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Motivational Representations within a Computational Cognitive Architecture

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Abstract

This paper discusses essential motivational representations necessary for a comprehensive computational cognitive architecture. It hypothesizes the need for implicit drive representations, as well as explicit goal representations. Drive representations consist of primary drives—both low-level primary drives (concerned mostly with basic physiological needs) and high-level primary drives (concerned more with social needs), as well as derived (secondary) drives. On the basis of drives, explicit goals may be generated on the fly during an agent’s interaction with various situations. These motivational representations help to make cognitive architectural models more comprehensive and provide deeper explanations of psychological processes. This work represents a step forward in making computational cognitive architectures better reflections of the human mind and all its motivational complexity and intricacy.

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Notes

  1. This notion is adopted, because we need to account for (1) context-dependent and (2) persistent but terminable drivers of behavior, (3) in an implicit way, as well as other properties of behavior mentioned early on.

  2. Note that, although drive states may sometimes be identified individually (as I will do next), such identifications are approximate. They do not represent the full complexity of the matter. Furthermore, the generation and change of these drive states are fully implicitly determined (through neural networks in computational modeling). Thus, I view drive states as being fundamentally implicit.

  3. This drive may be further differentiated as there may be different needs for different nutrients in accordance with bodily states; "tastes" are changeable over time.

  4. Murray's low-level (physiological, or viscerogenic in Murray's term) needs are not included in this list either. They may be attributed to some combinations of the low-level primary drives as enumerated earlier.

  5. We do not include here the need for eating, the need for tranquility, the need for physical exercises, and the need for romance, as in Reiss [37], since they are mostly physiological (see the list of low-level primary drives earlier).

  6. The empirical evidence appears to support multiple similar frameworks, not necessarily any particular one. The differences among these frameworks may be adjudicated through empirical and theoretical means, in particular, through capturing, simulating, and explaining empirical data using computational models [44, 46].

  7. For example, when water is nearby and easily accessible, and food-deficit is not too much greater than water-deficit but food stimulus is not available, the agent should address the water-deficit first.

  8. Note that this is the preferred alternative to using the afore-specified formulas directly, which would require various deficits and stimuli to be identified individually.

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Acknowledgments

This work has been supported in part by the ARI contract W74V8H-05-K-0002 (to Ron Sun and Bob Mathews) and the ONR Grant N00014-08-1-0068 (to Ron Sun). Nick Wilson conducted some of the simulations briefly mentioned here.

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  1. Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Ron Sun

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Sun, R. Motivational Representations within a Computational Cognitive Architecture. Cogn Comput 1, 91–103 (2009). https://doi.org/10.1007/s12559-009-9005-z

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