The Virtual Personalities Neural Network Model: Neurobiological Underpinnings

Abstract

The Virtual Personalities Model is a motive-based neural network model that provides both a psychological model and a computational implementation that explicates the dynamics and often large within-person variability in behavior that arises over time. At the same time the same model can produce -- across many virtual personalities – between subject variability in behavior that when factor analyzed yields familiar personality structure (e.g., the Big-5). First, we describe our personality model and its implementation as a neural network model. Second, we focus on detailing the neurobiological underpinnings of this model. Third, we examine the learning mechanisms, and their biological substrates, as ways that the model gets “wired up”, discussing Pavlovian and instrumental conditioning, Pavlovian to instrumental transfer (PIT), and habits. Finally, we describe the dynamics of how initial differences in propensities (e.g., dopamine functioning), wiring differences due to experience, and other factors could operate together to develop and change personality over time, and how this might be empirically examined. Thus, our goal is to contribute to the rising chorus of voices seeking a more precise neurobiologically-based science of the complex dynamics underlying personality.

The Dynamics of Personality Approach (DPA): 20 Tenets for Uncovering the Causal Mechanisms of Personality

“…The instruments developed on the basis of this taxonomy (e.g. Rauthmann et al, 2014) have already been used to describe, predict, or understand, for example, situations encapsulated in tweets (Serfass & Sherman, 2015), changes of situations within and between people (Rauthmann & Sherman, 2016), mean–level changes of situation characteristics across the lifespan (Brown & Rauthmann, 2016), and affect and self–reported behaviours in experience sampling studies (Sherman, Rauthmann, Brown, Serfass, & Jones, 2015).…”

“…Of special interest to the DPA are dynamic network modelling approaches to situations within everyday life, typically using the experience sampling method (Rauthmann & Sherman, 2016; Sherman et al, 2015). In this method, participants are prompted by a smartphone app or another device to provide short ratings on their experience and behaviour at intervals across the day for some extent of time (e.g.…”

“…Hull, 1943). One way in which individual differences can be represented is by differing strengths of specific motive goals and by baseline activations of these two motive systems (Read, Brown, Wang, & Miller, 2018). As behaviour may successfully arrive at the consummation of a goal (e.g.…”

Causal Inference in Generalizable Environments: Systematic Representative Design

“…As suggested earlier, this seems analogous to the "model system" concept so critical in modern biology. 46 In addition, because computational models can be used across scale (e.g., the interpersonal level, the individual level, and the neural level) to address personality and social psychological dynamics in producing emergent behavior (e.g., Read, Brown, Wang, & Miller, 2018), they can also suggest (across scale, for example in fMRI studies) what to measure and afford in building SRD. 47 In short, computational modeling, since it requires the math and precision to build, provides psychologists with new methods in our toolkit for illuminating hidden assumptions and theoretical gaps, while also affording ways to iteratively build and improve SRD as well as testable theories (Marsella & Gratch, 2016;Marsella, Gratch, & Petta, 2010;Vallacher, Read, & Nowak, 2017;Farrell & Lewandowsky, 2018) 48 It can take a decade or more for basic science to produce useful applications (Morris, Wooding, & Grant, 2011).…”

Development of a computer habit-forming algorithm

“…Основные претенденты -это области полосатого тела и коры, а главный кандидат на роль ключевого нейромедиаторадофамин (например, [4]). Что касается непосредственно механизма формирования привычки (например, демонстрирующего, как вещества перемещаются из клетки в клетку и возникает привычка, или какого-то иного), то вариантов предлагается очень мало [9].…”