Categorical perception refers to the enhancement of perceptual sensitivity near category boundaries, generally along dimensions that are informative about category membership. However, it remains unclear exactly which dimensions are treated as informative and why. This article reports a series of experiments in which subjects were asked to learn statistically defined categories in a novel, unfamiliar 2D perceptual space of shapes. Perceptual discrimination was tested before and after category learning of various features in the space, each defined by its position and orientation relative to the maximally informative dimension. The results support a remarkably simple generalization: The magnitude of improvement in perceptual discrimination of each feature is proportional to the mutual information between the feature and the category variable. This finding suggests a rational basis for categorical perception in which the precision of perceptual discrimination is tuned to the statistical structure of the environment.
AshbyF. G.MaddoxW. T. (2011). Human category learning 2.0. Annals of the New York Academy of Sciences, 1224, 147–161.
2.
BalsamP. D.FairhurstS.GallistelC. R. (2006). Pavlovian contingencies and temporal information. Journal of Experimental Psychology: Animal Behavior Processes, 32(3), 284–294.
3.
BatesC. J.JacobsR. A. (2020). Efficient data compression in perception and perceptual memory. Psychological Review, 127(5), 891–917.
4.
BatesC. J.LerchR. A.SimsC. R.JacobsR. A. (2019). Adaptive allocation of human visual working memory capacity during statistical and categorical learning. Journal of Vision, 19(2), Article 11. https://doi.org/10.1167/19.2.11
5.
BattitiR. (1994). Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5(4), 537–550.
6.
Bonnasse-GahotL.NadalJ. P. (2008). Neural coding of categories: Information efficiency and optimal population codes. Journal of Computational Neuroscience, 25(1), 169–187.
7.
BriscoeE.FeldmanJ. (2011). Conceptual complexity and the bias/variance tradeoff. Cognition, 118, 2–16.
8.
CaseyM. C.SowdenP. T. (2012). Modeling learned categorical perception in human vision. Psychological Bulletin, 33, 114–126.
9.
ClayardsM.TanenhausM. K.AslinR. N.JacobsR. A. (2008). Perception of speech reflects optimal use of probabilistic speech cues. Cognition, 108(3), 804–809.
10.
CoverT. M.ThomasJ. A. (1991). Elements of information theory. Wiley.
11.
DamperR. I.HarnadS. R. (2000). Neural network models of categorical perception. Perception & Psychophysics, 62(4), 843–867.
12.
DestlerN.SinghM.FeldmanJ. (2019). Shape discrimination along morph-spaces. Vision Research, 158, 189–199.
13.
DickinsonJ. E.BellJ.BadcockD. R. (2013). Near their thresholds for detection, shapes are discriminated by the angular separation of their corners. PLOS ONE, 8(5), Article e66015. https://doi.org/10.1371/journal.pone.0066015
14.
DieciucM.RoqueN. A.FolsteinJ. R. (2017). Changing similarity: Stable and flexible modulations of psychological dimensions. Brain Research, 1670, 208–219.
15.
FeldmanN. H.GriffithsT. L.MorganJ. L. (2009). The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychological Review, 116(4), 752–782.
16.
FolsteinJ. R.GauthierI.PalmeriT. J. (2010). Mere exposure alters category learning of novel objects. Frontiers in Psychology, 1, Article 40. https://doi.org/10.3389/fpsyg.2010.00040
17.
FolsteinJ. R.GauthierI.PalmeriT. J. (2012). How category learning affects object representations: Not all morphspaces stretch alike. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(4), 807–820. https://doi.org/10.1037/a0025836
18.
FolsteinJ. R.PalmeriT. J.GauthierI. (2013). Category learning increases discriminability of relevant object dimensions in visual cortex. Cerebral Cortex, 23(4), 814–823.
19.
FolsteinJ. R.PalmeriT. J.GauthierI. (2014). Perceptual advantage for category-relevant perceptual dimensions: The case of shape and motion. Frontiers in Psychology, 5, Article 1394. https://doi.org/10.3389/fpsyg.2014.01394
20.
FolsteinJ. R.PalmeriT. J.Van GulickA. E.GauthierI. (2015). Category learning stretches neural representations in visual cortex. Current Directions in Psychological Science, 24(1), 17–23.
21.
FranconeriS. L.SimonsD. J. (2003). Moving and looming stimuli capture attention. Perception & Psychophysics, 65(7), 999–1010.
22.
GauthierI.JamesT. W.CurbyK. M.TarrM. J. (2003). The influence of conceptual knowledge on visual discrimination. Cognitive Neuropsychology, 20(3), 507–523.
23.
GoldstoneR. L. (1994). Influences of categorization on perceptual discrimination. Journal of Experimental Psychology: General, 123(2), 178–200.
24.
GoldstoneR. L.LippaY.ShiffrinR. M. (2001). Altering object representations through category learning. Cognition, 78(1), 27–43.
25.
GoldstoneR. L.SteyversM. (2001). The sensitization and differentiation of dimensions during category learning. Journal of Experimental Psychology, 130(1), 116–139.
26.
HarnadS. (1987). Categorical perception: The groundwork of cognition. Cambridge University Press.
27.
HarnadS. (1993). Grounding symbols in the analog world with neural nets. Think, 2, 57–62.
28.
HockemaS. A.BlairM. R.GoldstoneR. L. (2005). Differentiation for novel dimensions. In BaraB.BarsalouL.BucciarelliM. (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 953–958). Erlbaum.
29.
HuttenlocherJ.HedgesL. V.VeveaJ. L. (2000). Why do categories affect stimulus judgment?Journal of Experimental Psychology: General, 129(2), 220–241.
30.
KangK.ShapleyR. M.SompolinskyH. (2004). Information tuning of populations of neurons in primary visual cortex. The Journal of Neuroscience, 24(15), 3726–3735.
31.
KingdomF. A. A.PrinsN. (2010). Psychophysics: A practical introduction. Academic Press.
32.
KruschkeJ. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99(1), 22–44.
33.
LakeB. M.SalakhutdinovR.TenenbaumJ. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338.
34.
LakeB. M.VallabhaG. K.McClellandJ. L. (2009). Modeling unsupervised perceptual category learning. IEEE Transactions on Autonomous Mental Development, 1(1), 35–43.
35.
LiS.OstwaldD.GieseM.KourtziZ. (2007). Flexible coding for categorical decisions in the human brain. The Journal of Neuroscience, 27(45), 12321–12330.
36.
LibermanA. M.HarrisK. S.HoffmanH. S.GriffithB. C. (1957). The discrimination of speech sounds within and across phoneme boundaries. Journal of Experimental Psychology, 54(5), 358–368.
37.
LivingstonK. R.AndrewsJ. K.HarnadS. (1998). Categorical perception effects induced by category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(3), 732–753.
38.
MaunsellJ. H.TreueS. (2006). Feature-based attention in visual cortex. Trends in Neuroscience, 29(6), 317–322.
39.
MayeJ.WerkerJ. F.GerkenL. (2002). Infant sensitivity to distributional information can affect phonetic discrimination. Cognition, 82(3), 101–111.
40.
NelsonJ. D.McKenzieC. R.CottrellG. W.SejnowskiT. J. (2010). Experience matters: Information acquisition optimizes probability gain. Psychological Science, 21(7), 960–969.
41.
NotmanL. A.SowdenP. T.ÖzgenE. (2005). The nature of learned categorical perception effects: A psychophysical approach. Cognition, 95(2), B1–B14.
42.
Op de BeeckH.WagemansJ.VogelsR. (2003). The effect of category learning on the representation of shape: Dimensions can be biased but not differentiated. Journal of Experimental Psychology: General, 132(4), 491–511.
PosnerM. I.KeeleS. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77(3), 353–363.
45.
PougetA.ZemelR. S. (2007). Population codes. In DoyaK.IshiiS.PougetA.RaoR. P. N. (Eds.), Bayesian brain: Probabilistic approaches to neural coding (pp. 115–129). MIT Press.
46.
RoschE. H. (1973). Natural categories. Cognitive Psychology, 4, 328–350.
47.
RosielleL. J.CooperE. E. (2001). Categorical perception of relative orientation in visual object recognition. Memory & Cognition, 29(1), 68–82.
48.
RotshteinP.HensonR. N.TrevesA.DriverJ.DolanR. J. (2005). Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain. Nature Neuroscience, 8(1), 107–113.
49.
SchynsP. G.GoldstoneR. L.ThibautJ.-P. (1998). The development of features in object concepts. Behavioral & Brain Sciences, 21, 1–17.
50.
SigalaN.LogothetisN. K. (2002). Visual categorization shapes feature selectivity in the primate temporal cortex. Nature, 415(6869), 318–320.
51.
SimsC. R. (2018). Efficient coding explains the universal law of generalization in human perception. Science, 360(6389), 652–656.
52.
SotoF. A.AshbyF. G. (2015). Categorization training increases the perceptual separability of novel dimensions. Cognition, 139, 105–129.
WallravenC.BülthoffH. H.WaterkampS.van DamL.GaissertN. (2014). The eyes grasp, the hands see: Metric category knowledge transfers between vision and touch. Psychonomic Bulletin & Review, 21(4), 976–985.
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