Forming Expectations
As the previous research by Pavlov showed, meeting any expectation, from a learning perspective, appears to be an important mechanism which can reinforce prior learning of cause and effect (stimulus and response). Through exploration, we proposed to utilise this mechanism to reinforce or redefine prior situational learning. In this way, expectations are a high-level abstraction about causality (events) and their results/outcomes, while causality itself is an abstraction of experiences.
For example, by using the Stimulus-response model, after a certain threshold of repeated S-R link occurrences (behavior in response to a stimulus), the produced response could become automatic, such that the detection of the stimulus becomes to mean that a fixed, expected response, which in humans, “…allows for faster, more efficient behavior when the same response is required to the same stimulus in the future”, likely cutting down on the processing required to determine or derive this behavior from its knowledge base (Allenmark, Moutsopoulou and Waszak, 2015).
One could simulate this by deleting re-occurring duplicate S-R link sequences (or rather stop measuring them) once some occurrence threshold is met, and then replace any cognitive processing associated with the S-R link sequences with producing a now fixed and expected learnt response.
This may be a simulation of a trivial mechanism, but it might inform the evolution, codification and emergence of more complex behaviour, including detecting important milestones (or importance in general) or consistency in situations. In our research, expectation could be created from observed causality that is established by stimulus-response sequences in the simulation.
Equally, a mismatched expectation could be used signal that the current knowledge is incomplete and requires more experience to properly inform it.
Incidentally, this may have other interesting outcomes such as organically simulating fixed and growth mind-sets in artificial learning entities, as the willingness to resist behavioral certainty or expectations (fixed mindset) for the potential benefit of gaining new, and potentially interesting outcomes, such as new and diverse learning opportunities which naturally comes with arresting ones expectations (growth mindsets), namely through exploring. The alternative (fixed mindsets) forgoes this cognitive processing burden of establishing reason and produces behaviour/responses based on established fixed expectations, perhaps limiting it from new learning in the process, and which is more closely modelled by traditional rule-based AI, based, for example, on finite state machines and static behavioural trees, etc.
Expectations influence behaviour
As an example of the importance expectations play in human behavioural learning in general, and as a consequence, the ability to form them from observations, is the phenomenon where one sees what one expects to see, without evaluating other available data. For example, while driving, we often notice pedestrians but might not even register a cyclist, or even notice a familiar person at an unfamiliar and unexpected location.
A popular example of this is a video demonstration (Dothetest, 2008) showing a Basketball game being played, and after having watched the short clip, the viewer is asked if they had seen the dancing bear. In many cases, they did not and are entertainingly surprised after re-watching the video (now expecting the dancing bear) that the dancing bear is not only readily viewable, but that it should have been obvious that it was there, as it occurs directly in the centre of view during the game.
Another example of the subtlety of expectation in influencing human behavior, is called priming, where showing first a series of numbers (priming our expectations) then showing an ambiguous figure (which is similar but is not a number), results in our immediate intuition to think, or expect that the unknown figure is a number, when in fact it might equally be shaped as many other things. This can therefore influence the behaviour that occurs after priming.
I speculate that expectations may serve as a short-cut to limit the overhead of cognitive processing, and as a means to calibrate the ineffectiveness of learning (or classification of experience), forming a kind of error detection function. It may thus be possible or useful to model and simulate priming behaviour by modelling the formation of expectations in the face of experiencing unknown and/or ambiguous situations to better understand how this might affect learning.
The identification and detection of underlying and otherwise unknown expectations from within experiences might be the precursor to the establishment of rules, which seem inextricably linked.
One model that appears to use fixed expectations to establish a fixed association between a stimulus and response without needing the stimuli to recur is called ‘One-Trial Learning’ (Feng and Sun, 2019). This might be crucial in evaluating any new and unknown. One-Trial learning occurs when a fixed response is generated in response to a single experience, usually resulting in a bad or undesirable situation, like when eating food which causes one to convulse and throw up, or when we experience pain. The response to these situations is a decisive and fixed one.
Goal-orientated pursuit
The implication of this is that this seems to suggest that there is a specific evaluation of the value of an encountered experience, i.e it possesses what our research classifies as an ‘experiential value’. Moreover, this suggests that behaviour is likely based on the degree of alignment to a set of priorities, where high-ranking priorities are more likely to trigger One-Trial learning. For example, reactions to experiences that compromise physical safety are high-priority goals, which strongly influence specific behavioural reactions.
The establishment of an experiential value measurement might thus help inform how to learn to react in response to new, and previously unknown stimuli, but which is recognised to be affecting a high-value goal, such as survival. A discussion of how one might value arbitrary experiences is discussed later.
One-Trail learning might be simulated by either hard-coding triggered behaviour to known, high-priority perceptions that are experienced, eg, getting hurt, which is more akin to how pre-determined AI works, or more interestingly, the model might dynamically assign a higher value to sensory data associated with that high-priority experience (which might be predefined or possibly even self-emerge). In this case, such one-time learning behaviour is triggered so that no subsequent cognitive processing is required to define the expected behaviour in response to it, as it's already been rapidly determined.
Either way, dealing with expectations while interpreting experiences is likely an important part of rationalising about reality.
Using expectations to make Sense of the unknown
Another aspect of this research is dealing with the unknown object that is experienced, and how it is rationalised or made sense of. What is the process of making sense?
Interestingly, expectation again appears to be a crucial component of this process. For example, research has demonstrated that ‘amodal’ completion helps make sense of something that is not perceptually present (unknown) because cognitively we allow what is missing or unknown to fit or be extrapolated into matching (or completing) an already known expectation (Ekroll, Sayim and Wagemans, 2017).
Figure 13) illustrates amodal completion, where the shape A represents a known expectation, and shape B is then amodally completed to align and define it according to the existing expectation, and in doing so, to make sense of unknown B.
In this way, it seems that knowledge may well be the collection of expectations, possibly informed by having witnessed expectations from observations of cause-and-effect, much like stimuli causing responses (S-R links) is represented.
Forming and identifying expectations
If knowledge (reasoning) can be generalized (at least) as a collection of expectations which are derived from the experience of observed causality, then the observation of an unknown thing (or situation) either needs to correspond or extrapolate to an existing expectation (from existing knowledge), or it needs to form a new expectation - which appears to be the creation of new knowledge. This model may underlie a means to simulate, trigger and identify the need for the formation of new knowledge based on perception and expectation.
It is in this way, that a simulation environment (such as proposed in our research) which allows for the establishment of causality through perceptual observation might allow for the construction of knowledge as expectations derived from sensory data, and in doing so, that the unknown might be reasoned about as the degree in which its observation fits the properties of known expectations (amodal completion).
These properties might include the causal relationship, such as S-R links and the associated CSIs. Matching S-R links and CSIs might indicate a means to make sense of the unknown or make sense of situations where properties of known expectations match. The degree of match might indicate the confidence in the understanding. Confidence in this respect appears to be the rate of identification of known expectations.
Understanding itself might then be defined as the ability to make ‘sense’, which in turn is the ability and degree to match known expectations (or knowledge), which in turn is derived from observations.
References
dothetest (2008) ‘Test Your Awareness: Do The Test’. Available at: https://www.youtube.com/watch?v=Ahg6qcgoay4 (Accessed: 13 August 2022).
Feng, N. and Sun, B. (2019) ‘On simulating one-trial learning using morphological neural networks’, Cognitive systems research, 53, pp. 61–70. doi: 10.1016/j.cogsys.2018.05.003.
Ekroll, V., Sayim, B. and Wagemans, J. (2017) ‘The Other Side of Magic: The Psychology of Perceiving Hidden Things’, Perspectives on psychological science, 12(1), pp. 91–106. doi: 10.1177/1745691616654676.