Delving Deeper Into The Salience Value of Online Porn

In a previous blog post, we argued the “Salience Value” of screen-based content could serve as a predictor of whether a screen user would fall into a “ludic loop” of problematic content consumption. In this post, we delve deeper into the concept of salience and how it may influence porn use and other screen content consumption behavior.

In their 2017 treatise Decision Neuroscience, researchers Kahnt and Tobler wrote: “At the most general level, salience can be defined as the capacity of a stimulus to direct attention.” They went on to describe, however, some confusion in the neuroscience literature between two concepts for which the term “salience” has been used as a shorthand. One of those concepts, “perceptual salience,” refers to “physical properties (i.e., the color, contrast, orientation, or luminance) that make [a stimulus] more likely to capture attention.” The other, “acquired salience,” refers to “the importance that a stimulus has acquired through association with an incentive outcome.” (It bears noting that both “perceptual” and “acquired” salience may have alternate definitions outside of the neuroscience context, amplifying the definitional ambiguity.)

In our blog post proposing a model for “ludic loops,” we posited (based mostly on anecdotal evidence) that screen-based stimuli – porn, social media, gaming, etc. – have a measurable Salience Value (SV), and that SV has a direct correlation to the likelihood of a problematic “ludic loop” use pattern arising. We hypothesized that the SV of screen-based content features at least two core components: (1) how the content is designed to attract attention, and (2) how the content responds to the user’s deep-seated or primordial needs. These two categories roughly correspond to the concepts of “salience” in neuroscience research articulated by Kahnt and Tobler. Screen content can be engineered to increase its “perceptual salience,” and can feature subject matter that varies in its “acquired salience” according to its fundamental importance to the user. 

Delving into those distinctions further, consider how the design engineering of screen-based porn (or social media or gaming) increases “perceptual salience”. Over the past year, ex-Silicon Valley engineers, among them former Google design ethicist Tristan Harris, have warned about the tricks and techniques app and web designers use to grab and hold the attention of content consumers. These include on-screen content placement and visual design, leveraging unpredictable rewards, auto-playing video content, infinite scrolling, and tracking user movements to design even more enticing, personalized future interactions. You don’t have to be a neuroscientist to recognize how effective these design features can be. Hands up if you’ve ever lost time scrolling through Instagram or playing Candy Crush. We can’t see you, but we know you have your hand up because we do, too.

Similarly, consider how degrees of “acquired salience” impact a user’s interaction with screen-based content. In our previous blog post, we contrasted the SV of porn with that of cute cat videos. Both may appear on screen in an identical, YouTube-style interface. Both likely feature design engineering calculated to grab and hold user attention. But, only one speaks to a primordial need – the sex drive – of most users. Kahnt and Tobler describe “acquired salience” as relating to the importance of a stimulus for motivated behavior independent” of whether its outcome is positive or negative for the content consumer. That makes sense in our model, because sexual content, whether it arouses or disgusts, holds an “absolute” value for most people that is far greater than more neutral content that  rarely arouses or disgusts, such as cat videos.  

Kahnt and Tobler also observe that “acquired salience” may vary according to learned expectations about the outcome tied to the stimulus. In the context of screen-based content, does this imply “acquired salience” can vary over time? Anecdotally, we suspect so. We think the “acquired salience” of a cute cat video be high initially (when a consumer finds himself entranced by the hilarity of watching a cat fall from a piano bench), but can decline predictably as the novelty of cat antics decreases. Conversely, the “acquired salience” of online porn sustains (or even grows) in intensity because of the near-infinite variety of pornographic genres, themes, and on-screen behaviors available for consumption. Simply put, online porn doesn’t just tickle a particularly potent human instinct, it also remains salient by offering the opportunity to discover new outcomes, no matter whether they are positive (arousing) or negative (disgusting).

Summarizing and simplifying these concepts, here’s the state of our thinking at this point on the Salience Value of screen-based content, particularly online porn. From the perspective of the user, the SV of screen-based content depends upon both exogenous (originating externally) and endogenous (originating internally) factors. Those factors aren’t necessarily held constant. In fact, they’re likely highly dynamic. Engineers can tweak exogenous factors to make content more perceptually salient, and users can respond by adapting modes of use that mitigate those tweaks (e.g., by going gray-screen). Likewise, users’ endogenous valuation of subject matter can evolve through learning, experience, aging, and (perhaps) medical interventions, but can be countered by content providers when they produce ever-more novel subject matter.

Why bother with this exercise? As we wrote previously,  we think there's a need to spur focused discussion about the multiple variables that may contribute to problematic online porn use and other compulsive, screen-based behavior.  Too many discussions of porn addiction we read proclaim the problem too difficult and nuanced for effective analysis. Our fondness for behavioral economics leads us to believe that basic modeling and multi-disciplinary analysis can bring order to chaotic discussions like these, and perhaps lead to treatment methods tailored to each problem user's needs. 

Comments welcome.  

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