. A similar phenomenon occurs in psychotherapeutic supervision. Therapists in training tend to replicate with the supervisor the interactions of their patients, a phenomenon labeled parallel process.This paper suggests that parent-infant interaction may provide insight into the therapistpatient and therapist-supervisor relationships. Understanding the parallel process that occurs during parent-infant therapy may help the therapist explore the patient's perceptions and resolve conflict.     

Primitive computations in speech processing

Previous research suggests that artificial-language learners exposed to quasi-continuous speech can learn that the first and the last syllables of words have to belong to distinct classes (e.g., Endress & Bonatti, 2007; Peña, Bonatti, Nespor, & Mehler, 2002). The mechanisms of these generalizations, however, are debated. Here we show that participants learn such generalizations only when the crucial syllables are in edge positions (i.e., the first and the last), but not when they are in medial positions (i.e., the second and the fourth in pentasyllabic items). In contrast to the generalizations, participants readily perform statistical analyses also in word middles. In analogy to sequential memory, we suggest that participants extract the generalizations using a simple but specific mechanism that encodes the positions of syllables that occur in edges. Simultaneously, they use another mechanism to track the syllable distribution in the speech streams. In contrast to previous accounts, this model explains why the generalizations are faster than the statistical computations, require additional cues, and break down under different conditions, and why they can be performed at all. We also show that that similar edge-based mechanisms may explain many results in artificial-grammar learning and also various linguistic observations.     

Cupid: A program for computations with probability distributions

This article describes Cupid, a program that performs computations with univariate probability distributions. Cupid can be useful in stochastic modeling and simulation, hypothesis testing, and examination of statistical procedures. The program runs on IBM-PC compatibles under the DOS operating system, and it is free for educational and noncommercial use.     

Neural computations of decision utility

How are decision alternatives represented in the primate brain? A recent study by Sugrue et al. sought to answer this question by integrating behavioral, computational and physiological methods in examining the choice patterns of monkeys placed in a dynamic foraging environment. They observed specific encoding of the relative value of alternatives by neurons in the parietal cortex, providing an important starting point for researchers interested in how value and probability are combined in the brain to arrive at decision outcomes.     

Flustered and faithful: embarrassment as a signal of prosociality

Although individuals experience embarrassment as an unpleasant, negative emotion, the authors argue that expressions of embarrassment serve vital social functions, signaling the embarrassed individual's prosociality and fostering trust. Extending past research on embarrassment as a nonverbal apology and appeasement gesture, the authors demonstrate that observers recognize the expression of embarrassment as a signal of prosociality and commitment to social relationships. In turn, observers respond with affiliative behaviors toward the signaler, including greater trust and desire to affiliate with the embarrassed individual. Five studies tested these hypotheses and ruled out alternative explanations. Study 1 demonstrated that individuals who are more embarrassable also reported greater prosociality and behaved more generously than their less embarrassable counterparts. Results of Studies 2-5 revealed that observers rated embarrassed targets as being more prosocial and less antisocial relative to targets who displayed either a different emotion or no emotion. In addition, observers were more willing to give resources and express a desire to affiliate with these targets, and these effects were mediated by perceptions of the targets as prosocial.     

Base-rate effects in category learning: a comparison of parallel network and memory storage-retrieval models

Exemplar-memory and adaptive network models were compared in application to category learning data, with special attention to base rate effects on learning and transfer performance. Subjects classified symptom charts of hypothetical patients into disease categories, with informative feedback on learning trials and with the feedback either given or withheld on test trials that followed each fourth of the learning series. The network model proved notably accurate and uniformly superior to the exemplar model in accounting for the detailed course of learning; both the parallel, interactive aspect of the network model and its particular learning algorithm contribute to this superiority. During learning, subjects' performance reflected both category base rates and feature (symptom) probabilities in a nearly optimal manner, a result predicted by both models, though more accurately by the network model. However, under some test conditions, the data showed substantial base-rate neglect, in agreement with Gluck and Bower (1988b).     

Epistemic representation, informativeness and the aim of faithful representation

In this paper, I take scientific models to be epistemic representations of their target systems. I define an epistemic representation to be a tool for gaining information about its target system and argue that a vehicle’s capacity to provide specific information about its target system—its informativeness—is an essential feature of this kind of representation. I draw an analogy to our ordinary notion of interpretation to show that a user’s aim of faithfully representing the target system is necessary for securing this feature.     

A visual embedding for the unsupervised extraction of abstract semantics

Vector-space word representations obtained from neural network models have been shown to enable semantic operations based on vector arithmetic. In this paper, we explore the existence of similar information on vector representations of images. For that purpose we define a methodology to obtain large, sparse vector representations of image classes, and generate vectors through the state-of-the-art deep learning architecture GoogLeNet for 20 K images obtained from ImageNet. We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics. We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances (e.g., 118 dog types). More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge (e.g., living things). Afterwards, we consider vector arithmetics. Although we are unable to obtain meaningful results on this regard, we discuss the various problem we encountered, and how we consider to solve them. Finally, we discuss the impact of our research for cognitive systems, focusing on the role of the architecture being used.     

Algorithms and computations for foldedness of P-ideals in BCI-algebras

In this paper, we introduce the notion of n-folds P-ideals in BCI-algebras which is a natural generalization of P-ideals in BCI-algebras. Via the concept of fuzzy point, we establish several characterizations of n-fold and fuzzy n-fold P-ideals in BCI-algebras. Furthermore, we construct some algorithms for folding theory applied to P-ideals in BCI-algebras.     

The ontogeny of cumulative culture: Individual toddlers vary in faithful imitation and goal emulation

The success of human culture depends on early emerging mechanisms of social learning, which include the ability to acquire opaque cultural knowledge through faithful imitation, as well as the ability to advance culture through flexible discovery of new means to goal attainment. This study explores whether this mixture of faithful imitation and goal emulation is based in part on individual differences which emerge early in ontogeny. Experimental measurements and parental reports were collected for a group of 2‐year‐old children (N = 48, age = 23–32 months) on their imitative behavior as well as other aspects of cognitive and social development. Results revealed individual differences in children's imitative behavior across trials and tasks which were best characterized by a model that included two behavioral routines; one corresponding to faithful imitation, and one to goal emulation. Moreover, individual differences in faithful imitation and goal emulation were correlated with individual differences in theory of mind, prosocial behavior, and temperament. These findings were discussed in terms of their implications for understanding the mechanisms of social learning, ontogeny of cumulative culture, and the benefit of analyzing individual differences for developmental experiments.