Matt Williams’s research while affiliated with Massey University and other places

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Publications (7)


Quantifying the Affective Dynamics of Pornography Use and Masturbation: An Ecological Momentary Assessment Study
  • Preprint
  • File available

September 2024

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210 Reads

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Matt Williams

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The causal relationships between pornography use, masturbation, moral incongruence, and mental health are poorly understood. While the link between problematic pornography use (PPU) and depression is well documented, the affective dynamics (i.e., emotional shifts over time) associated with pornography use and masturbation have not yet been quantified. Utilizing an Ecological Momentary Assessment (EMA) design, we measured the affective dynamics of mental health variables collected from 22 participants before, during and after pornography use and masturbation, and examined the moderating role of moral incongruence in these relationships. Participants completed an initial survey followed by a four-week EMA, capturing data on sexual activities and mental health variables. Bayesian hierarchical mixed-effects models were employed to analyse affective dynamics. Findings suggest that pornography use and masturbation were linked to changes in affective states that spiked both before and after sexual episodes. The magnitude of these state changes was greater in participants with high moral incongruence, who experienced increases in guilt, shame, loneliness, and difficulty thinking, along with decreased hedonic mood and perception of relationship connectedness, either before or after sexual episodes. Our findings signalled the potential for intermittent spiking effects in craving prior to sexual episodes, as well as potential evidence for ‘brain fog’ following pornography use in both low and high moral incongruence participants. Further, we discovered opponent process dynamics in the mood of high moral incongruence participants, providing a possible causal mechanism that may explain how PPU can lead to depression.

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A Hormetic Approach to the Value-Loading Problem: Preventing the Paperclip Apocalypse?

February 2024

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7 Reads

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Matt Williams

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The value-loading problem is a significant challenge for researchers aiming to create artificial intelligence (AI) systems that align with human values and preferences. This problem requires a method to define and regulate safe and optimal limits of AI behaviors. In this work, we propose HALO (Hormetic ALignment via Opponent processes), a regulatory paradigm that uses hormetic analysis to regulate the behavioral patterns of AI. Behavioral hormesis is a phenomenon where low frequencies of a behavior have beneficial effects, while high frequencies are harmful. By modeling behaviors as allostatic opponent processes, we can use either Behavioral Frequency Response Analysis (BFRA) or Behavioral Count Response Analysis (BCRA) to quantify the hormetic limits of repeatable behaviors. We demonstrate how HALO can solve the 'paperclip maximizer' scenario, a thought experiment where an unregulated AI tasked with making paperclips could end up converting all matter in the universe into paperclips. Our approach may be used to help create an evolving database of 'values' based on the hedonic calculus of repeatable behaviors with decreasing marginal utility. This positions HALO as a promising solution for the value-loading problem, which involves embedding human-aligned values into an AI system, and the weak-to-strong generalization problem, which explores whether weak models can supervise stronger models as they become more intelligent. Hence, HALO opens several research avenues that may lead to the development of a computational value system that allows an AI algorithm to learn whether the decisions it makes are right or wrong.


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Reducing Echo Chamber Effects: An Allostatic Regulator for Recommendation Algorithms

December 2023

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74 Reads

Recommendation systems are prevalent on the Internet but are prone to feedback loops that cause ‘echo chamber’ effects. We present an allostatic regulator for recommendation systems based on opponent process theory and behavioral posology principles to combat these effects. When applied as a code wrapper to a supervised K-Nearest Neighbors algorithm for movie recommendations, our prototype algorithm can dynamically restrict the proportion of potentially harmful content recommended to users. This technique is adaptable to other domains and is scalable to more complex machine learning algorithms with minimal changes to their internal parameters. Combining allostatic regulation with insights from human-derived models on the healthy limits of digital content consumption may provide app developers with a flexible tool to help users regulate their online experiences. In turn, allostatic regulation moderates positive feedback loops, potentially reducing echo chamber effects while increasing the transparency and interpretability of machine learning models.


Generic graphs of the Linear No Threshold model (LNT, dotted line), Linear With Threshold model (LWT, dashed line), and hormesis (solid line) for both drug‐based and behavioral contexts. NOAEL = no adverse effect level (also known as the hormetic threshold), presented for the hormetic curve only.
Hypothesized compartment model representing the cycle of a repeated digital behavior, potentially leading to addiction. Compartments linked by dotted lines were not modeled but demonstrate how the cycle of addiction could continue.
Model showing how summed a‐ and b‐processes can generate an overall decrease in mood (allostasis) over time at high dose frequencies. a,b) Biophase graphs based on Hill equation parameters. c) mrgsolve‐simulated compartment values for apk,bpk,apd${a_{pk}},{b_{pk}},\;{a_{pd}}$ and bpd${b_{pd}}$. d) Simulated compartment values for Ha,b(t)total${H_{a,b}}{( t )_{total}}$, showing allostasis and reduced average mood over time.
Simulation for varying levels of EC50b$E{C_{{{50}_b}}}$ (shades of blue), representing different levels of moral incongruence. a,b) Biophase curves for a‐ and b‐processes. c,d) Ha,b(t)total${H_{a,b}}{( t )_{total}}$ scores generated by mrgsolve. At a dose frequency of 0.0015 min⁻¹, b‐processes decay to homeostatic levels, but at a higher dose frequency of 0.006 min⁻¹, allostasis is observed at all levels of EC50b.$E{C}_{{50}_{b}}.\hspace*{0.28em}$e) BFRA curves plotted as a function of dose frequency at each level of EC50b$E{C_{{{50}_b}}}$.
Simulation of Solomon and Corbit's "standard pattern of affective dynamics" (dark red; EC50a$E{C_{{{50}_a}}}$ = 3, EC50b$E{C_{{{50}_b}}}$ = 35, ka,pk${k_{a,pk}}$ = 0.02, kb,pk${k_{b,pk}}$ = 0.005), and tolerance‐induced affective dynamics (light red; EC50a$E{C_{{{50}_a}}}$ = 3, EC50b$E{C_{{{50}_b}}}$ = 35, ka,pk${k_{a,pk}}$ = 0.04, kb,pk${k_{b,pk}}$ = 0.004). At a dose frequency of 0.2 min⁻¹ (near‐continuous dose), the individual opponent processes appear to merge into a single dynamic process, and the graph of Ha,b(t)${H}_{a,b}(t)$ (d) demonstrates the following distinctive features of affective dynamics: peak of primary affective reaction (1), adaptation phase (2), steady‐state (3), peak of affective after‐reaction (4), and decay of after‐reaction (5).
Behavioral Posology: A Novel Paradigm for Modeling the Healthy Limits of Behaviors

July 2023

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48 Reads

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3 Citations

One of the challenges faced by behavioral scientists is the lack of modeling methodologies for accurately determining when a behavior becomes problematic. The authors propose “behavioral posology” as a novel modeling paradigm for quantifying the healthy limits of behaviors through the concept of behavioral dose. As an example of this paradigm, a pharmacokinetic/pharmacodynamic model of a hypothetical digital behavior is presented, based on opponent process theory. The generic model can be adapted to simulate Solomon and Corbit's model of affective dynamics from 1974, and the model predicts features of addiction such as hedonic allostasis, withdrawal, and apparent tolerance. A behavioral frequency response analysis (BFRA) of the model demonstrates how behavior repetition may result in a hormetic dose–response relationship that depends on the frequency of the behavior. The model can be experimentally validated using Ecological Momentary Assessment, allowing researchers to hypothesize, model, and test causal mechanisms for behavioral addictions. The potential for behavioral posology to be applied as a clinical support tool in psychological medicine is discussed, as this modeling framework may help to detect and limit behaviors being performed too frequently based on factors such as the person's moral beliefs.


Behavioral Posology: A Novel Paradigm for Modeling Digital Addictions

March 2023

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6 Reads

We introduce ‘behavioral posology’ as a novel modeling paradigm to analyze digital addictions through the concept of behavioral dose. As an example of this paradigm, a pharmacokinetic/pharmacodynamic model of a hypothetical digital behavior is presented, based on opponent process theory. Our simulation results replicate Solomon & Corbit’s model of affective dynamics from 1974, and the model predicts features of addiction such as hedonic allostasis, withdrawal, and apparent tolerance. Using Frequency Response Analysis, we show how behavior repetition may result in a hormetic dose-response relationship that depends on the frequency of the behavior and is moderated by the subject’s moral beliefs. The model can be experimentally validated using Ecological Momentary Assessment, allowing researchers to hypothesize, model, and test causal mechanisms for behavioral addictions. We also discuss the potential for behavioral posology as a clinical support tool in psychiatry, as this modeling framework may help to detect and limit behaviors being performed too frequently based on the person’s moral beliefs.


Figure 1. Flow diagram illustrating exclusion of apps at various stages of the study.
Figure 2. Number of apps containing each feature for both Android and iOS, and minimum possible number of installations for each feature (Android only).
Figure 3. Combinatorial plot showing the most common combinations of features. Only the top 20 combinations are shown.
Figure 4. Distribution of ratings for apps with each feature, for Android and iOS, ordered by mean rating. Only apps that had received at least five ratings were recorded for each feature, and only features with at least five eligible app ratings were plotted.
mHealth Technologies for Managing Problematic Pornography Use: A Content Analysis (Preprint)

May 2022

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77 Reads

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5 Citations

JMIR Formative Research

Background: Several mobile apps are currently available that purportedly help with managing pornography addiction. However, the utility of these apps is unclear, given the lack of literature on the effectiveness of mobile health solutions for problematic pornography use. Little is also known about the content, structure, and features of these apps. Objective: This study aims to characterize the purpose, content, and popularity of mobile apps that claim to manage pornography addiction. Methods: The phrase "pornography addiction" was entered as a search term in the app stores of the two major mobile phone platforms (Android and iOS). App features were categorized according to a coding scheme that contained 16 categories. Apps were included in the analysis if they were described as helpful for reducing pornography use, and data were extracted from the store descriptions of the apps. Metrics such as number of user ratings, mean rating score, and number of installations were analyzed on a per-feature basis. Results: In total, 170 apps from both app stores met the inclusion criteria. The five most common and popular features, both in terms of number of apps with each feature and minimum possible number of installations, were the ability to track the time since last relapse (apps with feature=72/170, 42.4%; minimum possible number of installations=6,388,000), tutorials and coaching (apps with feature=63/170, 37.1%; minimum possible number of installations=9,286,505), access to accountability partners or communities (apps with feature=51/170, 30%; minimum possible number of installations=5,544,500), content blocking or content monitoring (apps with feature=46/170, 27.1%; minimum possible number of installations=17,883,000), and a reward system for progress (apps with feature=34/170, 20%; minimum possible number of installations=4,425,300). Of these features, content-blocking apps had the highest minimum possible number of installations. Content blocking was also the most detected feature combination in a combinatorial analysis (with 28 apps having only this feature), but it also had the lowest mean consumer satisfaction rating (4.04) and second-lowest median rating (4.00) out of 5 stars. None of the apps reviewed contained references to literature that provided direct evidence for the app's efficacy or safety. Conclusions: There are several apps with the potential to provide low- or zero-cost real-time interventions for people struggling to manage problematic pornography use. Popular app features include blockers of pornographic content, behavior monitoring, and tutorials that instruct users how to eliminate pornography use. However, there is currently no empirical evidence to support the effectiveness and safety of these apps. Further research is required to be able to provide recommendations about which apps (and app features) are safe for public consumption.


m-Health Technologies for Managing Problematic Pornography Use: A Literature Review and Content Analysis of Current Apps (Preprint)

May 2022

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3 Reads

BACKGROUND Several mobile apps are currently available that purportedly help with managing pornography addiction. However, the utility of these apps is unclear, given the lack of literature on the effectiveness of m-Health solutions for problematic pornography use (PPU). OBJECTIVE This study aimed to characterize the purpose and content of mobile apps that claim to manage pornography addiction. METHODS The phrase ‘pornography addiction’ was entered as a search term for the two major mobile phone platforms (Android and iOS). Apps were included in the analysis if they were described as reducing pornography use, and data were extracted from the store descriptions of the apps. RESULTS In total, 170 apps from both app stores met the inclusion criteria. The five most common and popular features, both in terms of number of apps with each feature (NAF) and minimum possible number of installations (MPNI) were the ability to track the time since last relapse (NAF=42%, 72/170; MPNI=6,388,000), tutorials and coaching (NAF=37%, 63/170; MPNI=9,286,505), access to accountability partners or communities (NAF=30%, 51/170; MPNI=5,544,500), content blocking or monitoring (NAF=27%, 46/170; MPNI=17,883,000), and badges (NAF=20%, 34/170; MPNI=4,425,300). Of these features, content blocking or monitoring had the highest MPNI. Content blocking was also the most detected feature combination in a combinations analysis (with 28 apps having only this feature), but also had the lowest mean consumer satisfaction rating (4.04) and second-lowest median rating (4.00) out of 5 stars. CONCLUSIONS There are several apps with the potential to provide low- or zero-cost, real-time interventions for people struggling to manage PPU, particularly those who are unable or unwilling to access a therapist. However, there is currently little evidence to support the effectiveness and safety of these apps. Further research is required to be able to provide recommendations about which apps (and app features) are safe for public consumption. This research strategy may enable the creation of combination therapies that employ a ‘Defence in Depth’ approach to manage PPU. CLINICALTRIAL pornography; pornography addiction; m-Health; problematic pornography use (PPU); mobile intervention; Just-In-Time Adaptive Intervention; smartphone-based therapy; addiction; psychology; Internet addiction

Citations (2)


... We propose that curvilinear decay might be seen in individuals after engaging in pornography use or masturbation. By treating sexual activity as a stimulus that generates affective opponent processes in the body (Henry et al., 2023;Solomon & Corbit, 1974), we hypothesized that an EMA would allow us to observe and quantify both the emotional high during a sexual episode (the 'a-process'), and the negative emotional after-effects (the 'b-process') (see Henry et al., 2023 for a more detailed explanation). We also hypothesized that these after-effects would follow an exponential decay curve. ...

Reference:

Quantifying the Affective Dynamics of Pornography Use and Masturbation: An Ecological Momentary Assessment Study
Behavioral Posology: A Novel Paradigm for Modeling the Healthy Limits of Behaviors

... No self pleasure AT ALL" (Rhodes and Ohropax 2011). NoFap followers are encouraged to use counter applications to display their days "clean" from PMO. Counter-applications currently have no data to support their efficacy in decreasing PMO and have raised serious privacy concerns (Henry et al. 2022). The abstinence period in NoFap is called "Rebooting," which leaders claim will return the brain to its state prior to engaging in sexual behavior(s). ...

mHealth Technologies for Managing Problematic Pornography Use: A Content Analysis (Preprint)

JMIR Formative Research