A computational Model of Human Affective Memory and Its Application to Mindreading
On average, PERSONA performed noticeably better than both baselines, excelling particularly in predicting arousal, and having the most difficulty predicting dominance. The standard deviations were very high, reflecting the observation that PERSONA’s predictions were often either very close to the actual valence, or very far. This can be attributed to one of several causes. First, multiple episodes described in the same journal entries may have led to improper associative learning. Second, the reflexive memory model does not account for conflicting word senses. Third, personal texts inputted for the imprimers often generated models skewed to positive or negative because text did not always have an episodic organization. While results along the pleasure and dominance dimensions are weaker, the arousal dimension recorded a mean spread of 0.22, suggesting the possibility that it alone may have immediate applicability.
In the experiment, we also analyzed how often the episodic memory, reflexive memory, and imprimers were triggered. Episodes were on average, 4 sentences long. For each episode, reflexive memory was triggered an average of 21.5 times, episodic memory 0.8 times, and imprimer reflexive memory 4.2 times. To measure the effect of imprimers and episodic memories, we re-ran the experiment turning off imprimers only, episodic memory only, and both. Table 2 summarizes the results.
Overall, the evaluation demonstrates that the proposed approach is promising, but needs further refinement. The randomized BASELINE 2 is a good comparison when considering possible entertainment applications, whose interaction is more fail-soft. PERSONA does quite well against the active BASELINE 2, and is within the performance range of these applications. However, the results also suggest that PERSONA may not be ready for deployment to a sociable computer just yet, because fallout (bad predictions) can be very costly in the realm of affective communication (Nass et al., 1994). Affective communication obeys certain social contracts, making them fail-hard applications.
Mindreading is a problem of interest to the cognitive science community, as well as the artificial intelligence community. While cognitive science is generally interested in attaining a deeper understanding of the problem, we argue that their primarily behavioral and neurological means of study will be insufficient to uncover the larger mechanistic picture of theory of mind and mindreading in humans. On the other hand, the artificial intelligence community is interested in applying mental modeling and mindreading to build sociable computers. However, their models of profiling and collaborative filtering are too weak. In this work, we have tried to address the need of both communities and walk a middle ground. In our study of affective mindreading, we proposed and built a system that attempts to model a person much deeper than present approaches in AI.
The proposed episode-reflex model of human affective memory has interesting implications for psychology and cognitive sciences, providing a real way to be able to test classical theories such as associative learning, memory organizations, and formation of identity of the self, among others. The fact that attitudes can be considered independently of beliefs and goals suggests that in computationalizing mindreading, it may be possible to decompose mindreading into several processes, of which affective mindreading is one. And indeed, it is possible that humans may have a toolkit of processes which combine to be called mindreading. It is not out of the realm of possibilities that a special process exists for modeling and assessing just the attitudes of another person. Such a process may serve the role, we speculate, of contextually disambiguating processes for belief determination.
6.ACKNOWLEDGMENTSI would like to thank Barbara Barry, Push Singh, and Andrea Lockerd for their ideas, suggestions, and inspiration in the course of this work.
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