Dinsmoor's selective observing hypothesis probably cannot account for a preference for unpredictable rewards: DMOD can

Daly, Helen B.

doi:10.1017/s0140525x00023074

Cited by 4 publications

(4 citation statements)

References 29 publications

(36 reference statements)

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“…Reinforcement theorists attribute observing to conditioned reinforcement but diverge in their interpretations of how this comes about (e.g., Daly, 1986;Dinsmoor, 1983Dinsmoor, ,1986Fantino, 1983;Perkins, 1983). Cognitive theorists commonly think of the living organism as a system that regularly and ubiquitously gathers information about sequences of events in the surrounding environment (e.g., Boneau, 1974; D'Amato, Etkin, & Fazzaro, 1968; Dember, 1974;Dickinson, 1980;Gibson, 1969;Schrier, Thompson, & Spector, 1980).…”

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confidence: 99%

Information on Response Requirements Compared With Information on Food Density as a Rein Forcer of Observing in Pigeons

Dinsmoor

Bowe

Green

et al. 1988

J Exper Analysis Behavior

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On a variable-interval schedule, pecking the key to the pigeon's right (observing response) produced red or green displays relating to the delivery of grain and its dependence on pecking the key to the left (food key). During various blocks of sessions, mixed (no stimulus change) schedules including the following pairs of components were temporarily converted by the observing response to their corresponding multiple (correlated stimuli) schedules: variable-interval 60-s, extinction; variable-interval 60-s, variable-time (response-independent) 60-s; extinction, variable-time 60-s. Differences in food delivery maintained substantial rates of responding on the observing key, without regard to pecking requirements on the food key. Although stimuli correlated with differences in the response requirement on the food key maintained higher observing rates than those maintained by uncorrelated stimuli, they were much lower than those based on food. The value of predictive stimuli as reinforcers is determined by the value of the events predicted. In particular, the cost of pecking appears to be low, and this may place limitations on the applicability of energy-based and economic models of behavior.

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confidence: 99%

Information on Response Requirements Compared With Information on Food Density as a Rein Forcer of Observing in Pigeons

Dinsmoor

Bowe

Green

et al. 1988

J Exper Analysis Behavior

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“…The model made some interesting predictions concerning when a preference for the unpredictable reward situation should occur. These predictions were subsequently supported (Daly, 1985;Daly & Daly, 1982); other theories have great difficulty accounting for these results (Daly, 1985(Daly, , 1986. We are just beginning to use the extensions of the model…”

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confidence: 85%

A computer simulation/mathematical model of learning: Extension of DMOD from appetitive to aversive situations

Daly

1987

Behavior Research Methods, Instruments, & Computers

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The computer simulation/mathematical model called DMOD, which can simulate over 35 different phenomena in appetitive discrete-trial and simple free-operant situations, has been extended to include aversive di~rete-trial situations. Learning (V) is calculated using a three-parameter equation .1V = a{j(A -V) (see Daly & Daly, 1982;Rescorla & Wagner, 1972). The equation is applied to three possible goal events in the appetitive (e.g., food)case and to three in the aversive (e.g., shock) case. The original goal event can be present, absent, or reintroduced; in the appetitive situation, these events condition approach (Vap), avoidance (Vav), and courage (Vee), respectively. In the aversive situation, the events condition avoidance (Vav*), approach (Vap*), and cowardice (Vcc*), respectively. The model was developed in simple learning situations and subsequently was applied to complex situations. It can account for such diverse phenomena as contrast effects after reward shifts, greater persistence following partial than following continuous reinforcement, and a preference for predictable appetitive and predictable aversive events. Application of the aversive version of the model to "reward" shifts is described.Our goal is to develop a computer simulation/mathematical model of learning that is as simple as possible with as much breadth as possible. The model, called DMOD (Daly MODification of the Rescorla-Wagner Model), is simple because learning is calculated with one simple equation using three parameters. It is applied to diverse situations by assuming that there are a number of different goal events possible, each of which conditions either approach or avoidance of the goal. It was originally developed to account for appetitive, discrete-trial experiments, and can currently account for behavior in over 30 different paradigms (see Daly & Daly, 1982). DMOD was then extended to simulate the effect of simple schedules of reinforcement in free-operant experiments (Daly & Daly, 1984b). Our purpose is to outline the extension of DMOD to the aversive case and to show its application in a complicated experimental situation. To understand the rationale behind the extension, however, it is neces- sary to review development of the model in the appetitive case and the rules we follow for development of DMOD. SELECTION OF PHENOMENA TO BE SIMULATEDWe believe that the initial goal of a new model is to be able to account for well-established and replicable phenomena. We feel that it is dangerous to develop a model around recently discovered phenomena, because the boundary conditions under which they can be obtained and the variables influencing them are unknown. The purpose of a theory, however, is not only to integrate existing replicable data, but also to correctly predict new results. The primary phenomenon we use to test predictions of the appetitive version of DMOD is acquisition of a preference for predictable reward. The model made some interesting predictions concerning when a preference for the unpredictable reward situati...

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“…DMOD has been used successfully to integrate over 60 appetitive and aversive learning phenomena (Daly & Daly, 1982, 1987, 1991). The model has also been used to predict behaviors in areas as diverse as (a) preference for unpredictable over predictable reward (Daly, 1985, 1986, 1989, in press) and (b) the behavioral changes in laboratory rats after they consumed salmon from Lake Ontario, which are environmentally contaminated with toxic chemicals (Daly, Hertzler, & Sargent, 1989). DMOD is simple because there is one basic learning equation with three parameters: Δ V = αβ (λ − ) (see Appendix for summary of terms).…”

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confidence: 99%

Changes in learning about aversive nonreward accounts for ontogeny of paradoxical appetitive reward effects in the rat pup: A mathematical model (DMOD) integrates results.

Daly

1991

Psychological Bulletin

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Baby rats do not show any paradoxical appetitive reward effects (e.g., faster extinction following partial than continuous reinforcement, contrast effects when large and small rewards are given) until they are at least 12-14 days old, but can learn to pattern when reward and nonreward are alternated (e.g., Amsel, 1986). These results have been puzzling, but are now successfully integrated by DMOD (Daly MODification of Rescorla and Wagner's [1972] mathematical model; Daly & Daly, 1982). It was assumed that young rats do not have the capacity to learn about aversive nonreward but slowly gain this ability between 12 and 26 days (1 parameter reflecting the rate of conditioning of aversive nonreward, beta 1 for Vav, is increased from 0 to .15 beginning at 12 days). This theoretical integration has implications for understanding behavioral and neurological development of altricial organisms, and effects of neurological damage and toxic substances.

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Dinsmoor's selective observing hypothesis probably cannot account for a preference for unpredictable rewards: DMOD can

Cited by 4 publications

References 29 publications

Information on Response Requirements Compared With Information on Food Density as a Rein Forcer of Observing in Pigeons

Information on Response Requirements Compared With Information on Food Density as a Rein Forcer of Observing in Pigeons

A computer simulation/mathematical model of learning: Extension of DMOD from appetitive to aversive situations

Changes in learning about aversive nonreward accounts for ontogeny of paradoxical appetitive reward effects in the rat pup: A mathematical model (DMOD) integrates results.

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