The intermixed-blocked effect in human perceptual learning is not the consequence of trial spacing.

Mitchell, Chris; Nash, Scott; Hall, Geoffrey

doi:10.1037/0278-7393.34.1.237

Cited by 56 publications

(54 citation statements)

References 16 publications

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“…The interleaved practice group outperformed both the blocked practice group and the blocked-with-cartoons group on a transfer test involving identification of new paintings by the same artists. These findings support the discriminative-contrast hypothesis because (a) the interleaved practice group was the only group that was presented with paintings from the different artists consecutively and hence could more easily contrast the styles of the different painters, and (b) spacing was controlled for in the blocked-with-cartoons group (Kang & Pashler, 2012; see also Mitchell, Nash, & Hall, 2008).…”

Section: Theoretical Accounts For Interleaving Effectssupporting

confidence: 67%

Why does interleaving improve math learning? The contributions of discriminative contrast and distributed practice

et al. 2019

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Interleaved practice involves studying exemplars from different categories in a non-systematic, pseudorandom order under the constraint that no two exemplars from the same category are presented consecutively. Interleaved practice of materials has been shown to enhance test performance compared to blocked practice in which exemplars from the same category are studied together. Why does interleaved practice produce this benefit? We evaluated two non-mutually exclusive hypotheses, the discriminative-contrast hypothesis and the distributed-practice hypothesis, by testing participants' performance on calculating the volume of three-dimensional geometric shapes. In Experiment 1, participants repeatedly practiced calculating the volume of four different-sized shapes according to blocked practice, interleaved practice, or remote-interleaved practice (which involved alternating the practice of volume calculation with non-volume problems, like permutations and fraction addition). Standard interleaving enhanced performance compared to blocked practice but did not produce enhanced performance compared to remote interleaving. In Experiment 2, we replicated this pattern and extended the results to include a remote-blocked group, which involved blocking volume calculation with non-volume problems. Performance on key measures was better for remoteinterleaved groups compared to remote-blocked groups, a finding that supports the distributed-practice hypothesis.

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Section: Theoretical Accounts For Interleaving Effectssupporting

confidence: 67%

Why does interleaving improve math learning? The contributions of discriminative contrast and distributed practice

et al. 2019

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“…The result is that blocked pre-exposure produces better performance in discriminating between AX and BX than is obtained in the control condition, but alternated pre-exposure leads to better discrimination still. We believe this to be the first demonstration of such an effect by simulation, and it fits well with demonstrations of such an effect (e.g., Mitchell, Nash and Hall, 2008;Hall, Blair and Artigas, 2006). The mechanism here seems to be one that could explain the finding reported by Hall and Rodriguez (2009) characterized by Hall (2003Hall ( , 2009 as alternated pre-exposure leading to associative activation of the unique components of the stimuli, which allows for some restoration of the loss in salience to these components that would otherwise have occurred.…”

Section: Perceptual Learningsupporting

confidence: 67%

Elemental representation and configural mappings: Combining elemental and configural theories of associative learning

2012

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In this paper we present our first attempt at combining an elemental theory designed to model representation development in an associative system (e.g., McLaren, Kaye and Mackintosh, 1989), with a configural theory that models associative learning and memory (McLaren, 1993). After considering the possible advantages of such a combination (and some possible pitfalls), we offer a hybrid model that allows both components to produce the phenomena that they are capable of without introducing unwanted interactions. We then successfully apply the model to a range of phenomena including latent inhibition, perceptual learning, the Espinet effect, and first and second order retrospective revaluation. In some cases we present new data for comparison with our model results; in all cases the model replicates the pattern observed in our experimental results. We conclude that this line of development is a promising one for generating truly comprehensive theories of learning and memory. IntroductionOur long-term aim is to produce a general model of associative learning and memory that captures the processes that are common to both humans and infra-humans. This paper investigates the feasibility of combining elemental and configural approaches to associative learning and memory as a stepping stone towards that ultimate goal. In doing so, it also addresses a particular computational problem: of how we can have representation development at an elemental level whilst still learning in a holistic fashion. We start by considering the problem in general, and by motivating the need to find a solution incorporating both elemental (e.g., Estes, 1959;Mclaren, Kaye and Mackintosh, 1989;Brandon, Vogel and Wagner, 2000;Harris, 2006) and configural (e.g., Pearce, 1987, McLaren, 1993Honey, 2000; Honey and WardRobinson, 2002) forms of representation, then move to a specific example of such a combination that attempts to amalgamate the McLaren, Kaye and Mackintosh (1989, henceforth MKM) model of representation development with the APECS (McLaren, 1993;Le Pelley and McLaren, 2001;McLaren, 2011) model of associative learning and memory. To anticipate slightly, the enterprise is a successful one in the sense that the hybrid model is able to reproduce the phenomena that can be simulated using its components (and thus is of wider scope than either of its constituent parts), but this outcome was not achieved without considerable effort and overcoming numerous difficulties. In the course of grappling with this problem we have developed a new respect for the way in which issues multiply as the complexity of the model increases, and we try to pass on our experience of what will and will not work when synthesizing elemental and configural approaches to learning and memory.

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“…One line has been followed by animal learning researchers (e.g., Blair & Hall, 2003; Blair, Wilkinson, & Hall, 2004; Dwyer & Mackintosh, 2002; Mackintosh, Kaye, & Bennett, 1991; McLaren, Kaye, & Mackintosh, 1989; McLaren & Mackintosh, 2000; Mitchell, Nash, & Hall, 2008; Symonds, Hall, & Bailey, 2002); the other line has been followed by sensory psychophysicists. Animal learning researchers have indicated that to fully understand the behavioral rules of learning, it is necessary to clarify the role of perceptual learning as a component of learning.…”

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confidence: 99%

Roles of attention in perceptual learning from perspectives of psychophysics and animal learning

Tsushima

Watanabe

2009

Learning & Behavior

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The intermixed-blocked effect in human perceptual learning is not the consequence of trial spacing.

Cited by 56 publications

References 16 publications

Why does interleaving improve math learning? The contributions of discriminative contrast and distributed practice

Why does interleaving improve math learning? The contributions of discriminative contrast and distributed practice

Elemental representation and configural mappings: Combining elemental and configural theories of associative learning

Roles of attention in perceptual learning from perspectives of psychophysics and animal learning

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