Animal intelligence; experimental studies

Thorndike, Edward L.

doi:10.5962/bhl.title.55072

Cited by 1,018 publications

(489 citation statements)

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“…According to a recent view (Capaldi, 1992), a variety of empirical findings suggest that a common characterization of the instrumental response in many learning theories provides a unit of analysis that is far from optimal, one which, for the most part, should be replaced by another broader or more inclusive unit. Consider first that, in many influential theories ranging historically from Thorndike's (1911) to Hull's (1943) to the present (e.g., Amsel, 1992), the instrumental response is characterized as an intratrial phenomenon, such as a trip from the startbox to the goalbox in a runway. As a concrete example, in Hull's (1943) the:' ory, the strength of the tendency to make such intratrial responses was said to increase when they were reinforced and to decrease when they were nonreinforced.…”

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confidence: 99%

Forming chunks in instrumental learning: The role of overshadowing

Capaldi

Birmingham

Miller

1999

Animal Learning & Behavior

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Lower order units combined into higher order functional units (e.g., letters into words) are called chunks. The hypothesis tested here suggests that chunks are formed when memories of goal events signal other goal events, a signal capacity that can be reduced when memories are seriously overshadowed by other more valid cues called grouping cues. In support of this hypothesis, it was found, in each of three experiments employing rats in runways, that the capacity of the memory ofnonreward to signal reward depended on its validity relative to that of two other situational cues, runway brightness cues and a change in brightness cues, from one trial to the next. Specifically,the capacity of the memory of nonreward to signal reward was much more seriously reduced when it was in competition with more valid situational cues (.33 vs.

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confidence: 99%

Forming chunks in instrumental learning: The role of overshadowing

Capaldi

Birmingham

Miller

1999

Animal Learning & Behavior

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“…The mathematicians Kraines and Kraines (1989) rediscovered it yet again in their research into evolutionary games and called it Pavlov because of its reflex-like character, and this term has persisted in the burgeoning literature of evolutionary games. To a psychologist, WSLS is essentially a formalization of Thorndike's (1898Thorndike's ( , 1911Thorndike's ( , 1927) law of effect:…”

Section: Mss Theorymentioning

confidence: 99%

Learning to cooperate without awareness in multiplayer minimal social situations

Colman¹,

Pulford²,

Omtzigt³

et al. 2010

Cognitive Psychology

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Experimental and Monte Carlo methods were used to test theoretical predictions about adaptive learning of cooperative responses without awareness in minimal social situations-games in which the payoffs to players depend not on their own actions but exclusively on the actions of other group members. In Experiment 1, learning occurred slowly over 200 rounds in a dyadic minimal social situation but not in multiplayer groups. In Experiments 2, 3, and 4, learning occurred rarely in multiplayer groups, even when players were informed that they were interacting strategically and were allowed to communicate with one another but were not aware of the game's payoff structure. Monte Carlo simulation suggested that players approach minimal social situations using a noisy version of the win-stay, lose-shift decision rule, deviating from the deterministic rule less frequently after rewarding than unrewarding rounds.

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“…The most fundamental of these is shown in Figure 2: In motivation theory, the linkage between results and future effort originates in Thorndike's Law of Effect (Thorndike 1911): actions that lead to desirable outcomes are likely to be repeated, and, conversely, actions leading to undesirable outcomes will be subsequently avoided. Hull (1943) extended this notion with his specification Effort=Drive*Habit, where habit represents the strength of the connection between effort and the desired outcome based on past experience.…”

Section: Reinforcementmentioning

confidence: 99%

A Simulation-Based Approach to Understanding the Dynamics of Innovation Implementation

Repenning

2002

Organization Science

254

198

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Abst ractThe history of management practice is filled with innovations that failed to live up to the promise suggested by their early success. A paradox facing organization theory is that the failure of these innovations often cannot be attributed to an intrinsic lack of efficacy. To resolve this paradox, in this paper I study the process of innovation implementation. Working from existing theoretical frameworks, I synthesize a model that describes the process through which participants in an organization develop commitment to using a newly adopted innovation. I then translate that framework into a formal model and analyze it using computer simulation. The analysis suggests three new constructs-reversion, regeneration and the motivation threshold-characterizing the dynamics of implementation. Taken together, these constructs offer an alternative explanation for the paradox of innovations that produce early results but fail to find a permanent home in the organizations that adopt them. 1 Int roductionThe history of management practice is filled with innovations that failed to live up to the promise suggested by their early success. Examples include job enrichment (Hackman 1975), quality circles (Lawler and Morhman 1987), Total Quality Management (TQM) (The Economist 1995), Business Process Re-engineering (White 1996), and various attempts to implement new computer technology (e.g. Orlikowski 1992). A paradox currently facing organizational theory is the fact that the failure of these innovations often cannot be attributed to an intrinsic lack of efficacy. Instead, there is often compelling evidence suggesting that, were the innovation in question appropriately adopted and implemented, the organization would benefit significantly. For example, while numerous studies find that the dedicated use of TQM improves quality, productivity, and overall competitiveness (Easton and Jarrell 1998, Hendricks and Singhal 1996, Barron and Paulson Gjerde 1996, a recent survey found that among US managers TQM is "…deader than a pet rock" (Byrne 1997). The situation is similar for a wide range of other innovations (Klein and Sorra 1996).Unfortunately, existing theory has little to offer on the question of why potentially useful innovations often fail to find a permanent home in the organizations that try to implement them.TQM is one of the more widely studied management techniques, but Dean and Bowen (1994:393) conclude that "...TQ initiatives often do not succeed, but as of yet there is little theory available to explain the difference between successful and unsuccessful efforts." The theoretical landscape is similarly bleak for other types of innovations (Klein and Sorra 1996). While the collection of tools and techniques designed to improve organizational effectiveness continues to grow, the knowledge about how to use them effectively apparently does not. This paper offers a theoretical framework for understanding the phenomenon of failed efforts to improve organizational effectiveness via innovation adoption and implementation...

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Animal intelligence; experimental studies

Cited by 1,018 publications

References 0 publications

Forming chunks in instrumental learning: The role of overshadowing

Forming chunks in instrumental learning: The role of overshadowing

Learning to cooperate without awareness in multiplayer minimal social situations

A Simulation-Based Approach to Understanding the Dynamics of Innovation Implementation

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