Brian M. Monroe

A neural network model of the structure and dynamics of human personality.

We present a neural network model that aims to bridge the historical gap between dynamic and structural approaches to personality. The model integrates work on the structure of the trait lexicon, the neurobiology of personality, temperament, goal-based models of personality, and an evolutionary analysis of motives. It is organized in terms of two overarching motivational systems, an approach and an avoidance system, as well as a general disinhibition and constraint system. Each overarching motivational system influences more specific motives. Traits are modeled in terms of differences in the sensitivities of the motivational systems, the baseline activation of specific motives, and inhibitory strength. The result is a motive-based neural network model of personality based on research about the structure and neurobiology of human personality. The model provides an account of personality dynamics and person-situation interactions and suggests how dynamic processing approaches and dispositional, structural approaches can be integrated in a common framework.

A general connectionist model of attitude structure and change: the ACS (Attitudes as Constraint Satisfaction) model.

A localist, parallel constraint satisfaction, artificial neural network model is presented that accounts for a broad collection of attitude and attitude-change phenomena. The network represents the attitude object and cognitions and beliefs related to the attitude, as well as how to integrate a persuasive message into this network. Short-term effects are modeled by activation patterns due to parallel constraint satisfaction processes, and long-term effects are modeled by weight changes due to the settling patterns of activation. Phenomena modeled include thought-induced attitude polarization, elaboration and attitude strength, motivated reasoning and social influence, an integrated view of heuristic versus systematic persuasion, and implicit versus explicit attitude change. Results of the simulations are consistent with empirical results. The same set of simple mechanisms is used to model all the phenomena, which allows the model to offer a parsimonious theoretical account of how structure can impact attitude change. This model is compared with previous computational approaches to attitudes, and implications for attitude research are discussed.

Modeling the Dynamics of Evaluation: A Multilevel Neural Network Implementation of the Iterative Reprocessing Model

We present a neural network implementation of central components of the iterative reprocessing (IR) model. The IR model argues that the evaluation of social stimuli (attitudes, stereotypes) is the result of the IR of stimuli in a hierarchy of neural systems: The evaluation of social stimuli develops and changes over processing. The network has a multilevel, bidirectional feedback evaluation system that integrates initial perceptual processing and later developing semantic processing. The network processes stimuli (e.g., an individual’s appearance) over repeated iterations, with increasingly higher levels of semantic processing over time. As a result, the network’s evaluations of stimuli evolve. We discuss the implications of the network for a number of different issues involved in attitudes and social evaluation. The success of the network supports the IR model framework and provides new insights into attitude theory.

The Cambridge Handbook of Computational Psychology: Computational Models in Personality and Social Psychology

This chapter focuses on cognitive as opposed to sensori-motor skills and on models that create or alter symbolic knowledge representations. It deals briefly with models that learn by adjusting quantitative properties of knowledge structures. Although occasionally referring to empirical studies, the chapter is primarily a review of theoretical concepts. It proceeds on the assumption that each hypothesis contains some grain of truth to be extracted and incorporated into future models. The learning mechanism is a more finegrained unit than the model or the cognitive architecture. Cognitive descriptions of processes in the mind are functional descriptions of what this or that piece of wetware is doing, what function it carries out. This perspective points to the need to understand the relation between learning mechanisms and modes of neural plasticity.

Must Judgments About Intentionality Precede Dispositional Inference

Reeder’s multiple inference model (MIM) is a great advance over the central theories of person perception and trait inference, and he and his colleagues have conducted a body of research that makes a convincing case for this approach. His work argues that social psychologists need to seriously rethink how we conceptualize person perception and the trait inference process. We are particularly impressed with the work he has done demonstrating that situational constraints often directly and positively increase the likelihood that perceivers will make motivational inferences, and as a result increases the likelihood that perceivers will make trait inferences related to those motives. Reeder’s work demonstrates that this process is often not the widely accepted hydraulic process where situational constraints and motivations trade off, but instead a process in which situational factors often increase the likelihood that certain motivational and dispositional factors are seen as the reasons for behavior. Reeder’s work takes direct aim at what has become gospel in the attribution area: that situational factors have an inverse relationship with motivational inferences and trait inferences. In contrast to that position, he has shown that in many cases situational factors directly activate motivations and thereby increase the likelihood of making dispositional inferences. Although we think this work is outstanding, we do have some issues with his characterization of both the causal attribution process and the motivational/dispositional inference process, in particular his arguments about the role of judgments of intentionality. In our commentary we first make a minor observation concerning Reeder’s history of the field’s treatment of dispositional inference processes and then focus on three aspects of his argument about the nature of the dispositional inference process:

A neurobiologically inspired model of personality in an intelligent agent

We demonstrate how current knowledge about the neurobiology and structure of human personality can be used as the basis for a computational model of personality in intelligent agents (PAC-personality, affect, and culture). The model integrates what is known about the neurobiology of human motivation and personality with knowledge about the psychometric structure of trait language and personality tests. Thus, the current model provides a principled theoretical account that is based on what is currently known about the structure and neurobiology of human personality and tightly integrates it into a computational architecture. The result is a motive-based computational model of personality that provides a psychologically principled basis for intelligent virtual agents with realistic and engaging personality.