Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

The organization of behavior: A neuropsychological theory

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The concept of mind

The self-medication hypothesis of addictive disorders derives primarily from clinical observations of patients with substance use disorders. Individuals discover that the specific actions or effects of each class of drugs relieve or change a range of painful affect states. Self-medication factors occur in a context of self-regulation vulnerabilities--primarily difficulties in regulating affects, self-esteem, relationships, and self-care. Persons with substance use disorders suffer in the extreme with their feelings, either being overwhelmed with painful affects or seeming not to feel their emotions at all. Substances of abuse help such individuals to relieve painful affects or to experience or control emotions when they are absent or confusing. Diagnostic studies provide evidence that variously supports and fails to support a self-medication hypothesis of addictive disorders. The cause-consequence controversy involving psychopathology and substance use/abuse is reviewed and critiqued. In contrast, clinical observations and empirical studies that focus on painful affects and subjective states of distress more consistently suggest that such states of suffering are important psychological determinants in using, becoming dependent upon, and relapsing to addictive substances. Subjective states of distress and suffering involved in motives to self-medicate with substances of abuse are considered with respect to nicotine dependence and to schizophrenia and posttraumatic stress disorder comorbid with a substance use disorder.

The Self-medication Hypothesis of Substance Use Disorders: A Reconsideration and Recent Applications

This article presents a comprehensive survey of research concerning interactions between associative learning and attention in humans. Four main findings are described. First, attention is biased toward stimuli that predict their consequences reliably (learned predictiveness). This finding is consistent with the approach taken by Mackintosh (1975) in his attentional model of associative learning in nonhuman animals. Second, the strength of this attentional bias is modulated by the value of the outcome (learned value). That is, predictors of high-value outcomes receive especially high levels of attention. Third, the related but opposing idea that uncertainty may result in increased attention to stimuli (Pearce & Hall, 1980), receives less support. This suggests that hybrid models of associative learning, incorporating the mechanisms of both the Mackintosh and Pearce-Hall theories, may not be required to explain data from human participants. Rather, a simpler model, in which attention to stimuli is determined by how strongly they are associated with significant outcomes, goes a long way to account for the data on human attentional learning. The last main finding, and an exciting area for future research and theorizing, is that learned predictiveness and learned value modulate both deliberate attentional focus, and more automatic attentional capture. The automatic influence of learning on attention does not appear to fit the traditional view of attention as being either goal-directed or stimulus-driven. Rather, it suggests a new kind of “derived” attention.

/pdf/attention-and-associative-learning-in-humans-an-integrative-1slykmhokk.pdf

Attention and associative learning in humans: An integrative review

1. Introduction Emmanuel M. Pothos and Andy J. Wills 2. The generalized context model: an exemplar model of classification Robert M. Nosofsky 3. Prototype models of categorization: basic formulation, predictions, and limitations John Paul Minda and J. David Smith 4. COVIS F. Gregory Ashby, Erick J. Paul and W. Todd Maddox 5. Semantics without categorization Timothy T. Rogers and James L. McClelland 6. Models of attentional learning John K. Kruschke 7. An elemental model of associative learning and memory Evan Livesey and Ian McLaren 8. Nonparametric Bayesian models of categorization Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, Daniel J. Navarro and Joshua B. Tenenbaum 9. The simplicity model of unsupervised categorization Emmanuel M. Pothos, Nick Chater and Peter Hines 10. Adaptive clustering models of categorization John V. McDonnell and Todd M. Gureckis 11. COBWEB models of categorization and probabilistic concept formation Wayne Iba and Pat Langley 12. The knowledge and resonance (KRES) model of category learning Harlan D. Harris and Bob Rehder 13. The contribution (and drawbacks) of models to the study of concepts Gregory L. Murphy 14. Formal models of categorization: insights from cognitive neuroscience Lukas Strnad, Stefano Anzellotti and Alfonso Caramazza 15. Comments on models and categorization theories: the razor's edge Douglas Medin.

/pdf/formal-approaches-in-categorization-1oqbk2ggxr.pdf

Formal approaches in categorization

Prediction error (“surprise”) affects the rate of learning: We learn more rapidly about cues for which we initially make incorrect predictions than cues for which our initial predictions are correct. The current studies employ electrophysiological measures to reveal early attentional differentiation of events that differ in their previous involvement in errors of predictive judgment. Error-related events attract more attention, as evidenced by features of event-related scalp potentials previously implicated in selective visual attention (selection negativity, augmented anterior N1). The earliest differences detected occurred around 120 msec after stimulus onset, and distributed source localization (LORETA) indicated that the inferior temporal regions were one source of the earliest differences. In addition, stimuli associated with the production of prediction errors show higher dwell times in an eye-tracking procedure. Our data support the view that early attentional processes play a role in human associative learning.

/pdf/predictive-learning-prediction-errors-and-attention-evidence-4d6h0jd3ns.pdf

Predictive Learning, Prediction Errors, and Attention: Evidence from Event-related Potentials and Eye Tracking

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).

/pdf/on-the-adequacy-of-current-empirical-evaluations-of-formal-26p9k1yczt.pdf

On the adequacy of current empirical evaluations of formal models of categorization.

It has been demonstrated that when people free classify stimuli presented simultaneously in an array, they have a preference to categorize by a single dimension. However, when people are encouraged to categorize items sequentially, they sort by “family resemblance,” grouping by overall similarity. The present studies extended this research, producing 3 main findings. First, the sequential procedure introduced by G. Regehr and L. R. Brooks (1995) does not always produce a preference for family resemblance sorts. Second, sort strategy in a sequential procedure is sensitive to subtle variations in stimulus properties. Third, spatially separable stimuli evoked more family resemblance sorts than stimuli of greater spatial integration. It is suggested that the family resemblance sorting observed is due to an analytic strategy. Categorization is a fundamental building block of everyday cognition; it is hard to imagine how people would function effectively without it. Categorization enables one to react to different objects in the same way and to make inferences about how novel objects should be treated. For instance, when a person sees a novel object, classifying it as a “dog” allows the person to deal with it in an appropriate manner. Because of the immense variety of discriminable objects that people encounter in the natural environment, it is necessary that this categorization process should be highly constrained, as there are virtually limitless numbers of ways in which objects can be partitioned. It is therefore of fundamental importance to understand the principles that underlie the categories that people have. Traditional categorization experiments give the participant itemby-item specific feedback about category membership. While such an approach is undoubtedly useful in examining category learning, it seems extremely unlikely that people receive this level of feedback anywhere other than in the laboratory. One way of addressing this issue is to examine how people spontaneously categorize a group of objects. This can be done by providing them with a group of stimuli and asking them to categorize them in the way that they think is most appropriate. No feedback, or other information, is given by the experimenter. Such an approach has variously been called category construction (e.g., Medin, Wattenmaker, & Hampson, 1987), free sorting (e.g., Bersted, Brown, & Evans, 1969), and free classification (e.g., Handel & Imai, 1972). It seems reasonable to assume that the categories we prefer to create would reflect the underlying structure of objects we encounter outside the laboratory. Over the years, there have been several

http://elliewills.co.uk/pubs/2004miltonwills.pdf

Andy J. Wills

Papers

Attention and associative learning in humans: An integrative review

Formal approaches in categorization

Predictive Learning, Prediction Errors, and Attention: Evidence from Event-related Potentials and Eye Tracking

On the adequacy of current empirical evaluations of formal models of categorization.

The influence of stimulus properties on category construction.