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Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences.

Herrera D, Treviño M - PLoS ONE (2015)

Bottom Line: We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences.Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM.Virtual RDMs could be further employed to guide the selection of proper training schedules for perceptual decision-making studies.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Neurociencias, Universidad de Guadalajara, Guadalajara, Jalisco, México.

ABSTRACT
In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS). When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'). As an alternative, a 'Gellerman-like training schedule' (GLS) can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide the selection of proper training schedules for perceptual decision-making studies.

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Related in: MedlinePlus

Testing the RDM with different 'short-term memory' decaying rates.Results are displayed as the difference in the probability of finding output biased choices produced with a 'Gellerman-like' (GLS) minus a 'Full Random' (FRS) training schedule, after being processed by the 'Rational Decision Maker' (RDM) algorithm (i.e. ΔP = Poutput_bias[GLS] − Poutput_bias[FRS]). Panels are arranged in columns (k1 = 0.5, 0.75 and 0.90) and rows (k2 = 0.5, 0.75 and 0.90). Increasing k values implies longer short-term memory representatioτns. k values of 0.5, 0.75 and 0.90 correspond to a τ of 1.44, 3.47 and 9.49, respectively.
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pone.0136084.g003: Testing the RDM with different 'short-term memory' decaying rates.Results are displayed as the difference in the probability of finding output biased choices produced with a 'Gellerman-like' (GLS) minus a 'Full Random' (FRS) training schedule, after being processed by the 'Rational Decision Maker' (RDM) algorithm (i.e. ΔP = Poutput_bias[GLS] − Poutput_bias[FRS]). Panels are arranged in columns (k1 = 0.5, 0.75 and 0.90) and rows (k2 = 0.5, 0.75 and 0.90). Increasing k values implies longer short-term memory representatioτns. k values of 0.5, 0.75 and 0.90 correspond to a τ of 1.44, 3.47 and 9.49, respectively.

Mentions: According to the matching law, each reward contributes equally to increasing the probability of choosing an option [16]. This contribution is conceptualized as being stable over time because these mathematical descriptions are valid only for choice behavior in a steady-state scenario [12]. In a dynamic system, however, the reward changes its location over time and choice behavior is strongly influenced by recently obtained reinforcers [12]. In such scenario, k1 and k2 represent constants that determine the decay rate for the contribution of past rewards to current choice (Eq 3(B) and Eq 4; see also [13]). Higher k values imply a higher impact of reward history on current choice, whereas lower values represent a more prolonged effect of reinforcement history. Based on this idea, we investigated whether ΔP could invert its sign after changing the relative contribution of past rewards. We tested all possible combinations for three different discrete values for k1 and k2, respectively. In Fig 3 we show that ΔP values were positive for all combinations tested (covering both k1> k2 and k1< k2), yet group differences became smaller as k values increased. Also, k1 had a stronger effect (than k2) in dissipating ΔP for high β values (Fig 3).


Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences.

Herrera D, Treviño M - PLoS ONE (2015)

Testing the RDM with different 'short-term memory' decaying rates.Results are displayed as the difference in the probability of finding output biased choices produced with a 'Gellerman-like' (GLS) minus a 'Full Random' (FRS) training schedule, after being processed by the 'Rational Decision Maker' (RDM) algorithm (i.e. ΔP = Poutput_bias[GLS] − Poutput_bias[FRS]). Panels are arranged in columns (k1 = 0.5, 0.75 and 0.90) and rows (k2 = 0.5, 0.75 and 0.90). Increasing k values implies longer short-term memory representatioτns. k values of 0.5, 0.75 and 0.90 correspond to a τ of 1.44, 3.47 and 9.49, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4549334&req=5

pone.0136084.g003: Testing the RDM with different 'short-term memory' decaying rates.Results are displayed as the difference in the probability of finding output biased choices produced with a 'Gellerman-like' (GLS) minus a 'Full Random' (FRS) training schedule, after being processed by the 'Rational Decision Maker' (RDM) algorithm (i.e. ΔP = Poutput_bias[GLS] − Poutput_bias[FRS]). Panels are arranged in columns (k1 = 0.5, 0.75 and 0.90) and rows (k2 = 0.5, 0.75 and 0.90). Increasing k values implies longer short-term memory representatioτns. k values of 0.5, 0.75 and 0.90 correspond to a τ of 1.44, 3.47 and 9.49, respectively.
Mentions: According to the matching law, each reward contributes equally to increasing the probability of choosing an option [16]. This contribution is conceptualized as being stable over time because these mathematical descriptions are valid only for choice behavior in a steady-state scenario [12]. In a dynamic system, however, the reward changes its location over time and choice behavior is strongly influenced by recently obtained reinforcers [12]. In such scenario, k1 and k2 represent constants that determine the decay rate for the contribution of past rewards to current choice (Eq 3(B) and Eq 4; see also [13]). Higher k values imply a higher impact of reward history on current choice, whereas lower values represent a more prolonged effect of reinforcement history. Based on this idea, we investigated whether ΔP could invert its sign after changing the relative contribution of past rewards. We tested all possible combinations for three different discrete values for k1 and k2, respectively. In Fig 3 we show that ΔP values were positive for all combinations tested (covering both k1> k2 and k1< k2), yet group differences became smaller as k values increased. Also, k1 had a stronger effect (than k2) in dissipating ΔP for high β values (Fig 3).

Bottom Line: We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences.Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM.Virtual RDMs could be further employed to guide the selection of proper training schedules for perceptual decision-making studies.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Neurociencias, Universidad de Guadalajara, Guadalajara, Jalisco, México.

ABSTRACT
In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS). When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'). As an alternative, a 'Gellerman-like training schedule' (GLS) can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide the selection of proper training schedules for perceptual decision-making studies.

No MeSH data available.


Related in: MedlinePlus