Continuous dynamics of color categorization

Huette, Stephanie; McMurray, Bob

doi:10.3758/pbr.17.3.348

Cited by 30 publications

(35 citation statements)

References 31 publications

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“…When perceiving sequential stimuli like musical sequences or words, learners take in information across time, with multiple representations competing for recognition as the input is received (Bharucha, 1987;McClelland & Elman, 1986). Even for simple perceptual decisions, single-unit recording studies with non-human primates show ongoing competition between multiple representations at the neuronal level (Shadlen & Kiani, 2013), and human studies using techniques like mouse-tracking and event-related potentials (ERPs) show similar unfolding dynamic competition (Huette & McMurray, 2010;McKinstry, Dale, & Spivey, 2008;Miller & Hackley, 1992). From the earliest moments of stimulus processing, the decision making system considers multiple candidate interpretations of the input in parallel.…”

Section: Introductionmentioning

confidence: 99%

Learning During Processing: Word Learning Doesn't Wait for Word Recognition to Finish

Apfelbaum

McMurray

2016

Cognitive Science

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Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed when, during the course of these dynamic recognition processes, learned representations are formed and updated. If learned representations are formed and updated while recognition is ongoing, the result of learning may incorporate spurious, partial information. For example, during word recognition, words take time to be identified, and competing words are often active in parallel. If learning proceeds before this competition resolves, representations may be influenced by the preliminary activations present at the time of learning. In three experiments using word learning as a model domain, we provide evidence that learning reflects the ongoing dynamics of auditory and visual processing during a learning event. These results show that learning can occur before stimulus recognition processes are complete; learning does not wait for ongoing perceptual processing to complete. KeywordsWord learning; associative learning; processing dynamics; temporal processes; lexical access A century of research on learning offers detailed mechanistic understanding of how learners come to link various representations (what we broadly refer to as associative learning). This work has elucidated what sorts of things can be learned. Major branches of cognitive science study what types of associations form most easily and what constraints control the things that are learnable (e.g., Goldstone & Landy, 2010;Munakata & O'Reilly, 2003;Thiessen, 2011). Research has also uncovered much about where in the brain associative linkages occur. We have complex understanding of how different neural circuits accommodate different types of information, and how this information is stored within neural circuits (J. H. Freeman, 2010;O'Reilly & Norman, 2002). Further, we know how associations are built; that is we have clear theories and models of how information is combined to strengthen and weaken associations (Hebb, 1949;Rescorla & Wagner, 1972; Rumelhart, Hinton, & 1 Corresponding author: apfelbaum.3@osu.edu, (614) 248-1242. HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript Williams, 1986) and of the complex emergent products that arise when many such associations are built simultaneously (O'Reilly & Munakata, 2000;Seidenberg & McClelland, 1989;Wasserman, Brooks, & McMurray, 2015).However, we do not yet know when learning occurs. It is clear that learning does not link raw perceptual inputs (if there is such a thing) to each other; rather stimuli are processed, encoded, represented, and/or categorized either during learning or prior to it. Such processes unfold dynamically over time (Spivey, 2007). What is not clear then is when-during these dynamically unf...

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Section: Introductionmentioning

confidence: 99%

Learning During Processing: Word Learning Doesn't Wait for Word Recognition to Finish

Apfelbaum

McMurray

2016

Cognitive Science

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“…In the absence of perceptual information, they must rely, instead, on a categorical code to select the target item. The new explanation derives from the fact that central targets are categorized and named more quickly, more accurately, and more consistently than less central targets (Agrillo & Roberson, 2009;Brown & Lenneberg, 1954;Huette & McMurray, 2010;Rosch Heider, 1975). If a target is named correctly at presentation and at test on a withincategory trial but the distractor is labeled incorrectly, the target can be selected reliably on the basis of the categorical code alone.…”

Section: Proportion Correctmentioning

confidence: 99%

“…CP has been investigated for auditory continua such as musical notes (e.g., (Howard, Rosen, & Broad, 1992;Locke & Kellar, 1973) and phonemes (e.g., Liberman, Harris, Hoffman, & Griffith, 1957;Repp, 1984), for natural visual continua such as color (e.g., Gilbert, Regier, Kay, & Ivry, 2006), and for morphed continua between face identities or facial expressions (Angeli, Davidoff, & Valentine, 2007;Gilbert et al, 2006;Goldstone & Hendrickson, 2009;Huette & McMurray, 2010;Liberman et al, 1957). It has even been found for morphed continua between human and ape faces (Campbell et al, 1997) and can also be trained for novel category boundaries (Goldstone, 1994(Goldstone, , 1998Özgen & Davies, 2002).…”

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confidence: 99%

Categorical perception effects reflect differences in typicality on within-category trials

Hanley

Roberson

2011

Psychon Bull Rev

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Many studies have shown better discrimination of two stimuli that cross a category boundary than of two stimuli belonging to the same category. This finding, known as categorical perception, is generally assumed to reflect consistently good performance on cross-category trials, relative to within-category trials. However, Roberson, D., Damjanovic, L., and Pilling, M. (Memory & Cognition, 35, 1814-1829 revealed that performance on withincategory pairs of morphed facial expressions matched performance on cross-category trials when the target was a good exemplar of its category. Here, we investigate the generality of that finding by conducting new analyses of data from a series of studies of categorical perception in facial identity and color domains with speakers of different languages. Consistent with Roberson et al. (2007), the new analyses demonstrate that performance for central targets on within-category trials is as good as performance on crosscategory trials. Participants perform badly on withincategory items only when the target is closer to the category boundary than is the distractor. These results provide no support for the view that categorical perception is associated with increased perceptual sensitivity at category boundaries.

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“…Within a category participants show differences in mouse trajectories that are sensitive to the variations in hue (Huette & McMurray, 2010). This indicates that even within a category differences in performance can be found.…”

Section: Movement Trajectorymentioning

confidence: 93%

“…Previous experiments have made participants start to make their movement before the stimulus appears (e.g., Huette & McMurray, 2010;Spivey et al, 2005), so that the decision was not complete before the initiation of the movement. However, this type of procedure is unlike most real-world tasks (and most experimental choice-RT tasks) in which movements are initiated after the stimulus is perceived.…”

Section: General Methodsmentioning

confidence: 99%

Reaching into response selection: Stimulus and response similarity influence central operations.

Wifall¹,

Buss²,

Farmer³

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

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iv ABSTRACT This dissertation examines the impact of stimulus and response similarity on response selection. Traditional models of response selection invoke a central processor that operates like a look-up table by matching the perceptually classified stimulus (e.g., green square) to the specified response (e.g., right button press). The look-up property of response selection affords the system the ability to map any stimulus onto any response, even if that stimulus-response has never been paired before. Under such an approach, the degree of perceptual similarity or dissimilarity that exists among stimuli in the environment should have little effect on central operations, the similarity or dissimilarity of the motor response executed in response to a stimulus should not influence response selection, and no interaction between stimulus and response features is permitted, given that stimulus features affect the encoding process, and response features affect the output process, but not response selection itself.Eight studies examine the influence of stimulus and response similarity during response selection. The first two experiments establish the interaction across different task demands between stimulus and response similarity. The interaction was not the result of perceptual difficulty (Experiment 3) and was extended to a new set of stimuli (Experiment 4). A consequence of the design in Experiments 1 -4 was that response condition was confounded with response configuration. In one of the response conditions the target location had three competitors on one side of it compared to the other condition where the target had one competitor on one side and two others on the other side.Experiments 5 and 6 examined the separate roles that response configuration and response metrics had on the interaction between stimulus and response similarity. The mechanism that produced the interaction was the result of competition between partially activated stimulus-response alternatives. Experiments 7 and 8 further explored the role of competition during response selection by turning to traditional response selection methodologies that introduce competition through either the presentation of irrelevant v stimulus information or through presenting the stimulus along an irrelevant spatial dimension.These data have broad implications for models of RS. To account for the ability to pair any stimulus modality with any response modality dominant accounts of RS assume that central operations are performed by a generic set of processes that operate over representations that are stripped of metric information (amodal representations).Response selection works as a look-up table that receives a categorized stimulus as an input and returns an abstract response code as output. This type of model cannot produce an interaction between stimulus and response similarity and thus, the present data provide a serious challenge to these types of models. Finally, the data provide evidence that the metric relationship between stimuli and response matter an...

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Continuous dynamics of color categorization

Cited by 30 publications

References 31 publications

Learning During Processing: Word Learning Doesn't Wait for Word Recognition to Finish

Learning During Processing: Word Learning Doesn't Wait for Word Recognition to Finish

Categorical perception effects reflect differences in typicality on within-category trials

Reaching into response selection: Stimulus and response similarity influence central operations.

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